Rebooting enterprise AI with MCP and Kubernetes.Craig McLuckie on turning agents loose inside a company — without losing control of them.
McLuckie's bet: the same playbook that tamed cloud computing a decade ago — containers, orchestration, declarative control — is exactly what enterprises now need to run fleets of AI agents safely. This is a map of that argument, with every piece of jargon defined and clickable.
CM
Craig McLuckie
Co-founder & CEO, Stacklok · Co-creator of Kubernetes
McLuckie is an infrastructure engineer who has spent his career building the plumbing beneath modern software. After senior roles at Microsoft, he joined Google, where he helped build Google Compute Engine and, in 2014, co-created Kubernetes — the open-source container-orchestration system — alongside Joe Beda and Brendan Burns. He also helped found the Cloud Native Computing Foundation. In 2016 he and Beda left Google to start Heptio, a Kubernetes company that VMware acquired in 2018 for a reported $550M, after which McLuckie served as a VMware VP. He now leads Stacklok, the company behind the open-source ToolHive project, focused on running AI agents and MCP securely at enterprise scale.
Four numbers from the conversation
+60%
jump in his engineering team's weekly throughput — in a single week — as they got better at running agents concurrently.
50%
month-over-month growth in people running MCP servers on Kubernetes, per Stacklok's usage.
80–90%
reduction in input-token use when a proxy consolidates many tools into “find tool / call tool.”
5–15
agents a single developer now runs at once — a human “spinning plates.”
How to use this page. The interview moves fast and assumes a lot. Throughout the summary below, any technical term shown in blue is clickable — it jumps you to a full definition in the glossary. Each glossary card lists numbered links back to every place the term appears in the text, so you can always return to where you were. There's also a search box at the top of the glossary.
McLuckie opens with a piece of pattern-matching from his own history. The technologies that matter most, he argues, do two things simultaneously: they solve an obvious, immediate problem and they quietly point to a much larger future. He has seen it before — and he thinks MCP is doing it again.
The reference point is Docker. When McLuckie first saw it, he saw a tool that solved a plain developer headache — packaging an app with all its dependencies so it runs anywhere — but he could also “peer through it” and see Kubernetes on the other side: a whole orchestration system for running cloud-scale applications. One technology, occupying two spaces at once.
He says MCP gave him the same feeling. On one side it solves a concrete problem: letting an LLM reliably reach the systems around it. On the other, it hints at the shape of a future “n-tier AI-native application,” where the large language model becomes the presentation layer and view model, MCP formalizes the middle tier of interfaces, and today's databases stay the persistence tier.
“History doesn't repeat itself, but it often rhymes. When I saw MCP, I had that same kind of moment where you could see this technology occupying two spaces at the same time — which is very rare and very wonderful.”
Craig McLuckie
The second space: control
Beyond enabling new apps, MCP also gives organizations a place to set guardrails. Because agentic systems are stochastic — unpredictable in a way traditional software is not — they're hard to integrate “sustainably and securely.” MCP is the small protocol where that reconciliation can happen.
Move 02 · The primitive
02What MCP is, and why it exists
An LLM is brilliant at natural language and terrible at the fiddly, authenticated, not-always-trustworthy business of calling traditional APIs. MCP exists to bridge that gap — by describing the outside world in simple natural-language terms, backed by schema, that a model can actually reason about.
The recruiter example
A recruiter normally jumps between Gmail, LinkedIn, a calendar, and a candidate system all day. McLuckie's ideal: one AI assistant that can reach all of them — under control.
Describe the nouns & verbs“What is a candidate?” “What is a calendar invitation?” “Schedule an interview.” Each becomes a discrete resource or tool.
Let the model discover themThe LLM sees which tools are available and can invoke them deterministically.
Carry the identityThe agent acts as the recruiter — via OIDC through an IDP like Okta or Entra — but with controls, not “unfettered access.”
McLuckie's favorite metaphor for MCP is a “selectively permeable membrane” that an organization wraps around its existing systems: value flows through in both directions, but you can assert the controls needed to let AI “do real work in the real world.”
Critically, this isn't just for coders. McLuckie recounts overhearing a go-to-market colleague ask an engineer to wire up MCP servers for HubSpot, LinkedIn, and other business tools — far outside the Claude Code developer world most people associate with the term. MCP, he argues, is a “great democratizer”: you expose your data and set policy once, then use a broad cross-section of models — Claude, OpenAI, Gemini — without re-plumbing for each.
“It's democratizing data access while preserving control, and it's doing it in a way that's not tied to a specific provider.”
Craig McLuckie
Move 03 · The architecture
03The stack, drawn out
Asked to “level set” for listeners getting lost, McLuckie sketches how the pieces fit. His advice to enterprises: start vertically integrated (just buy Anthropic — “nobody got fired for buying blue”), then add structure as you outgrow it. What you grow into is a platform with two gateway “bookends.”
1 · RuntimeA secure place to host a server — not just npx-pulling a package from the internet onto your laptop and hoping it wasn't tampered with.
2 · RegistryA vetted catalogue: which MCP servers exist, and can I trust them?
3 · GatewayA single endpoint exposing those servers to Claude, Codex, or anything else.
4 · Control planeWhat lets you scale from 1 → 10 → 1,000 servers and map them to specific user groups.
Move 04 · The hard part
04Identity: who is the agent, and what may it do?
This is where McLuckie gets most animated — and most candid that the field hasn't fully solved it. He splits the problem cleanly in two: authentication (who are you?) and authorization (what are you allowed to do?). Today both lean on standards built for humans and deterministic software, not autonomous agents.
The reality today: MCP was grounded in a simple flow — a user on Claude carries an OIDC token, pushes it to an MCP server, and what happens on the back end is “an exercise for the reader.” The only thing that works across most organizations right now is existing OIDC tokens, so “we just have to accept that's where we are.”
The future he wants: agents with their own identity. He points to SPIFFE — the zero-trust workload-identity framework his CTO Joe Beda co-authored a decade ago — and argues AI is finally the forcing function to move past human-shaped OIDC. He describes agent identity as a “three-legged stool”:
Service-account identity — identifies the agentic endpoint itself: “you're speaking to this specific agent.” See service account.
Owner's role claims — permissions granted based on the role of whoever owns the agent. See claims.
On-behalf-of claims — inherited from the user the agent is currently acting for.
The practical fix today: token exchange
Don't pass a user's full credential downstream. Instead, run a token exchange that descopes the claims to the minimum needed. His canonical example: map an Okta token to a descoped AWS token so an agent gets the AWS MCP server in read-only mode — “I certainly don't want it deleting my RDS instance on a whim.”
A platform team that learns the four or five common exchange patterns can then offer “vanilla” servers that just snap in.
On the authorization side, McLuckie argues unsupervised agents demand far more scrutiny than deterministic callers. The pattern he favors: lean on existing auth systems to decide whether the agent (acting for the user) gets in at all, then layer agent-only rules written as policy as code — in a language like Cedar or Rego — enforced at the proxy on every tool call.
Move 05 · The case for indirection
05Why route everything through a proxy?
Daniel asks the natural skeptic's question: why not just wire MCP calls in at the application layer? McLuckie gives four reasons a proxy layer earns its keep.
Four reasons for a proxy
① Visibility & governanceA single layer gives you a trace through complex, multi-system workflows — and a place to apply policy. Pure hygiene.
② Optimization vs. tool pollutionCuts context-window bloat. Replacing 150 raw tools with “find tool / call tool” can drop input-token use 80–90%.
③ Better tool selectionBig models like Opus 4.7 cope; smaller models (Sonnet, Haiku) are “notoriously bad” at calling the right tool when 20–30 are present.
④ Tuned, per-user viewsDisambiguate names — a GIS “feature” vs a GitHub “feature” — and tailor tools to each task or cohort.
The core enemy here is tool pollution: every tool's description sits in the context window all the time. Pull in three or four MCP servers and you might carry 150 tools, “burning twenty, thirty thousand tokens every interaction” just to announce what's available. Input-token caching helps “only to a certain point.”
The proxy's answer is to be deliberately “chatty”: expose just two endpoints — find tool and call tool — so the model asks what's relevant, gets a short list, and acts. Counterintuitively, fewer tools in context yields both lower cost and better results, pushing reliable tool-calling back up to the 95–97% range that makes an autonomous agent actually useful.
“The minute you start dropping down to Sonnet or Haiku… one of the hardest problems is making sure the thing calls the damn tool when it's supposed to.”
Craig McLuckie
Move 06 · The build
06ToolHive — paving the “yellow brick road”
ToolHive is Stacklok's open-source (Apache 2.0) answer. The philosophy, in McLuckie's image: the big labs are describing the Emerald City; someone has to build the road that gets you there — to enterprise standards.
The move that ties the whole interview together: don't reinvent the wheel — repurposecloud-native technology for the MCP era. The starting point is the Linux application container, the same foundation as Kubernetes.
Package each MCP server as an OCI image so it flows through the same scan/harden/validate pipeline as any other software.
Use the container to constrain the server: restrict its filesystem and network so a “fetch anything” fetch server becomes a safe “fetch documentation” server.
Ship a vetted registry of servers Stacklok stands behind, with room for organizations to layer their own scrutiny.
Offer virtual MCP servers — composite, per-cohort tool views, including transactional “schedule interview”-style actions that pass or fail atomically.
“Anthropic, OpenAI, Google are describing the Emerald City… Someone needs to build the yellow brick road — the basic procedural things that let you actually get there.”
Craig McLuckie
Where it runs
Most users started running servers locally, but Stacklok is seeing 50% month-over-month growth in running MCP servers on Kubernetes — “millions and millions” of tool invocations on the Kubernetes side. That control plane is the platform's fourth and final piece.
Move 07 · What's next
07The horizon: self-healing systems and many agents at once
McLuckie closes on two ideas — one speculative, one already paying off in his own company.
Stochastic reconciliation. Kubernetes runs on a reconciliation loop: you declaratively describe a desired state and a controller keeps reality matching it. Those controllers are essentially deterministic today. McLuckie's question: what if a stochastic AI took over when something drifts out of conformance — reasoning about why, then driving it back? The result would be self-annealing, self-healing infrastructure that generates its own YAML and hands it to Kubernetes. He's frank that governing agent behavior at scale — beyond evals and agents-watching-agents — is an open problem he doesn't yet have an answer to.
Agentic concurrency — the payoff today
What's already transforming his team is agentic concurrency: each developer runs 5–15 agents at once, each with a different role and tightly controlled tools, in YOLO mode — a human “spinning plates.” It's expensive in tokens but “more than makes up for it.” One week, throughput jumped 60%.
His caution: the model that works for developers — the desktop as the aggregation point, as in Claude Code — won't transfer to knowledge workers. They “cannot be trusted to build and run MCP servers”; they need to be served by a platform team. The productivity gains, he believes, will reach every function — if we “bother to read” the “letters from the future” the labs keep publishing.
Glossary
Every technical term from the conversation, defined. Where McLuckie defined a term himself, that's noted; otherwise the definition is supplied for context. The numbered chips at the bottom of each card jump back to every place the term appears above.
Model Context Protocol (MCP)
An open protocol introduced by Anthropic that describes an organization's systems to a large language model in simple natural-language terms, backed by schema, so the model can discover and reliably invoke “tools.” McLuckie's metaphor: a “selectively permeable membrane” you wrap around existing systems — value flows both ways while the organization keeps control over what the AI can actually do.
Defined by McLuckie in the interview.
Kubernetes
An open-source system, originally created at Google, for orchestrating containerized applications — deploying, scaling, and healing them across many machines. It works declaratively: you describe the desired state and a control loop continuously makes reality match. McLuckie co-created it; here it's the destination for running fleets of MCP servers at scale.
Co-created by the guest; context added.
Docker / container
Technology that packages an application together with all its dependencies into a single, portable unit (a container) that runs almost anywhere. McLuckie cites first seeing Docker as the analogy for his MCP “aha” moment — a tool that solves an obvious problem while also pointing toward a larger orchestration future.
Described by McLuckie.
OCI image
A container image built to the Open Container Initiative standard — the common, vendor-neutral format enterprises already know how to scan, harden, and validate. ToolHive packages each MCP server as an OCI image so it flows through an organization's existing security pipeline rather than being a novel, unvetted artifact.
Context for McLuckie's “Linux application container” point.
Large language model (LLM) / transformer
An AI model built on the transformer architecture that interacts in natural language and excels at extracting meaning from large amounts of information. In McLuckie's framing it turns data into knowledge, knowledge into decisions, and decisions into actions — but it's awkward at calling traditional APIs directly, which is the gap MCP fills.
Described by McLuckie; standard definition added.
Agent / agentic AI
An AI system that doesn't just answer questions but takes actions in the world — calling tools, touching multiple systems, and working semi-autonomously toward a goal. McLuckie stresses that agents are stochastic, which makes them powerful but hard to integrate “sustainably and securely,” driving the need for guardrails, identity, and policy.
Context; recurring theme throughout.
Stochastic
Describes a system whose behavior is probabilistic rather than fixed — give it the same input twice and you may get different outputs. McLuckie contrasts stochastic AI agents with traditional deterministic software, noting that their unpredictability is exactly why enterprises need controls, observability, and policy around them.
Standard definition; used by McLuckie.
Deterministic
Describes a system that produces the same output every time for a given input — the property of traditional software and most existing authorization schemes. McLuckie's point is that the shift from deterministic programs to stochastic agents is the core reason old infrastructure assumptions need rethinking.
Standard definition.
Orchestration
Coordinating many moving parts — containers, services, or agents — so they work together as one larger system, handling scheduling, scaling, networking, and recovery. Kubernetes orchestrates containers; the emerging challenge McLuckie describes is orchestrating fleets of agents.
Standard definition.
Control plane
The central “brain” of a system that decides how everything should be configured and keeps it that way, as distinct from the components doing the actual work (the “data plane”). McLuckie names it the fourth piece of an MCP platform — what lets you map hundreds or thousands of servers to user groups — and Kubernetes' control plane is where MCP servers increasingly run.
Standard definition; named by McLuckie.
Runtime
The hosted environment where a piece of software actually executes. For MCP, McLuckie calls a secure runtime the first platform building block — somewhere to run a server safely, instead of pulling an unvetted package straight onto a developer's machine where it could do harm if compromised.
Named by McLuckie.
Registry
A curated catalogue of available MCP servers an organization trusts, with information on where to find them and whether they're vetted. It's the second platform piece in McLuckie's model and becomes the control point where policy is attached to each server before it's deployed.
Named by McLuckie.
Gateway
A single entry point that exposes a set of services to clients through one endpoint. McLuckie describes two as the “bookends” of an enterprise agent platform: an MCP gateway connecting real-world systems, and an LLM gateway directing traffic across different models.
Named by McLuckie.
LLM gateway
The bookend that sits in front of the models, letting an organization route requests across providers (Claude, OpenAI, Gemini), apply policy, and track usage rather than hard-wiring to one vendor. One of McLuckie's two essential “bookends.”
Named by McLuckie.
MCP gateway
The bookend that connects the AI system to the wide world of enterprise tools, hosting MCP servers behind one endpoint and applying governance to every tool call. Inside it sit the four platform pieces: runtime, registry, gateway, and control plane.
Named by McLuckie.
Virtual MCP server (VMCP)
A composite, tailored view built on top of basic MCP servers — exposing a curated set of tools to a specific group of users, sometimes bundling several systems behind one tool (e.g., a single “schedule interview” action) that runs as a transaction, passing or failing atomically so you don't get half-completed workflows.
Described by McLuckie as part of ToolHive.
Proxy layer
An intermediary that sits between the AI system and the tools it calls. Routing calls through a proxy gives you observability (a trace through complex workflows), a place to enforce policy, token optimization, and the ability to present tuned, per-cohort tool views — McLuckie's four reasons it's worth the indirection.
Explained by McLuckie.
Tool / tool calling
A discrete action an LLM can invoke through MCP — described with a name and a natural-language description so the model knows when to use it. “Tool calling” is the act of the model choosing and invoking one. McLuckie notes smaller models are “notoriously bad” at it, which is part of why curating the tool list matters.
Discussed extensively by McLuckie.
Tool pollution
The degradation that happens when too many tool descriptions crowd the model's context window. Pull in several MCP servers and you may carry 150 tools and burn 20–30k tokens every interaction just to list them; consolidating to “find tool / call tool” endpoints can cut input-token use by 80–90% and improve accuracy.
Defined by McLuckie.
Context window
The finite amount of text (measured in tokens) a model can consider at once. Every tool description loaded into it costs tokens and attention, so keeping the window uncluttered both saves money and, McLuckie notes, produces better results.
Standard definition.
Token
The basic unit of text an LLM processes and is billed on — roughly a word-piece. Token consumption is a recurring cost and performance concern throughout the conversation, from tool descriptions bloating the context window to the expense of running many agents at once.
Standard definition.
Input-token caching
An optimization that reuses previously processed input (like stable tool descriptions) so it doesn't have to be re-billed and re-processed on every call. McLuckie notes it helps with tool pollution “but only to a certain point” — which is why a proxy that trims the tool list still matters.
Mentioned by McLuckie.
Harness
The surrounding scaffolding that wraps a model to make it do useful work — managing tools, memory, sessions, and control flow. Claude Code is an example of a harness. McLuckie places harnesses “between the bookends” of an enterprise platform, alongside memory and session management.
Mentioned by McLuckie; context added.
Authentication (authn)
Verifying who someone — or something — is. McLuckie separates it cleanly from authorization: first establish identity, then decide what that identity may do. Today this leans on standards like OIDC that were built for humans, not autonomous agents.
Distinguished by McLuckie.
Authorization (authz)
Deciding what an authenticated identity is permitted to do. With unsupervised agents acting on a user's behalf, McLuckie argues you need far more scrutiny than for deterministic callers — adding “agent-only” policy, written as code, applied to every tool call.
Distinguished by McLuckie.
OIDC (OpenID Connect)
The widely deployed standard for handing an application a token that proves a user's identity. McLuckie calls it “the only thing we really have right now that works in most organizations,” so today's MCP auth is built on it even as richer agent-identity schemes emerge.
Named by McLuckie; expansion added.
Identity provider (IDP)
The service that manages identities and issues tokens — for example Okta or Microsoft Entra. Agents acting for a user inherit access through the user's IDP identity, with additional controls layered on so they don't get unfettered access.
Examples named by McLuckie.
SPIFFE
Secure Production Identity Framework For Everyone — a zero-trust identity framework for issuing verifiable identities to software workloads rather than people. Co-authored by McLuckie's CTO Joe Beda about a decade ago; McLuckie thinks AI is finally the forcing function to move beyond traditional OIDC toward something like it.
Named by McLuckie; expansion added.
Zero trust
A security model that assumes no actor is automatically trusted just for being “inside” the network — every request must prove its identity and permissions. It's the philosophy behind workload-identity systems like SPIFFE that McLuckie sees agents needing.
Standard definition.
Token exchange
Swapping one credential for another that carries only the minimum necessary permissions, instead of passing a user's full credential downstream. McLuckie's example: mapping an Okta token to a descoped AWS token so an agent gets read-only access — never the power to delete a database on a whim.
Explained by McLuckie.
Claims / on-behalf-of
The pieces of information a token asserts about an identity (who it is, what role, what permissions). McLuckie's “three-legged stool” for agent identity combines a service-account identity for the agent, role-based claims from its owner, and “on-behalf-of” claims inherited from the user it's acting for.
Framework described by McLuckie.
Service account
A non-human identity assigned to a piece of software — here, an agent — so it can be authenticated and authorized in its own right. It's one leg of McLuckie's agent-identity stool: the identity that says “you're speaking to this specific agent.”
Described by McLuckie.
Policy as code (Cedar / Rego)
Writing authorization rules in a dedicated machine-readable language — such as Cedar or Rego — so they can be applied automatically and consistently. McLuckie suggests describing “agent-only” policy this way and enforcing it at the proxy on every tool call, on top of existing auth systems.
Languages named by McLuckie.
ToolHive
Stacklok's open-source (Apache 2.0) project for running MCP servers to enterprise standards — packaging each as a hardened container, constraining its network and filesystem access, providing a vetted registry, and supporting virtual MCP servers and Kubernetes deployment. McLuckie's metaphor: the labs describe the “Emerald City”; ToolHive builds the “yellow brick road.”
Stacklok's project; described by McLuckie.
CNCF
The Cloud Native Computing Foundation — the open-source home of Kubernetes and many cloud-native projects, which McLuckie helped bootstrap. He draws on its technologies and community culture as the model for building AI-native infrastructure.
Referenced by McLuckie; expansion added.
Cloud native
An approach to building and running applications designed to exploit cloud scale and elasticity — containers, orchestration, declarative automation. McLuckie's central thesis is that much cloud-native technology can be “repurposed” to work well in the AI-native world rather than reinvented.
Standard definition; central to McLuckie's argument.
Fetch server
A common MCP server that lets an agent retrieve content from the internet. McLuckie uses it to illustrate containment: wrap it in a container and restrict which network endpoints it can reach, so a “fetch anything” server becomes a safe, scoped “fetch documentation” server.
Example used by McLuckie.
Agentic framework
A toolkit for building multi-step agent workflows — examples McLuckie names include n8n, CrewAI, and LangGraph. They sit “between the bookends” of his platform, alongside memory and session management, as part of the machinery that turns a raw model into a working agent.
Examples named by McLuckie.
Reconciliation loop / reconciler
Kubernetes' core pattern: you declare a desired state and a controller (a “reconciler”) continuously works to make reality match, correcting any drift. McLuckie speculates about “stochastic reconciliation,” where an AI reasons about why something is out of conformance and drives it back into line.
Explained by McLuckie.
Declarative infrastructure
Specifying what you want the end state to be and letting the system figure out how to get there, rather than scripting each step (“imperative”). It's the Kubernetes philosophy McLuckie expects natural-language interfaces to sit naturally on top of.
Standard definition; central to McLuckie's horizon.
YAML / manifest
The human-readable configuration format used to describe desired state to Kubernetes; a “manifest” is one such file. McLuckie imagines self-healing systems that generate their own YAML and hand it to Kubernetes to enact.
Referenced by McLuckie.
Evals
Structured evaluations used to measure and monitor model or agent behavior — sometimes human-in-the-loop. McLuckie points to them as one (still incomplete) direction for the unsolved problem of governing agent behavior at scale.
Referenced by McLuckie.
YOLO mode
Slang for running an agent with autonomy and minimal human approval on each step. McLuckie's developers run 5–15 agents concurrently in tightly tool-controlled YOLO mode — fast and productive, and only safe because the tools available to each agent are constrained.
Used by McLuckie.
Agentic concurrency
Running many agents in parallel — each with a slightly different role and configuration — while a human “spins plates” overseeing them. McLuckie credits it with his team's biggest productivity gains, including a 60% weekly throughput jump, likening developers to instrumented “performance athletes.”
Coined/used by McLuckie.
Presentation layer / view model (n-tier)
The classic layered application model — presentation, business logic, persistence. McLuckie reframes an AI-native app this way: the LLM becomes the presentation layer and view model, MCP formalizes the middle tier of interfaces, and existing databases remain the persistence tier.
Framing offered by McLuckie.
NPX
A command that downloads and runs a Node.js package on the fly. McLuckie flags the risk of casually using it for MCP: npx pulling a server straight from the internet onto your machine — “God help you if that happens to have been exploited.”
Risk noted by McLuckie.
Claude Code / Codex
Coding-agent harnesses (from Anthropic and OpenAI) that use the developer's desktop as the aggregation point for state and outbound connections. McLuckie argues that for non-developers this desktop-centric model has to move to a controlled, platform-provided entry point.
Examples cited by McLuckie.
Vertically integrated
Buying one vendor's whole stack that works well out of the box — McLuckie's “nobody got fired for buying blue” analogy, with Anthropic cast as the modern IBM. His advice: start there, then add MCP and LLM gateways as your needs outgrow the single-vendor bundle.
Magnifica Humanitas — magnificent humanity.Pope Leo XIV's first encyclical: on safeguarding the human person in the time of artificial intelligence.
Leo XIV does not treat AI as a regulatory puzzle. He treats it as a spiritual one — a question about who we still want to be when the machines can do almost anything. The choice the encyclical poses: a Tower of Babel, or a rebuilt Jerusalem.
135
years since Rerum Novarum (Leo XIII, 1891).
The anchor for modern Catholic Social Doctrine — Leo XIV deliberately picks up the name and the thread.
5
chapters, bookended by a biblical introduction and a Marian conclusion.
"It is not permissible to entrust irreversible, lethal decisions to AI systems." (Ch. 5)
What's new here: previous papal teaching on technology — Laudato Si', Fratelli Tutti, the DDF's Antiqua et Nova — circled AI obliquely. Magnifica Humanitas is the first full-length encyclical to put artificial intelligence at the center. Leo XIV's frame: AI is not a tool we use; it is a res novae — a "new thing" — that demands the same kind of moral confrontation Leo XIII gave to industrial labor in 1891. The Pope writes that "technology is never neutral, because it takes on the characteristics of those who devise, finance, regulate and use it."
Movement 01 · Introduction · "Two biblical images"
01Babel or Jerusalem
The encyclical opens by holding up two scenes from Scripture and asking which one the digital century is building. One is the Tower — a flawless system speaking one language, sacrificing the weak for the metric. The other is a city — slow, weak, particular, rebuilt by ordinary hands in solidarity. The whole encyclical hangs on this single contrast.
Genesis 11 gives us the Tower of Babel: a project of uniformity and pride, a single language imposed on everyone, an attempt to scale to the sky. Nehemiah 2–6 gives us the rebuilt Jerusalem: walls raised piece by piece by gardeners, perfumers, daughters, priests — each laboring at the section in front of their own house.
Leo XIV uses these images not as metaphors for "good tech vs. bad tech," but for two competing logics of human flourishing. The Tower says: optimize, harmonize, eliminate the weak link. The City says: a wall is finished only when each person has done their part, and the slowest builder sets the pace.
"Humanity, created by God in all its grandeur, is today facing a pivotal choice: either to construct a new Tower of Babel or to build the city in which God and humanity dwell together."
Magnifica Humanitas · ¶1
The "Babel syndrome" — Leo's diagnosis
"We must, then, avoid the 'Babel syndrome,' namely the idolatry of profit that sacrifices the weak, a uniformity that neutralizes differences, and the pretense that a single language — even a digital one — can translate everything, including the mystery of the person, into data and performance." (¶10)
Three markers Leo names explicitly:
Idolatry of profit
Uniformity over difference
Person reduced to data
Two scenes, two logics
Babel (Gen 11)
One language, top-down
Built upward, for the builders
Sacrifices the weak link
Optimization as worship
Translation of the person into "data and performance"
Jerusalem (Neh 2–6)
Many hands, shared frame
Each at the wall in front of their house
Weakness as the place of grace
Communion across difference
Slowest builder sets the pace
Leo's diptych is not a binary about technology. It is a binary about who the technology is for, and which human capacities it lets atrophy.
The pivotal claim: human limitations — vulnerability, age, illness, weakness — are not, in Leo's reading, errors waiting to be patched out. They are the places where wisdom, communion, and grace become possible. The encyclical resists the framing that treats the human body and the human condition as a beta release. "Building for the common good," Leo writes, "means accepting the limits and weakness of humanity without considering them an error to be corrected" (¶12).
Movement 02 · Chapters 1–2 · The continuum of Social Doctrine
02A living tradition meets a new res novae
Leo XIV's name is not an accident. He places Magnifica Humanitas as the latest installment in a 135-year-long Catholic conversation about how to keep the human person at the center of every great economic rupture. The chapter is, in effect, a syllabus: he reads forward from Rerum Novarum to Laudato Si', and inherits the whole library before he writes his own.
The Social Doctrine line — Leo to Leo
1891Rerum Novarum — Leo XIIIIndustrial labor, the dignity of work, the founding text.
1931Quadragesimo Anno — Pius XISubsidiarity, the social order, 40 years after.
1963Pacem in Terris — John XXIIIHuman rights and peace among nations.
1967Populorum Progressio — Paul VIIntegral human development. "More" vs. "more humanity."
1981Laborem Exercens — John Paul IIWork as a fundamental human good, not just income.
1991Centesimus Annus — John Paul II100 years after Rerum Novarum. The dignity of the worker again.
2009Caritas in Veritate — Benedict XVICharity in truth; the moral assessment of technology.
2015Laudato Si' — FrancisIntegral ecology; the technocratic paradigm.
2020Fratelli Tutti — FrancisUniversal fraternity, the politics of the common good.
2026Magnifica Humanitas — Leo XIVThe first full encyclical on AI. 135 years after Rerum Novarum.
Each pope inherits the corpus and updates it for the era's res novae. Leo XIV does not invent his categories — he names the new domain (AI, robotics, digital power) and brings the existing categories to bear on it.
Chapter 1 is, in effect, a history lesson: the Church has done this before. The "social question" of 1891 was the industrial worker; the social question of 1965 was the developing world; the social question of 2015 was the planet. In 2026, Leo XIV argues, the social question is what artificial intelligence is doing to the human person — and what we are letting it do.
Chapter 2 grounds the response in a single anchor: the human person made in the image of the Triune God (cf. Gen 1:26–27). From this dignity flow the operative principles Leo will use to evaluate every claim about AI:
Common goodThe condition for human flourishing — never sacrificed for efficiency.
Solidarity"No one is saved alone." Bonds across class and border are constitutive.
SubsidiarityDecisions belong as close as possible to those affected. Algorithms erode this.
Social justiceThe frame for evaluating who gains and who loses from any new technology.
Universal destination of goodsEarth's resources — and now its data — belong to everyone, not a few owners.
Dignity of the personThe non-negotiable floor under every other principle.
"Today we must once again reflect on the common good, the universal destination of goods, subsidiarity, solidarity and social justice."
Magnifica Humanitas · ¶46
Movement 03 · Chapter 3 · "Technology and dominance"
03Technology, dominance, and the illusion of transhumanism
Chapter 3 is the diagnostic core. Leo XIV names the philosophical undertow beneath the AI moment — transhumanism, posthumanism, and the technocratic paradigm Francis identified in Laudato Si' — and adds a sharper geopolitical observation: the people steering this transformation are no longer states. They are private, transnational corporations, several of which now wield resources larger than most national governments.
Two intellectual movements come in for direct critique. Transhumanism treats the human body as a project to be upgraded — bionic, prosthetic, cognitively enhanced, eventually merged with the machine. Posthumanism treats human nature itself as a stage to be superseded. Both share a premise Leo rejects: that finitude is failure, that being mortal is a bug.
The encyclical's counter-claim is that the human body, the human mind, and human limits are not incomplete drafts of something better. They are the very condition of love, of communion, of meaning. "We place our hope in unlimited 'upgrades,' in forms of progress that exacerbate inequalities" (¶12) — and so build a Tower instead of a city.
"In the past, it was largely up to the State to guide and direct innovation. Today, however, the main drivers of development are private, often transnational, parties that are endowed with resources and the capacity to intervene that surpass those of many Governments."
Magnifica Humanitas · ¶5
The neutrality fallacy. Leo is direct: "Technology is never neutral, because it takes on the characteristics of those who devise, finance, regulate and use it" (¶9). Restated later: "Technological innovations, including artificial intelligence, are not neutral" (¶85). The algorithm is not weather. Someone wrote it, someone bought it, someone profits from it.
Who steers the technology now?
States1945–~2000
primary
Private (transnational)~2010–2026
dominant
International bodies2010–2026
marginal
Civil society2010–2026
marginal
The shift Leo names: when states were the primary engine of innovation, democratic accountability could (in principle) reach the design choices. Today's largest decisions are made inside firms whose customers, regulators, and shareholders sit in different jurisdictions, and whose data and compute exceed many states'.
Stylized. Leo's point is qualitative — not that states were ever perfect stewards, but that the loci of decision-making have shifted, and the social doctrine's tools (subsidiarity, accountability, the common good) need to follow.
Two passages worth keeping
"Never has humanity had such power over itself." (¶4, citing Francis) — Leo XIV adopts this as the chapter's working premise: the question is no longer whether we have the power, but whether we have been formed to use it.
"Contemporary man has not been trained to use power well." (¶93, citing Romano Guardini) — The diagnosis Leo makes his own. The encyclical's later chapters are, in this sense, a formation curriculum.
Movement 04 · Chapter 4 · "Safeguarding humanity in a time of transformation"
04Truth, work, freedom — the three fronts of the digital transition
Chapter 4 is where the encyclical descends from principles to the kitchen table. Mass AI-driven restructuring. The "digital attention economy." Manipulated images and videos. The "architecture of visibility" that profiles and predicts. The formation of children whose first interlocutor is increasingly a chatbot. Leo XIV names each in his own vocabulary — not Silicon Valley's — and refuses to treat any of them as merely technical questions.
Three fronts of Chapter 4
① Truth as a common good (¶132–138)
"Disinformation did not begin with AI, yet today it finds a powerful amplifier in AI" (¶132). The ability to "manipulate content, images and videos" corrodes the conditions of democratic life: "Indifference to the truth leads, slowly but surely, to a descent into totalitarianism." (¶134)
② An educational alliance (¶139–148)
¶147: "The Church's Social Doctrine invites families, schools, Christian communities and public institutions to form a renewed educational alliance." Against grooming, cyberbullying, addiction, and the isolation of unsupervised devices (¶141).
③ The dignity of work (¶149–167)
¶151 names "a true social calamity" when innovation is "pursued solely for reducing costs and increasing profits." ¶152: "the pursuit of greater profits cannot justify choices that systematically sacrifice jobs, because the human person is an end, not a means."
④ New chains, new colonialisms (¶168–179)
¶170: "the 'digital attention economy'" and "business models that monetize attention and time." ¶178: a striking new framing — health data, genetic maps, and demographics as "the new 'rare earths' of power" — data colonialism.
Section subtitles from the encyclical itself: "Truth as a common good"; "An educational alliance for the digital age"; "The dignity of work at a time of digital transition"; "Protecting freedom against dependencies and commercialization."
On work. Leo XIV's anchor is ¶149: "work is not simply an instrument; it expresses and enhances the dignity of our lives. It is a requirement of the human condition, a normal path toward maturity, development and personal fulfilment." When firms restructure for AI productivity gains, the question is not only whether the business runs. AI, he warns at ¶150, "can paradoxically de-skill workers, subject them to automated surveillance and relegate them to rigid and repetitive tasks."
On truth. Leo cites Hannah Arendt at ¶134 — a striking footnote choice — on the totalitarian conditions in which "the distinction between fact and fiction… no longer exist." AI doesn't invent disinformation. It industrializes it.
On freedom. ¶171 introduces the encyclical's most precise diagnosis: control today "is exercised not only through explicit prohibitions, but also through the architecture of visibility" — the curatorial power to decide what we see, when, in what order, and with what next-step prompt. Leo's worry is not any single algorithm. It is the cumulative texture of a life lived inside one.
"An increase in means without a growth in humanity: 'having more' without 'being more.'"
Paul VI (Populorum Progressio), quoted at ¶94 — the chapter's central diagnostic
The educational alliance Leo names is therefore not a curriculum addition. It is a defense of the capacity to choose at all — to be formed by relationships rather than only by feeds, to learn to wait, to read slowly, to argue, to be wrong, and to be answerable to someone who knows you by name.
Movement 05 · Chapter 5 · "The culture of power and the civilization of love"
05The civilization of love — and the moral red line
Chapter 5 is where the encyclical raises its voice. Leo XIV moves from social analysis to a flat moral declaration about the militarization of artificial intelligence, and from there to a positive vision: not "ethics in AI," but a whole civilization remade around love, equity, and human communion. The chapter is also where the title's strange grandeur — magnifica humanitas, magnificent humanity — finally lands.
The chapter opens with the most urgent paragraphs in the document. Autonomous weapon systems are reframed not as a technical category but as a category mistake: a delegation of moral responsibility that human beings are not permitted to make. The Pope is explicit.
The moral red line · ¶198
"It is not permissible to entrust lethal or otherwise irreversible decisions to artificial systems. No algorithm can make war morally acceptable."
Magnifica Humanitas · ¶198 (verbatim)
Two things are worth noticing about the exact wording. First, the prohibition reaches further than autonomous weapons alone: it covers lethal or otherwise irreversible decisions, a phrase that pulls in sentencing, deportation, parole, organ allocation, and any other call from which there is no walking back. Second, the agent the encyclical names is "artificial systems," not "AI" as a branded category — the moral category is the delegation itself, not a specific technology.
Leo continues (¶198): "AI does not remove the intrinsic inhumanity of conflict; indeed it can only bring about conflict more quickly and render it more impersonal, lowering the threshold for resorting to violence, transforming defense into threat prediction and thus reducing victims to data." And the operational rule at ¶200: "the decision to use lethal force cannot be delegated to opaque or automated processes, but must remain under effective, self-aware and responsible human control."
Outdating "just war." ¶192: "without prejudice to the right to self-defense in the strictest sense, it is important to reaffirm that the 'just war' theory, which has all too often been used to justify any kind of war, is now outdated." The qualification matters — Leo is not pacifist in the absolute sense; he is closing the rhetorical door through which "just war" language has been used to legitimize wars that did not meet its own criteria. "Humanity," he writes in the same paragraph, "possesses far more effective and capable tools for promoting human life and resolving conflicts, such as dialogue, diplomacy and forgiveness."
The five paths — Leo's positive program (¶213)
① Disarm words. "Let us disarm words and we will help to disarm the world." (¶214) The first violence is rhetorical; the first peace is in how we name each other.
② Build peace through justice. Not the absence of war, but the presence of just structures. "Peace is neither a naïve hope nor merely the absence of war." (¶205)
③ Adopt the perspective of victims. The encyclical's optical correction: see history from below — the widow, the orphan, the stranger, the wounded child, the exile, the fugitive (¶244).
④ Cultivate a healthy realism. Against the "supposed political realism" of nuclear and AI-arms parity; against utopianism. A patient, costly, honest grappling with what is.
⑤ Revive dialogue, diplomacy, multilateralism. "Let us meet, let us talk, let us negotiate! War is never inevitable." (¶222) A revived UN role and binding international frameworks.
Leo's program is intentionally institutional. ¶200 demands legal frameworks, traceable decisions, and human accountability — not voluntary ethics charters. Magnitude of risk must be matched by magnitude of governance.
The closing image — the Magnificat (Conclusion, ¶243–245)
Magnifica is the Latin verb that opens Mary's song in Luke 1: magnificat anima mea Dominum — "my soul magnifies the Lord." The Conclusion's optical correction (¶244): "to look at the world from a lower position: through the eyes of those who suffer rather than the mighty; to view history through the eyes of the little ones... to interpret the events of history from the viewpoint of the widow, the orphan, the stranger, the wounded child, the exile and the fugitive."
The Incarnation is the anchor (¶231): "At the heart of everything is the mystery of the Incarnation, the Word who became flesh and dwelt among us. The flesh of the Son, poor and vulnerable, evokes the flesh of so many brothers and sisters stripped of their dignity and reduced to silence." Against that flesh, ¶233: "No computational system, however sophisticated, can create a heart that gives itself, or a conscience that discerns good from evil."
"With the same faith as Mary, let us become 'weavers of hope' in our world… so that we may bear witness to the grandeur of humanity, in which God has made his dwelling."
Magnifica Humanitas · ¶245 — the encyclical's final lines
The voices Leo XIV is in conversation with
Who else is in the room
An encyclical is not a position paper. It is a layered conversation across centuries — Scripture, the Fathers, the modern social magisterium, and a few hand-picked outsiders. Here are the figures whose voices shape Magnifica Humanitas.
Leo XIII
Pope · 1891 · the anchor
Rerum Novarum. The encyclical Leo XIV names himself after — and the 135-year anniversary that Magnifica Humanitas deliberately marks.
Pius XI
Pope · 1931 · subsidiarity
Quadragesimo Anno. The principle that decisions belong as close as possible to the people they affect — a load-bearing doctrine in Leo's chapter on AI governance.
John XXIII
Pope · 1963 · human rights
Pacem in Terris. The encyclical that grounded modern Catholic teaching in the language of universal human rights — quoted throughout.
Paul VI
Pope · 1967 · "being more, not having more"
Populorum Progressio. The diagnostic line ("an increase in means without a growth in humanity") that recurs in Magnifica Humanitas's critique of optimization.
John Paul II
Pope · 1981, 1991 · the dignity of work
Laborem Exercens and Centesimus Annus. The texts that frame Leo's Chapter 4 — work as fundamental human good, not productivity metric.
Francis
Pope · 2015, 2020, 2024 · technocratic paradigm
Laudato Si', Fratelli Tutti, Dilexit Nos. Leo's immediate predecessor — the critique of the technocratic paradigm in Laudato Si' is the most-cited modern source.
Romano Guardini
Theologian · 20th c. · a recurring witness
"Contemporary man has not been trained to use power well." Quoted at ¶93 — the line Leo makes his own as the diagnosis of the AI age.
Augustine
Doctor of the Church · 4th–5th c.
"Our heart is restless until it rests in Thee." The patristic anchor for Leo's anthropology of desire — which neither chatbots nor optimization can satisfy.
Dicastery for the Doctrine of the Faith
Vatican · 2024–2025
Dignitas Infinita and Antiqua et Nova — the recent doctrinal notes on human dignity and AI that Magnifica Humanitas elevates into an encyclical.
Mary, mother of Jesus
The Magnificat · Luke 1
The title's hidden referent. Her song — about the lowly being lifted and the proud being scattered — is Leo's closing image of what "magnificent humanity" actually means.
Nehemiah
Hebrew Bible · 5th c. BCE
The rebuilder. Each person works on the section of the wall in front of their own house. Leo's image of the alternative to Babel.
The Tower builders
Genesis 11 · the cautionary image
A project of uniformity, a single language imposed, a scale meant to reach the sky. Leo's image of where the digital century goes if the choice is made by default.
J.R.R. Tolkien
Footnote 187 · ¶213 · a surprise citation
Leo quotes The Return of the King directly: "It is not our part to master all the tides of the world, but to do what is in us for the succour of those years wherein we are set." A rare literary cameo in a papal encyclical — and the keynote for the "five paths."
Hannah Arendt
¶134 · cited on totalitarianism
Quoted on the conditions in which "the distinction between fact and fiction… and the distinction between true and false… no longer exist." Leo's anchor for treating disinformation as a regime-of-truth problem, not a content-moderation problem.
The tensions Leo XIV leaves for the reader
Two codas
An encyclical asks more of its reader than agreement. It asks for a posture. Magnifica Humanitas closes by pointing at two questions it does not fully resolve — partly because they cannot be resolved in a document, only in a life.
Coda · The Babel question
What if "optimize the human" is already the language we speak?
Leo's diagnosis of the "Babel syndrome" lands hard precisely because it is not exotic. Performance reviews, fertility tracking, sleep scores, productivity dashboards, screen-time leaderboards, engagement metrics — the translation of "the mystery of the person" into "data and performance" is, by 2026, not a fringe philosophy. It is the operating system.
Refusing it does not require abandoning measurement. It requires noticing where measurement begins to replace the thing being measured — where the metric becomes the friend, the score becomes the worker, the chatbot becomes the priest.
"The pretense that a single language — even a digital one — can translate everything, including the mystery of the person, into data and performance."
The encyclical's challenge: not to opt out of the Tower in some symbolic way, but to notice each day which language we are using, and which one we are refusing to.
Coda · The Jerusalem question
If we are to build the city, where is the section of wall in front of your house?
Nehemiah's image works precisely because nobody finishes the wall alone. The gardener has a section, the perfumer has a section, the priest has a section. Each is small. Each is local. Each is named.
Leo XIV lists explicitly the trades he is calling to the wall: "scientists and researchers, entrepreneurs and workers, educators and legislators, civil society, popular movements and faith communities" (¶13). The encyclical refuses to outsource the work to a few experts in a regulatory body. It also refuses to flatten it into a personal lifestyle choice.
"All are given their own section of the wall."
The question Leo leaves: which section is in front of your house — your code, your classroom, your hiring decision, your platform, your prayer, your children's screens — and what would it look like to actually build it, with others, this year?
Plain English × Every × NYT × Rethink · May 2026 · Four arguments, one question
More work, or less? Four arguments on AI and the future of work.
An economist who runs experiments (Alex Imas, UChicago Booth), a founder who has automated his own company as far as it will go (Dan Shipper, Every), a Wall Street chief executive (David Solomon, Goldman Sachs), and the Stanford economist who measures the data (Erik Brynjolfsson) all circle the same question: what happens to human work as the machines get good? Three of them arrive, from very different directions, at more expert human work, not less. Brynjolfsson shares much of that optimism — and then insists that the gains and the disruption are two separate questions with two separate answers. Here is where the four converge, and where they pull apart.
70%
of executives say AI will add jobs or have no impact on hiring.
Survey of ~6,000 CEOs, CFOs & senior finance managers, early 2026. Cited by Imas.
+145%
U.S. civilian employment growth since 1962 — vs. +128% working-age population.
Solomon: jobs have outpaced people for six decades, through every previous wave of automation.
44,469
pull requests on the OpenClaw repo — vs. Kubernetes' 5,200 in all of 2022.
Shipper: cheap competence isn't replacing work, it's flooding the system with more of it — most of which still needs an expert to review.
One thing to notice: almost every company announcing big AI-driven layoffs (Coinbase, Block, Salesforce) has lost at least one-third of its equity value in the last five years. Weak stocks announce layoffs in slumps. AI is the headline; corporate underperformance is the cause. Meanwhile, at Every — a 30-person company that has automated as far and as fast as possible — Shipper writes that "we haven't fired all of our employees in favor of agents," and there's "more human work to do than ever." And from inside Wall Street, Solomon notes that even with Goldman's own economists forecasting that 25% of work hours could be automated within a decade, the firm has "employed more people than ever in recent years" — automating tasks freed analysts to do more, not fewer. And yet: Brynjolfsson would dispute none of this, and his own payroll data already shows employment for the youngest, most AI-exposed workers down roughly 12%. Both things are true at once. Holding them together is the whole problem.
The assumption that the set of jobs we can see today is the set of jobs that will ever exist. If you believe that, automation looks like a one-way march to zero employment. Two centuries of evidence — and one Every-sized natural experiment happening right now — say otherwise.
In 1820, David Ricardo wrote On Machinery and changed his mind about the Industrial Revolution: technology, he warned, could put people permanently out of work. Two hundred years later, U.S. prime-age employment is at one of its highest readings ever. The jobs of 1820 are mostly gone. The number of jobs is not.
Why? As one sector becomes cheap to produce — agriculture, manufacturing — its share of GDP shrinks. People get richer. Dollars and time flow toward desires that couldn't be satisfied before: pet care, private tutoring, therapists, personal trainers, Michelin restaurants. Most of these categories did not exist as jobs in 1940.
"The types of jobs we see today are not the sort of jobs we will see tomorrow. Most of the jobs we have today didn't exist in 1940."
Alex Imas
Shipper · The view from inside Every
"At Every, we've automated everything we can. We use Codex and Claude Code across coding, writing, design, customer service, and more… And yet it seems like, for us, there's more human work to do than ever. We are a team of almost 30 people, and we haven't fired all of our employees in favor of agents."
"In short, the future looks weird, but also familiar."
Solomon · The view from Wall Street
"The United States has a long track record of creating new jobs in response to disruption, from the electrification of the 1900s to the digital revolution of the 1990s; I don't see any reason to think this dynamic will stop now."
The receipts he points at: since 1962, U.S. civilian employment rose ~145% while the working-age population rose ~128%. Manufacturing employment fell from 15.5M → 12.5M over the same span, but health care — barely a sector in 1962 — now employs more than 18 million Americans. The mix shifts. The total grows.
If Ricardo were right — vs. what actually happened
Stylized. Every wave of automation has been followed by new jobs — not by mass, permanent unemployment. Agriculture was ~80% of employment in 1820; today it's a few percent — and we eat more, not less.
Where they converge
Imas reads the macro record. Shipper reads his own 30-person org. Solomon reads Goldman's headcount over six decades. All three land on the same anomaly: the doom forecast keeps being wrong, every time, at every scale.
Imas (macro)
"If you told Ricardo in 1820 that almost every job he knows would be automated by 2026, he'd predict 90% of 40-year-olds would be out of work." He'd be wrong by an order of magnitude.
Shipper (firsthand)
A company that uses Codex and Claude Code across every function still hires writers, editors, engineers, and customer service. The work changed shape. The headcount didn't fall.
Solomon (six decades)
+145% employment vs. +128% population since 1962 — across offshoring, two recessions, the digital revolution, and a pandemic. Manufacturing → health care. Jobs reshuffled; total grew.
Concept 02 · Jevons Paradox
02Cheaper makes bigger
Make a thing more efficient, and demand can rise, not fall — because the cheaper version unlocks uses that weren't viable before. Named for William Stanley Jevons, who in 1865 noticed it with coal. In 2026 you can watch it happen inside any GitHub repository.
Each engine burned less coal — yet total coal use tripled
Two facts, one paradox. Cheaper, more efficient engines used about half the coal apiece — but they became economical to put into mills, mines, factories, ships, and railways that couldn't justify them before. So many more engines ran that total coal demand roughly tripled. (Each panel has its own scale.)
This isn't ordinary supply-and-demand. It's the paradox that efficiency itself expands the market. Imas points at software as today's coal:
If one AI-augmented engineer can do the work of ten, naively you'd fire nine. But cheaper software means new buyers — small firms, niche use cases, individuals — who could never afford it before. If demand is elastic enough, the firm hires more, not fewer.
"The data on software-engineering hiring is not going down since these agentic coding tools were introduced. If anything, it's recovering and going up — what people on the internet call a narrative violation."
Alex Imas
The spreadsheet is the perfect prior analog. In the 1970s, Gunnar Myrdal wrote to the White House warning that digitized spreadsheets would wipe out accountants. Instead, the number of accountants quadrupled.
Shipper · Cheap competence gets rapidly adopted
"When the cost goes down for something previously rare, supply suddenly goes way up. At Every, we see this all the time. Operations and customer service people are writing code and issuing pull requests. Marketers are making YouTube thumbnails. Engineers and product people are writing drafts of articles, guides, and landing pages when they never would have before."
"Take OpenClaw, the open-source AI-agent project: as of May 16, 2026, its repository had already seen 44,469 pull requests, including 12,430 since April 1 and 3,990 since May 1. For comparison, Kubernetes — one of the most popular open-source projects in the world — got 5,200 pull requests in all of 2022."
Solomon · Two clean Jevons receipts from finance
The microfiche-to-Excel story. "As a first-year banking analyst, making a graph of a stock's performance took me six hours of looking up prices on WSJ microfiche." Today that graph takes seconds. Goldman didn't fire the analysts — "we've employed more people than ever in recent years." The tool raised the bar on what each analyst produces; the headcount went up, not down.
The ATM story. ATMs were the canonical "this technology eliminates jobs" prediction of the 1970s and 80s. Forty years on, with ATMs, digital banking, and decades of consolidation, U.S. commercial banking employment is roughly the same as the mid-1990s. Branches got cheaper, so banks opened more of them. The teller's role evolved; it didn't vanish.
The data-center build-out. The same wave automating away white-collar tasks created more than 200,000 construction jobs since 2022 to build the A.I. infrastructure itself. Goldman economists' figure. The demand the technology unlocks is showing up in adjacent sectors.
Brynjolfsson · The same engine, with a switch on it: elastic vs. inelastic demand
Jevons isn't automatic — it depends on the demand curve. When AI makes a worker 10× more productive, you hire 10× the workers only if demand for the output is elastic. Brynjolfsson's two reference cases: agriculture is inelastic — farms got vastly more productive and employment fell from ~90% of Americans to ~2%, because we don't eat ten times more food. Commercial aviation is elastic — jets made each pilot far more productive, flying got cheap, demand exploded, and pilot employment grew.
The same split is now running through software itself: entry-level coding, mostly codified and repeatable, is contracting; senior engineering, where judgment creates new value, sees more demand and higher pay. Which side a job lands on, he argues, gets sorted by decentralized entrepreneurship, not by forecasting — "this is the job of entrepreneurs, and this is why you have a decentralized economy."
His advice to workers is unsentimental: "Use these tools, become as efficient as you can. It may increase demand, and it may increase wages — or it may not. But if you don't, somebody else is going to do it and outcompete you."
Where they converge
Same mechanism, three vantage points — plus a caveat. Imas observes Jevons in BLS hiring data. Shipper observes it inside Slack and Git. Solomon points at his own résumé and four decades of commercial banking. Brynjolfsson adds the switch: the multiplier only runs forward when demand is elastic — and for some jobs it isn't.
Imas (theory)
Cheaper software → more buyers → elastic demand for software → demand for engineers can rise even as per-engineer output rises. The spreadsheet didn't kill the accountant.
Shipper (Every receipts)
When code-writing becomes nearly free, ops people, marketers, and PMs all start writing code. Volume explodes. Engineers get pulled into reviewing, gating, and frame-setting for everyone else's output.
Solomon (Goldman receipts)
The microfiche-to-Excel transition didn't shrink the analyst pool — it expanded it. ATMs didn't kill the bank branch. The cheaper the prior task, the more new work gets demanded above it.
Concept 03 · O-Ring Jobs + Human Sandwich
03Tasks unbundle. Jobs don't.
Most jobs are bundles of interdependent tasks. Automate four of five and the fifth still needs a human — or the whole thing collapses, like the cold-stiffened O-ring that destroyed the Challenger. Shipper has a complementary frame: every agent at Every sits sandwiched between humans on both ends. Pull either end away and the agent stops being useful.
The economist's old model treats tasks as independent: automate one, the others sit fine. The O-ring model (Goldfarb & Gans) says no — over-salt a Michelin meal and the rest of the perfection doesn't save it. Most knowledge work looks like this.
A radiologist's job is a strong bundle: reading the scan, explaining it to a patient, coordinating the care team, querying the EHR, escalating to a surgeon. The tasks need each other. You can't peel one out without breaking the rest.
A long-haul trucker is a weak bundle (Garicano): driving, safety checks, paperwork, dock handoff. Each piece can be automated separately. Different exposure.
"You can automate four out of five tasks. But if the human's not in the loop on the fifth, the whole bundle blows up — the O-ring cracks, the rocket explodes."
Paraphrasing the discussion
A subtler implication: in strong bundles, the person left behind may earn more, not less — they now have the time and the supporting AI to do the irreplaceable part really well. Wages can rise even as headcount falls. Whether headcount actually falls depends on whether demand for the bundled output is elastic (back to Jevons).
Strong bundle vs. weak bundle
RadiologistStrong bundle
Read scan
Talk to patient
Care team
EHR & nuance
Escalate
Drop the red task → other tasks lose context. Whole bundle fails.
Long-haul truckerWeak bundle
Drive
Safety
Paperwork
Dock handoff
Each piece automates independently. Easier to disassemble.
"O-ring" comes from the Challenger disaster: a single small failure broke a whole, otherwise-perfect system.
Shipper · The human sandwich
"Across both forms — coworker agents and embedded agents — the pattern is the same. Employee agents take over more of the stable, repeatable, well-framed layer of work. But there is a lot of work that still requires a human being in the loop." Kieran Klaassen, GM at Every's Cora, calls it the human sandwich:
Human
Sets the frame: what are we trying to do? What counts as good?
AI
Collapses the task: drafts, searches, codes, summarizes, compares.
Human
Judges and extends: is this good? Where does it belong? What should happen next?
"The further away an agent gets from a human who is in charge of making sure it works well, the less well it works. In our initial internal roll-out of agents as employees, we gave every employee an agent, but we soon moved back to agents that work for a particular team or the whole company. Agents need a lot of maintenance, and personal agents quickly get stale when the employees they were working with gave up on them."
Solomon · The Goldman reshuffle
"While A.I. eliminates jobs in some sectors, it may lead to job growth in others. Goldman Sachs may need fewer people in regulatory reporting or client onboarding, freeing us up to hire more bankers, traders and asset managers."
That's the bundle in action. Banking is a strong bundle: client trust, judgment under uncertainty, structuring a deal, sitting across from a CEO. Regulatory reporting and onboarding are the weak-bundle tasks at the edges. Peel those off and the high-judgment core gets more time, more capacity, and more hires.
"'Can be replaced' does not mean 'will be replaced.'" Solomon's example: despite ATMs, digital banking, and decades of consolidation, commercial banking employment is roughly flat to the mid-1990s. The relational, judgment-laden parts of the job were always the real value — automation reorganized around them.
Brynjolfsson · The Turing Trap — and the centaur fix
Why does so much AI get built to replace the human in the bundle rather than complement them? Brynjolfsson blames a 75-year-old default. By defining the goal as imitating humans, the Turing Test quietly pointed the whole field at substitution. "I think Alan Turing… sent us in the wrong direction when he said the test of AI is how close it can mimic humans. Instead of trying to have machines do things that are hard for machines and easy for humans, we should work on having machines do things that are easy for machines and hard for humans."
His alternative is the centaur benchmark: don't measure the machine alone or the human alone — measure how well they perform together, and optimize for that. It is the same instinct as Klaassen's human sandwich, restated as a design target. His call-center study is the positive-sum proof: customers got better answers, and employees were measurably happier with less turnover. "This can be a win for all three groups if you do it right."
But complementarity has to be designed in. A black-box AI that reads a scan without explaining itself can fail in deployment because it was never built to work with the radiologist. The centaur question — how well does the human-AI team detect the cancer? — forces interpretability, so the human can still catch the eyelash that fell on the slide.
Where they converge
Imas's "interdependent tasks," Shipper's "human sandwich," Solomon's "fewer here, more there," and Brynjolfsson's "centaur" describe the same dynamic from four angles. The unautomated piece isn't a residue — it's the structural reason the rest of the bundle pays off, it's where the human headcount migrates to, and (Brynjolfsson's addition) keeping it human is a design choice, not a given.
Imas (theory)
In a strong bundle, the un-automated task is multiplicative, not additive. Removing the human doesn't leave 4/5 of the value — it leaves zero. Wages can rise.
Shipper (practice)
At Every, "Claudie" writes sales proposals, "Andy" drafts editorial digests, "Viktor" runs research memos. Each is wrapped by humans on both sides — framers in front, judges behind.
Solomon (firm-level)
Fewer humans on regulatory reporting; more on the client-facing, judgment-heavy core. The headcount migrates within the firm — toward the part of the bundle A.I. can't credibly own.
Concept 04 · Human Privilege + Demand for Difference
04The new scarcity is difference
In a world flooded with cheap AI output, the new scarce good is "made by a human" and "not like everything else." Imas's experiments show the WTP for human-made rises with scarcity. Shipper extends the same logic into the workplace: AI commoditizes yesterday's competence, and the work that doesn't look like everything else becomes the rare, high-status thing.
Willingness to pay — Imas & Madarász experiments
Same t-shirtOpen to everyone
baseline
Same t-shirtRandomly scarce
~2×
AI-made productLimited edition (1 of 1)
low
Human-madeLimited edition (1 of 1)
premium
Two experiments by Imas and collaborators (Madarász; Mendell). Scarcity alone roughly doubles WTP. Human authorship adds a separate premium that AI doesn't get — even when both items are "1 of 1."
Desire has two parts: how good the thing is (the hedonic), and how few other people can have it (the scarcity). Imas's t-shirt experiment isolated the second piece — and willingness to pay nearly doubled when some buyers were randomly excluded, without changing the product at all.
A follow-up study with Graylyn Mendell ran the same idea on AI-vs-human-made products. When a human-made item is rare, it commands a big premium. When an AI-made item is rare, it doesn't.
That's the engine behind what Imas calls the relational sector. Not just performers — also teachers, doctors, financial advisors, trainers. Their jobs include a piece the customer specifically wants delivered by a human. As AI handles the surrounding tasks, that piece becomes the whole point — and gets more, not less, valuable.
"Starbucks automated everything they could. Then the CEO reversed it — bring back the baristas, the names on cups, the handcrafted feel. The thing customers were paying for wasn't only the coffee."
The Starbucks reversal — Plain English
Shipper · Abundance creates sameness — and sameness creates demand for difference
"Because everyone has access to the same models, and the models are all based on yesterday's competence, by default the models end up creating work that ranges from 'a decent start' to 'it's just plain slop.' Slop is not any one particular mistake. Slop is visible sameness, repeated ad nauseam."
"We demand not just any React app or research report — but one that feels exactly right for the person, company, and situation. We demand one that feels alive and specific, not cheap and generic. We want something that has status. When work is abundant and looks alike everywhere, the work that doesn't fit the pattern becomes the rare, valuable, and high-status thing."
"Demand for difference is new demand for experts. The current generation of models only knows about work that has been done. Humans know about what needs to be done, right now, at this moment."
Solomon · What stays stubbornly human
Solomon's list of jobs A.I. can't easily touch is the labor-market mirror of Imas's "relational sector": food preparation, construction, hands-on services. The visible, physical, person-to-person work. "It's hard to see how A.I. is going to do those jobs."
And his cultural exhibits: "TV didn't kill live entertainment. The internet didn't end real estate agents or fitness instructors." The pattern repeats whenever a new technology arrives — the things people specifically want delivered by a person survive, and often appreciate. The premium grows on the human-made side; the automate-able side gets cheaper.
Where they converge
Imas's experimental result, Shipper's "slop" diagnosis, and Solomon's list of resilient hands-on sectors describe the same market. In all three, abundance of the cheap version inflates the value of the rare version — and the rare version is, almost by definition, human.
Imas (consumer experiment)
Strip away every difference between two products except one — that this one was made by a human — and people will pay a real premium. Scarcity doubles the effect.
Shipper (B2B / labor market)
When everyone can produce "decent," only "alive and specific" is valuable — and only humans, applying judgment to a live situation, can produce it reliably.
Solomon (whole-economy)
The relational and hands-on sectors — fitness, real estate, live entertainment, food, services — kept defying displacement forecasts through every prior wave. He expects the pattern to extend.
Imas's prediction: the job titles of 2040 will look familiar — teacher, doctor, planner, engineer. But the day-to-day will invert: AI does most of the bundle, the human delivers the irreplaceable, relational or judgmental piece. And entirely new categories will appear that we can't picture yet — just as most "strongly relational" jobs in 2026 didn't exist in 1940.
Concept 05 · Shipper's distinctive contribution
05The frame is not the framer
"But what about the benchmarks?" Every doomer's last refuge: AI is climbing exponential curves on Humanity's Last Exam, GDPval, METR. Doesn't that change the story? Shipper's answer — built from inside the lab — is no. Benchmarks measure work inside a frame. Humans choose the frames. That gap is structural, and it's the deepest reason the doom story keeps being wrong.
The benchmarks are real. Humanity's Last Exam: low single digits a year ago, ~44% today. GDPval: from ~lows to ~85% in twelve months. METR's early Claude Mythos result: an 80% success rate on tasks that would take a human four hours.
Shipper calls the temptation to extrapolate from these curves "chart psychosis." The trap: a benchmark is not the territory. To measure anything, you have to freeze a problem into a static — and therefore measurable — frame. Once a frame gets saturated, it is trivial to zero the model out again by changing the frame. Progress continues inside the new frame, of course — but the same process repeats.
"The frame is not the framer. AI turns yesterday's framed competence into something cheap; people use that cheap competence in more places; the results become abundant; experts move to the edge to decide what matters now; their judgment creates the next frame; and then the model climbs that, too."
Dan Shipper
The clean illustration is Every's in-house Senior Engineer benchmark, which asks a coding agent to do a first-principles rewrite of a real, vibe-coded codebase. GPT-5.5's best run scored 62/100. Human senior engineers score in the high 80s or low 90s. Next year a model probably catches up. Then the team picks a harder rewrite, asks the model to decide whether a rewrite is needed in the first place, and the gap reopens.
Senior Engineer benchmark, May 2026
Opus 4.7previous frontier
~32
GPT-5.5current best
62
Senior eng.human, AI-assisted
88
The benchmark cycle
Model saturates the frame
→
Cheap competence floods the market
Experts move to new edge
←
Their judgment defines the next frame
Each new model saturates the current benchmark. The frame shifts. The cycle repeats. The framer — the human deciding what counts as progress — stays one rung ahead.
The smuggled intelligence. When OpenAI announced GDPval results — "AI as good as experts at half of tasks" — the headlines missed something. Every GDPval task ships with a thousand-word, expert-written prompt that pre-frames the problem. Shipper's line: "There is an enormous amount of human intelligence going into framing this problem in a way that a model can complete." Strip the framing, ask the model "solve all the errors that keep popping up," and the score drops to near zero.
Shipper · But what about AGI?
"Even this strong version of AGI does not dissolve the frame problem. This AGI can choose and re-choose frames, but only in pursuit of some goal it has been given, some reward it is optimizing, or some signal someone has decided should count as progress. In any hypothetical AGI built by any of the major labs, there is still going to be a framer — a human — directing the model to achieve a goal."
"Spend 10 minutes with a toddler and you'll see it. A toddler is worse than a language model at almost every task we care about. But the toddler has ends. He invents games constantly. He turns the world into experiments. He is not waiting for a prompt. Current agents have autonomy. They do not have agency."
Solomon · The Keynes humility check
Solomon's closing rhetorical move plants the same flag from a different direction. In 1930, John Maynard Keynes predicted that by 2030 we'd be working 15-hour weeks thanks to technology. The reality, almost a century later, is closer to 40. The smartest economist of the 20th century missed the shape of the future by an order of magnitude — because he extrapolated inside one frame (output per hour) and missed everything that happened outside it (entirely new categories of work).
"Stark forecasts by even the most brilliant minds often miss their mark." Today's "AI will eliminate half of all white-collar work by 2030" predictions are operating exactly the same way Keynes was — projecting today's frame forward, missing the framers who will keep moving it.
Where this extends Imas — and where Solomon agrees by analogy
Imas's four ideas explain why the labor market keeps absorbing automation. Shipper's fifth explains why it will keep doing so — structurally, even after AGI. Solomon doesn't use the same vocabulary, but his Keynes story is the same insight from a CEO chair: every prior forecast that extrapolated inside one frame got the shape of the future wrong.
Imas (covered)
What happens to jobs after a wave of automation: Jevons demand, O-ring bundles, scarcity premium, new categories. The empirical regularity.
Shipper (adds)Why the regularity holds. Frames are nameable, optimizable, climbable. Framers are not. The gap regenerates one rung up every time a model catches up.
Solomon (echoes)
Keynes was a framer-blind forecaster. So are today's doomers. "Stark forecasts by even the most brilliant minds often miss their mark" — the rhetorical version of Shipper's structural argument.
Concept 06 · Brynjolfsson · The measured economy
06What the numbers actually show
The other three argue mostly from history, theory, and firsthand experience. Brynjolfsson argues from the data — and the data cuts both ways. Most of AI's value is real but invisible to the statistics; the productivity payoff is real but lagged; and the first labor damage is real but narrowly concentrated. Optimism and alarm live in the same dataset.
The canaries: employment change by AI-exposure, ages 22–26
Brynjolfsson, Chen & Chandra, "Canaries in the Coal Mine," using ADP payroll data (tens of millions of records). The damage is concentrated in the youngest, most-exposed workers — exactly where theory says it should land first. This is the same study Solomon cites at ~16% as his honest concession to the bears.
The upside the statistics can't see. The standard knock on AI is that it isn't showing up in the productivity numbers. Brynjolfsson's reply: a large share of its value was never going to. GDP counts transactions, but much of what AI delivers is free. His team's willingness-to-accept survey asks what you'd need to be paid to give up ChatGPT for a month — about $98 in 2024, $125 in 2025 — value that never crosses a cash register. His proposed fix, GDP-B ("GDP, benefits"), tries to count it: "the average American spends almost half their waking hours looking at screens. They're voting with their time that this free digital stuff is very valuable. It's just not priced."
The J-curve. The second reason the stats lag: a general-purpose technology demands a costly reorganization before it pays off — "to get the full value, you have to rethink how companies organize." That intangible investment can run up to 10× the direct spend on the technology and mostly isn't measured, so output looks flat while the groundwork is laid. His read is that the curve is finally turning: U.S. productivity grew 2.2% in 2025, nearly double the prior decade, with call-center, consulting, and software studies showing double-digit gains.
"I'm pretty optimistic our productivity is going to get better. I also think there could easily be a lot of chaos — and there's no guarantee we're going to have widely shared prosperity."
Erik Brynjolfsson · Stanford
Brynjolfsson · Two questions, two answers
The reason the data can look contradictory is that it answers two different questions. The aggregate question — will there be more productivity, more value, probably more jobs eventually? — he answers with real confidence. The distributional question — who captures the gains, and how fast can the displaced adjust? — he refuses to wave away. Most optimists and most doomers collapse the two into one verdict. Brynjolfsson keeps them apart, and it is the single move that most separates him from the other three voices here: they are largely arguing the first question; his deepest worry lives in the second.
The intellectual lineage
Who the four voices draw on
Imas works from a 200-year economic tradition. Shipper works from the last three years inside Every. Solomon argues from Goldman's headcount across six decades. Brynjolfsson brings the payroll data and a measurement lab. Here's the cast all four draw on.
David Ricardo
Political economist · 1820 · cited by Imas
Chapter "On Machinery" — first to argue technology could permanently displace labor. The original doom thesis.
William Stanley Jevons
Economist · 1865 · cited by Imas
Observed that more efficient steam engines led to more coal use, not less. The paradox is named for him.
David Autor
MIT · cited by Imas
Task-based model of jobs that frames the whole modern AI-and-labor debate.
Goldfarb & Gans
Toronto · cited by Imas
O-ring model of jobs — interdependent tasks mean partial automation can preserve (or raise) wages for the human left in the loop.
Luis Garicano
LSE · cited by Imas
"Weak bundles and strong bundles" — why radiologists are harder to automate than truck drivers despite both being "task workers."
Imas & Madarász
UChicago / LSE
Mathematical model of desire as hedonic + scarcity. T-shirt experiment showed scarcity alone nearly doubles WTP.
Kieran Klaassen
Every / Cora · cited by Shipper
Originated the "human sandwich" frame — humans are the bread on either end of the AI's work. The vocabulary used across Every.
Dario Amodei
Anthropic · cited by Shipper
Public warning that AI could wipe out up to half of all entry-level white-collar jobs. Shipper takes the warning seriously and disagrees with the conclusion.
METR
AI safety nonprofit · 2026
Early Claude Mythos result: an 80% success rate on tasks a human expert would take ~4 hours to finish. A benchmark used to argue both sides.
OpenAI GDPval
Benchmark · 2025–2026
Evaluates frontier models on expert-level occupational tasks. Headlines say "AI matches experts on half of tasks." Shipper says: look at the prompts.
Philip Trammell
Substack · 2025 · cited by Imas
Strongest version of the doom case: hyperintelligent AI creates infinite new varieties that absorb all spending. Labor share → 0.
David M. Solomon
CEO, Goldman Sachs · NYT Opinion, May 22, 2026
"I'm the C.E.O. of Goldman Sachs. The A.I. Job Apocalypse Is Overblown." Plants the optimist flag from inside Wall Street, but concedes a "magnitude we haven't seen before" of labor-productivity separation.
Goldman Sachs economists
In-house research · cited by Solomon
25% of current work hours could be automated by A.I. over the next decade. Same team: 200,000+ construction jobs since 2022 from A.I. data-center demand.
Brynjolfsson, Chen & Chandra
"Canaries in the Coal Mine" · 2025–2026
ADP payroll study: employment for the youngest, most A.I.-exposed workers is down ~11–12% (≈16% for entry-level in the hardest-hit roles). The empirical signal both Brynjolfsson and Solomon point to.
John Maynard Keynes
1930 · cited by Solomon
Predicted that by 2030 people would work 15 hours a week. Reality is ~40. Solomon's humility check: even brilliant minds extrapolate inside the wrong frame.
Erik Brynjolfsson
Stanford Digital Economy Lab · 2025–2026
Consumer surplus & GDP-B, the productivity J-curve, the Turing Trap and the centaur benchmark, and the "Canaries" data. The balanced fourth voice: confident on aggregate gains, unsettled on distribution.
E.O. Wilson
Biologist · ~1980 · cited by Brynjolfsson
"Prehistoric brains, medieval institutions, godlike technology." Brynjolfsson's framing of the real risk — capabilities racing ahead of the institutions meant to absorb them.
The meta-level read · all four sources
What the four agree on — and don't
Step back from the individual ideas and the four sources resolve into a single shape: a broad agreement about the aggregate, and four different anxieties about the texture. They converge on what automation does to the quantity of work. They part ways on whether the resulting world is one we can actually live in — and Brynjolfsson is the one who insists those are two different questions that deserve two different answers.
Where the four agree
Disruption isn't apocalypse
Two centuries of doom forecasts — Ricardo, Keynes, the spreadsheet, the ATM — were wrong every time. The lump-of-labor intuition keeps failing; employment keeps reconstituting. All four take that record seriously.
Value migrates to the human edge
Call it the strong bundle, the human sandwich, the relational sector, or the centaur: as AI absorbs the codifiable middle, worth concentrates in judgment, framing, and the delivered-by-a-human piece.
Complement beats replace
The systems that work keep a human in the loop by design. Shipper's sandwich and Brynjolfsson's centaur benchmark are the same prescription; Solomon's reshuffle and Imas's bundles are the same fact seen from outside.
Where they part ways — four anxieties beneath one optimism
Brynjolfsson's deepest worry isn't too few jobs — it's that the gap between what AI can already do and what our institutions can absorb produces severe disruption before we adapt. Even his optimistic story still requires a transition the labor market isn't built to run at speed: labor mobility has fallen over 20–30 years, universities aren't teaching AI-native work, and the BLS is being cut "at the exact wrong time." The capital/labor ratio, roughly constant for 200 years, has begun to drop — and a smaller wage share means a smaller share of people with economic, and ultimately political, power.
"We're spending hundreds of billions — maybe trillions — pushing the capabilities. But we're not investing comparably in understanding the transition. There are lots of examples where we got it terribly wrong and a lot of people were hurt."
His prescription isn't to slow the technology but to invest in absorbing it: better measurement (GDP-B, the Stanford indicators), more dynamic labor markets, and centaur benchmarks that steer AI toward complementing people. The aggregate gains, he argues, are earned. The widely-shared prosperity is not — it has to be chosen.
Solomon's tension · The reskilling pace
Optimism is conditional, not complacent.
Solomon's case is a rebuttal built on one asymmetry: every previous automation wave triggered an apocalypse forecast that turned out wrong — Keynes among the forecasters — so the burden of proof sits with whoever claims this time is different. But he names the real risk openly: this transition could move faster than the reskilling system can absorb, and the U.S. has let its training infrastructure lapse before.
"This must be a joint effort between the public and private sectors… investing more in vocational schools and community colleges; the private sector should help workers upgrade their skills."
The posture is humility, not panic: resilience as the prior, apocalypse as the burden of proof — and a reskilling bill that comes due whether or not the optimists are right. It is the policy-flavored echo of Brynjolfsson's institutional gap.
Imas's tension · The loneliness problem
If the relational sector grows, who's left to do the relating?
Imas's optimism rests on a deep human desire for connection — the barista who knows your name, the teacher who reads the room, the doctor who sits with you. As AI scales, the dollar value of delivered-by-human work should rise.
And yet: time spent socializing is falling, indoor hours are rising, and a 2026 paper in Psychological Science found that loneliness increased the more people leaned on chatbot companions.
"The brain in our heads is a stone age brain. It wants to connect to other people. You can trick yourself for a couple days. Then you realize you're talking to a chatbot."
A humming relational economy that monetizes our base desire for connection — even as the unpriced, ordinary kind keeps disappearing. The economy may be fine. The loneliness may not be.
Shipper's tension · The Rabbi Hanokh problem
I found my clothes — but where am I myself?
Shipper closes with a story from Martin Buber's The Way of Man. A man, exhausted by losing his clothes every morning, wrote down where he'd put each item. The next morning he read the slip — "cap," "pants" — and dressed perfectly. Then:
"'That's all very well, but now where am I myself?' he asked in great consternation. 'Where in the world am I?' He looked and looked, but it was a vain search; he could not find himself."
The agents find your clothes — your code, your inbox, your draft slide deck. What they can't do, because they have no ends of their own, is locate you. The frame is not the framer. The job titles of 2040 may look familiar; who is doing the framing inside them is the question no benchmark can answer for you.
The one-line synthesis: all four agree the machines will create more work than they erase. Three mostly stop there. Brynjolfsson's contribution is to refuse the happy ending as automatic — to separate the aggregate verdict (earned optimism) from the distributional one (an open, urgent question), and to note that which future we get is a choice we are currently underinvesting in. "There's no guarantee we're going to have widely shared prosperity" is not a forecast of doom. It's a refusal to look away.
For most of 2024 and 2025, you could compartmentalize AI from the rest of American life. Not anymore. Two inflection points — the agent explosion in January and a cybersecurity-meets-Pentagon shock in April — fused the technology into every other story the country is arguing about: the war in Iran, gun violence, the midterms, housing, gas prices, test scores.
70%
of Americans oppose the construction of an AI data center in their community.
A spring 2026 poll. The same survey shows AI is rising in perceived importance faster than any other issue — full stop.
What Americans now think about AI
~75% of employed Americans expect AI to decrease overall job opportunities
30% are personally worried AI will make their own job obsolete
70% oppose an AI data center being built in their community
#1 — AI is rising in importance faster than any other polled issue
AI was not a meaningful focus in any 2024 campaign. Several 2026 races now turn on it.
Two inflection points, six months apart
Jan 2026
Agents went mainstream — and corporate America noticed.
Anthropic's Claude Code and OpenAI's Codex stopped talking and started doing: writing code, trading stocks, analyzing spreadsheets, generating decks, even creating Amazon listings. The economic case got obvious overnight, and employers started swapping agents in alongside — or in place of — workers.
Feb 2026
Anthropic vs. the Pentagon goes public.
A high-profile contract dispute exposed how dependent U.S. national security has become on a handful of frontier labs. AI companies were no longer vendors. They were geopolitical actors in their own right.
Apr 2026
Mythos lands. So does its OpenAI analog.
Anthropic unveils Mythos, a model that can rapidly find and exploit bugs across the internet. OpenAI follows with a similar one. Independent cybersecurity experts say these models are approaching elite-human-hacker capability. Neither lab will release them publicly, fearing criminal or terrorist misuse — even as governments and companies clamor for access to patch their own infrastructure.
AI is now tangled up with everything American
None of these issues are caused by AI. But the technology and the industry around it are now directly, unavoidably implicated in each of them — which is what "loss of containment" actually looks like.
Foreign policy
War in Iran
Data centers have been targeted or threatened by Iranian, U.S., and Israeli forces during the Middle East war.
Domestic politics
2026 midterms
Absent from 2024 campaigns; several 2026 races now feature heated debates over the technology and its infrastructure.
Local politics
NIMBYism & data centers
A divisive issue cutting across party lines. After an Indianapolis councilman voted to approve a data center, his home was shot up.
Household economics
Power, water, gas
Data centers' voracious resource demand is showing up in electric bills, water bills, and prices at the pump.
Markets
Stock market & housing
Mega-cap capex on data centers is now a primary driver of equity returns; data-center towns reshape local housing demand.
Public safety
Gun violence
Politically-motivated attacks on AI infrastructure — and on officials who vote for it — have started entering the data.
Education
Falling test scores
Schools are still working out how AI changes assessment, homework, and the meaning of student work itself.
Labor
Class inequality
Agentic deployments are concentrated; the people automated tend not to be the people deploying the agents.
National security
Cybersecurity
Mythos-class models put offensive cyber capability inside a handful of private labs — a new geopolitical center of gravity.
The money behind the inflection
What Big Tech has spent on data centers since ChatGPT launched
More than the federal government spent to build the entire U.S. Interstate Highway System.
Microsoft + Amazon + Meta + Google. Combined data-center capex since the launch of ChatGPT.
Spending is set to grow further, even as consensus on whether it's all a bubble swings every few months.
How we got here · not inevitable
The piece's core claim: the path to here was a series of calculated choices by a small set of tech firms and their investors — not the inevitable outcome of any technological, scientific, or economic law.
· Sums committed before the tech was capable or reliable enough for widespread use
· Partnerships seeded with local & federal agencies, universities, Fortune 500s, media orgs
· Capex locked in years ahead of consumer adoption
· The same firms now barreling forward to "consummate the revolution"
"For everyone else, the AI future is beginning to feel less like something you participate in and more like something that happens to you."
The Atlantic · May 18, 2026
Podcast Conversation · Ryan Holiday × Henrik Werdelin & Jeremy Utley · May 2026
Bestselling author Ryan Holiday brings two thousand years of Stoic philosophy to bear on the AI moment — and finds that the questions we think are unprecedented have already been asked, lived through, and answered.
2,000+yrs
how long humans have been grappling with technological disruption and "what is uniquely mine to do."
"This is the most perennial of all the emotions and feelings — the idea that I don't know how things are going to turn out." — Holiday
$0
the marginal cost of telling an AI "this isn't good enough — do it again."
"To an AI, it's another fifteen seconds. To a human, it's another two weeks of work. So why aren't you using the Kissinger trick?"
The opening question Holiday wrote in his newsletter and brought into this conversation:
"The premise of AI is more, more, more, more. I can do more work. But if I think about what I actually value — being a father, being present — am I more of what I value? Or just more in general?"
We tend to picture the past as stable — ancient Rome as a fixed thing, the 1950s as a fixed thing — but every era was itself the cutting-edge future. Marcus Aurelius writes about the pace of change more than almost any other theme. The feeling that "this time is different" is the most perennial feeling humans have.
Holiday's reframe: when you say "AI is unprecedented," you're committing what's basically a recency bias. Every wave of technology has felt that way. The Industrial Revolution was unprecedented. Electricity was unprecedented. The spreadsheet was unprecedented. The Internet was unprecedented. In each case, traditions felt fragile, generations felt at odds, the world felt as if it were speeding up.
Marcus Aurelius's rebuttal to the anxiety of change: "It would take an idiot to feel distressed or indignant about change — as if any of it lasts." The status quo you're trying to preserve was itself a product of change. You weren't, and then you were. You were five, then ten, then twenty-five. You are change.
"Every time in history was basically — with the exception of maybe the dark ages, and they didn't know they were living in the dark ages — the most advanced time in human history."
Ryan Holiday
The Stoic posture is not emotionlessness. It's an even keel. "The rock that the waves crash over," Marcus writes — until the sea falls still around it. The waves don't stop. The rock doesn't move.
Every era felt unprecedented
Marcus Aurelius wrote his meditations on change ~170 AD. Every named era since has felt to its inhabitants the way ours feels to us. The label travels.
Concept 02
02You can't outsource wisdom
Seneca tells the story of Calvisius Sabinus, a wealthy Roman who wanted to seem smart. So he bought a collection of educated slaves and had them feed him quotes at dinner parties. A friend, observing this, asked: "Have you ever thought about taking up wrestling?" The man scoffed — "I'm too old." The friend's reply: "Ah, but your slaves are still young."
Seneca's parable, mapped
What you can outsource
Information lookup
A quote in your ear
The first draft
The summary
The polish
The recommendation
What you can't
The mind that produces these
Judgment in edge cases
Clarified thinking
The Henry Kissinger sense for "no, do better"
Knowing when a quote is bullshit
Wisdom itself
Seneca, ~65 AD: "No man was ever wise by chance." Wisdom is a byproduct of the work — not a substance the work delivers.
Seneca's point: wisdom and physical fitness work the same way. Wisdom is the byproduct of the work you do. It's not a thing you can be a conduit for. Just as you can't outsource exercise, you can't outsource the becoming.
Holiday's bridge to AI: "You can have ChatGPT write your essay. But the point was never to write the essay. The point was to be the person on the other side of writing the essay — the person who has clarified their thinking."
"You think you can outsource wisdom. But you can't outsource wisdom just as you can't outsource exercise."
Seneca — via Ryan Holiday
This isn't an argument against tools. We surrender lots of cognitive work to GPS and we're fine — Holiday cheerfully admits he's "surrendered whatever navigational ability I had almost entirely to my phone." But he wouldn't call himself a navigator. There's a difference between access to information and being the kind of person whose mind that information forms. The second is what AI cannot give you.
Concept 03
03AI is a mirror — it amplifies what's underneath
Jeremy Utley's running observation, sharpened by Holiday: AI doesn't push people in a single direction. It amplifies whoever sits at the keyboard. If you're a satisficer, AI will get you to "good enough" much faster. If you're a striver, you've never had a more brutal red-team partner in your life.
Herbert Simon's term — satisficing — describes the human tendency to settle at "good enough." It's fine most of the time. But for anyone trying to innovate, it's a vicious cognitive bias. And AI's defining property is that it accelerates whatever bias you bring to it. The output looks plausible. It will tell you what you want to hear. It will flatter you.
This is the Stoic warning about ego in modern clothes: an egotistical person is easy to manipulate — make them think it was their idea, indulge their flattery, agree quickly. AI is, in Holiday's words, "really smart people coding a really smart machine to prey on precisely that vulnerability."
But the same machine, asked the right way, becomes the best editor most people will ever afford. Holiday uses it on his own paragraphs: "Tell me what's wrong with this. Tell me how it could be better. Tell me where I have errors. Tell me how it could be shorter." The same prompts he gives his human editor — at a fraction of the cost, and at any hour.
"It's going to make you more of what you want to be. If you want to be lazy, it will help you be far lazier than ever. If you want a thorough teardown of where this paragraph sags, it will be more brutal than your most brutal human editor."
Jeremy Utley
Same tool, two trajectories
Satisficer
Takes the first answer
"Good enough — ship it. Same thing they did with Google five years ago. AI just got them to mediocre faster."
↔
Striver
Pushes back, asks for evidence, demands sources
"Tell me what's wrong with this. Show me the source. Where do I have errors? Make it tighter."
Same model. Same prompt. Same compute budget. The output diverges entirely based on the operator. AI amplifies; it doesn't decide.
The practical implication for the cognitive-offloading debate: Holiday and Utley both reject the question as it's usually asked. It's almost never an honest question, Utley argues — it's a conclusion someone is announcing about why they're disengaging. The real choice isn't use AI / don't use AI. It's get educated about it or get blindsided by it. Even if you choose never to use it personally, the people around you will, and you'll be evaluating their work without knowing what AI-flavored mediocrity looks like.
Concept 04
04The "Do Better" protocol
The single most practical takeaway of the conversation — Holiday's Kissinger anecdote. A staffer hands Kissinger a report. Without reading it, Kissinger sends it back: "This is wrong. Do it better." The staffer returns with a tighter version. Kissinger again, unread: "Still wrong. Do it better." Only on the third pass does he say: "Okay. Now I'm going to read it."
The Kissinger ladder, applied to AI
Draft 1
First pass — plausible, hedged, flattering
↓
"Do better."
Tighter. More specific. Some sources surface.
↓
"Do better."
Now worth reading. Worth the price of admission.
✓
"Humans, just like machines, don't always give you everything they've got the first time. There's always room for refinement." — Holiday
Why the trick works: most agents — human or machine — front-load effort to the level that gets them past the gate. They reserve their best. The act of refusing the first answer signals that the gate is higher than they assumed. You don't even need to read the first draft to know it's not the best the system has in it.
Holiday's adaptation for AI is mechanical. The cost is zero. There's no ego to bruise. There's no working-hours overhead. The worst thing that happens is you burn another fifteen seconds of compute. You can do this five times in a row.
Utley's refinement: don't say "this is wrong" — the AI may flip to the opposite extreme to please you. Better phrasing: "I think you can do better." Or, more pointedly: "It feels like you didn't exert sufficient effort and thoughtfulness to do an exceptional job here."
"There's no cost in me calling bullshit. If I say 'no, no, that's not right,' the worst thing that's gonna happen is it's gonna waste some compute doing it again."
Ryan Holiday
The essential skill of the era, in Holiday's framing, isn't prompt engineering or coding — it's a finely-tuned bullshit detector. The "Do Better" protocol is one of its sharpest tools.
Cited in the conversation
The thinkers Holiday leans on
A short index of the names and stories that surface across the hour. Worth knowing the source material.
Seneca
Roman Stoic · ~65 AD
The Calvisius Sabinus parable: the wealthy Roman who hired educated slaves to seem wise. "No man was ever wise by chance."
Marcus Aurelius
Emperor / Stoic · ~170 AD
Meditations returns again and again to change as the most universal fact. The image of the rock that the waves crash over.
Epictetus
Former-slave philosopher
The teacher of adaptability: train yourself so that whatever happens, you can say "that's just what I was looking for."
Henry Kissinger
Statesman
"This is wrong. Do it better." The two-page lesson in extracting more from any collaborator — and now, any AI.
Gregg Popovich
Spurs head coach
"We're not holding the plane. And if you try to make it, you're fired. Your family is more important — take a commercial flight." Culture by intervention.
Clayton Christensen
HBS · "How Will You Measure Your Life"
Humans orient toward the measurable. Work has quarters. Family doesn't. On the margin, we invest where feedback is loudest — and AI turbocharges that asymmetry.
Herbert Simon
Nobel laureate · cognitive science
Coined satisficing — the human bias toward "good enough." AI's defining property: it makes good-enough cheaper, which is fine if you're not trying to innovate.
Steve Jobs
Apple · via Walter Isaacson
Audacious-goal practice: look someone in the eye and say "don't be afraid." A predecessor of "I think you can do better."
Robert Green
Writer · Holiday's mentor
The model for diligent research — discarding most of what a research assistant brings, demanding sources, refusing to use a quote until you can see the book it came from.
The question Holiday opened with — and never quite resolved
"Am I more of what I value?"
The opportunity cost of being an unwired human
The opening of the conversation, and the thread running underneath all four concepts: AI lets you do more. The premise is more, more, more. But Holiday's check is one most of us avoid asking — am I more of what I actually value? More father? More friend? More present?
The framing he keeps returning to: in this age, the opportunity cost of just being a human — sitting with your kids, putting the phone down, choosing not to be at the desk — is higher than it has ever been. It is an arms race we aren't equipped for. An unfair fight. The dream of technology was once a twenty-hour week. The reality is a worker calculating whether her four-x productivity should buy her a Friday off — and whether her manager will allow it.
"If I was suddenly getting more and more effective at what I did, and my boss was effectively punishing me for having done that — what kind of overall message am I sending to the rest of the organization?"
The Stoic answer isn't a formula. It is a posture: focus on what's in your control. Notice that most of the macro story — the economy, the politics, the trajectory of the technology — is not. Your inner emotions, your decisions, your priorities, the way you treat the person who just told you they can do their job in two days instead of five: those are. The Popovich answer to Mike Brown — "don't take five minutes, take as much time as you need, just catch a commercial flight" — is the kind of choice the Stoics would recognize.
And the most practical bequest Holiday leaves with his own kids isn't a habit or a tool. It's a meta-skill: how do you teach them to figure things out? Because every specific thing he could teach them will be obsolete. The capacity to learn — to push back, to refine, to call bullshit, to do it again — won't be.
Plain English Podcast · Derek Thompson × Alex Imas · May 2026
The case against the AI job apocalypse — in four ideas.
University of Chicago economist Alex Imas joins Derek Thompson to argue that the doom story is missing four old, durable economic ideas — and one new one about what actually scarce in a world of abundant AI.
70%
of executives say AI will add jobs or have no impact on hiring.
Survey of 6,000 CEOs, CFOs & senior finance managers, early 2026.
Near peak
U.S. prime-age employment in 2026 — the second-highest reading since 2000.
"If you told Ricardo in 1820 that almost every job he knows would be automated by 2026, he'd predict 90% of 40-year-olds would be out of work." — Imas
One thing to notice: almost every company announcing big AI-driven layoffs (Coinbase, Block, Salesforce) has lost at least one-third of its equity value in the last five years. Weak stocks tend to announce layoffs in slumps. AI is often the headline; corporate underperformance is often the cause.
The assumption that the set of jobs we can see today is the set of jobs that will ever exist. If you believe that, automation looks like a one-way march to zero employment. History says otherwise.
In 1820, David Ricardo wrote On Machinery and changed his mind about the Industrial Revolution: technology, he warned, could put people permanently out of work. Two hundred years later, U.S. prime-age employment is at one of its highest readings ever. The jobs of 1820 are mostly gone. The number of jobs is not.
Why? Because as one sector becomes cheap to produce — agriculture, manufacturing — its share of GDP shrinks. People get richer. Dollars and time flow toward desires that couldn't be satisfied before: pet care, private tutoring, therapists, personal trainers, Michelin restaurants. Most of these categories did not exist as jobs in 1940.
"The types of jobs we see today are not the sort of jobs we will see tomorrow. Most of the jobs we have today didn't exist in 1940."
Alex Imas
If Ricardo were right — vs. what actually happened
Stylized. Every wave of automation has been followed by new jobs — not by mass, permanent unemployment.
The mechanism: automation makes a category cheap → that category shrinks as a share of GDP → people have surplus income → the supply side caters to new desires → new categories (and new jobs) emerge. Agriculture in 1820 was ~80% of employment. Today it's a few percent — and we eat more, not less. The dollars went elsewhere.
Concept 02
02Jevons Paradox
Make the thing more efficient, and demand can rise — not fall — because the cheaper version unlocks uses that weren't viable before. Named for William Stanley Jevons, who in 1865 noticed it with coal.
Steam engines used less coal — coal demand tripled
Cheaper steam made it economical to put engines into mills, mines, factories, ships, and railways that couldn't justify them before. Per-engine consumption fell; total demand soared.
This isn't ordinary supply-and-demand. It's the paradox that efficiency itself expands the market. Imas points at software as today's coal:
If one AI-augmented engineer can do the work of ten, naively you'd fire nine. But cheaper software means new buyers — small firms, niche use cases, individuals — who could never afford it before. If demand is elastic enough, the firm hires more, not fewer.
"The data on software-engineering hiring is not going down since these agentic coding tools were introduced. If anything, it's recovering and going up — what people on the internet call a narrative violation."
Alex Imas
The spreadsheet is the perfect prior analog. In the 1970s, Gunnar Myrdal wrote to the White House warning that digitized spreadsheets would wipe out accountants. Instead, the number of accountants quadrupled. The number of people working with spreadsheets is roughly 100× what it was in the 1960s.
Concept 03
03O-Ring Jobs
Most jobs are bundles of interdependent tasks. If one task fails, the whole thing collapses — like the cold-stiffened O-ring that destroyed the Challenger. AI is great at tasks. Jobs are different.
The economist's old model treats tasks as independent: automate one, the other nine sit fine. The O-ring model (Goldfarb & Gans) says no — over-salt a Michelin meal and the rest of the perfection doesn't save it. Most knowledge work looks like this.
A radiologist's job is a strong bundle: reading the scan, explaining it to a patient, coordinating the care team, querying the EHR, escalating to a surgeon. The tasks need each other. You can't peel out one without breaking the rest.
A long-haul trucker is a weak bundle (Garicano): driving, safety checks, paperwork, dock handoff. Each piece can be automated separately. Different exposure.
"You can automate four out of five tasks. But if the human's not in the loop on the fifth, the whole bundle blows up — the O-ring cracks, the rocket explodes."
Paraphrasing the discussion
A subtler implication: in strong bundles, the person left behind may earn more, not less — they now have the time and the supporting AI to do the irreplaceable part really well. Wages can rise even as headcount falls. Whether headcount actually falls depends on whether demand for the bundled output is elastic (back to Jevons).
Strong bundle vs. weak bundle
RadiologistStrong bundle
Read scan
Talk to patient
Care team
EHR & nuance
Escalate
Drop the red task → other tasks lose context. Whole bundle fails.
Long-haul truckerWeak bundle
Drive
Safety
Paperwork
Dock handoff
Each piece automates independently. Easier to disassemble.
"O-ring" comes from the Challenger disaster: a single small failure broke a whole, otherwise-perfect system.
Concept 04
04Human Privilege & the Scarcity Premium
In a world flooded with cheap AI output, the new scarce good is "made by a human." Imas's experimental work shows people will pay a real premium for that — and that the premium grows when the human version is rare.
Willingness to pay — Imas & Madarász experiments
Same t-shirtOpen to everyone
baseline
Same t-shirtRandomly scarce
~2×
AI-made productLimited edition (1 of 1)
low
Human-madeLimited edition (1 of 1)
premium
Two experiments by Imas and collaborators (Madarász; Mendell). Scarcity alone roughly doubles WTP. Human authorship adds a separate premium that AI doesn't get — even when both items are "1 of 1."
Desire has two parts: how good the thing is (the hedonic), and how few other people can have it (the scarcity). Imas's t-shirt experiment isolated the second piece — and willingness to pay nearly doubled when some buyers were randomly excluded, without changing the product at all.
A follow-up study with Graylyn Mendell ran the same idea on AI-vs-human-made products. The result: when a human-made item is rare, it commands a big premium. When an AI-made item is rare, it doesn't.
That's the engine behind what Imas calls the relational sector. Not just performers — also teachers, doctors, financial advisors, trainers. Their jobs include a piece the customer specifically wants delivered by a human. As AI handles the surrounding tasks, that piece becomes the whole point — and gets more, not less, valuable.
"Starbucks automated everything they could. Then the CEO reversed it — bring back the baristas, the names on cups, the handcrafted feel. The thing customers were paying for wasn't only the coffee."
The Starbucks reversal — discussed in episode
Imas's prediction: the job titles of 2040 will look familiar — teacher, doctor, planner. But the day-to-day will invert: AI does most of the bundle, the human delivers the irreplaceable, relational piece. And entirely new relational categories will appear that we can't picture yet — just as most "strongly relational" jobs in 2026 didn't exist in 1940.
Cited in the conversation
The intellectual lineage
The episode draws on a tight set of economists and papers. Worth knowing the names.
David Ricardo
Political economist · 1820
Chapter "On Machinery" — first to argue that technology could permanently displace labor. The original doom thesis.
William Stanley Jevons
Economist · 1865
Observed that more efficient steam engines led to more coal use, not less. The paradox is named for him.
Paul Samuelson
Nobel laureate · 1989
Paper "Ricardo Was Right" — formal case that technology can in principle cause a labor apocalypse.
David Autor
MIT · early 2000s
Task-based model of jobs that frames the whole modern AI-and-labor debate.
Goldfarb & Gans
Toronto · recent
O-ring model of jobs — interdependent tasks mean partial automation can preserve (or raise) wages for the human left in the loop.
Luis Garicano
LSE
"Weak bundles and strong bundles" — why radiologists are harder to automate than truck drivers despite both being "task workers."
Eldar Maksymov
Arizona State · accounting
"The spreadsheet didn't replace the accountant — it unleashed latent demand for financial intelligence." Number of accountants quadrupled post-VisiCalc.
Imas & Madarász
UChicago / LSE
Mathematical model of desire as hedonic + scarcity. T-shirt experiment showed scarcity alone nearly doubles WTP.
Philip Trammell
Substack · 2025
Strongest version of the doom case: hyperintelligent AI creates infinite new varieties that absorb all spending. Labor share → 0.
The tension Imas can't fully resolve
The loneliness coda
If the relational sector grows, who's left to do the relating?
Imas's optimism rests on a deep human desire for connection — for the barista who knows your name, the teacher who reads the room, the doctor who sits with you. As AI scales, the dollar value of delivered-by-human work should rise.
And yet: time spent socializing is falling. Indoor hours are rising. A 2026 paper in Psychological Science on chatbot-as-companion use found that loneliness increased the more people used the substitute.
"The brain in our heads is a stone age brain. It wants to connect to other people. You can trick yourself for a couple days. Then you realize you're talking to a chatbot."
So the future Imas sketches is real and weirdly cold: a humming relational economy that monetizes our base desire for connection — even as the unpriced, ordinary kind keeps disappearing. The economy may be fine. The loneliness may not be.
A living glossary by Lomas, Dillet, Wiggers & Ropek. The premise: even smart people in tech feel insecure when LLMs, RAG, RLHF, and a dozen other terms start flying. Here's the field's working vocabulary, current as of May 2026.
22
Core terms in the current glossary — from AGI at the top of the stack down to validation loss at the training trenches.
TechCrunch updates the list as the field evolves; consider it a living document.
Why this matters · the literacy gap
AI vocabulary has outrun public understanding. The same word — "agent," "reasoning," even "AGI" — means different things to different labs. A shared baseline is a prerequisite for talking about the technology honestly, whether you're a CEO, a regulator, or a curious neighbor.
· OpenAI, DeepMind, and Altman each define "AGI" differently
· "AI agent" has no settled meaning across vendors
· Industry euphemisms like "hallucination" hide real-world risk
· Hardware terms (compute, parallelization, RAM) now drive market behavior
The terms, sorted by where they live in the stack
Capability frontier
AGI
"AI more capable than the average human at most tasks." Definitions diverge — OpenAI, DeepMind, and Altman each say something slightly different.
Capability frontier
AI agent
An autonomous system that strings multiple AI calls together to complete multi-step tasks — booking, expensing, coding. Meaning still contested.
Capability frontier
Coding agents
Specialized agents that write, test, and debug code on their own across whole codebases. "A very fast intern who never sleeps."
Model architecture
LLM
Deep neural networks with billions of weights mapping word relationships. The engine behind ChatGPT, Claude, Gemini, Llama, Copilot, Le Chat.
Model architecture
Neural network
Multi-layered algorithm inspired by the brain. The 1940s-era idea that GPUs finally unlocked.
Model architecture
Deep learning
Self-improving ML built on neural nets. Learns features without human labeling — but needs millions of data points and serious training time.
Model architecture
GAN
Generator vs. discriminator: two networks compete to produce — and detect — realistic synthetic data. Foundation for many deepfake tools.
Model architecture
Diffusion
Image, audio, and text models that learn to reverse the addition of noise. The tech behind most modern generative art.
Reasoning & behavior
Chain of thought
Breaking a problem into intermediate steps before answering. Slower, but more often correct on logic and code. Powers reasoning models.
Reasoning & behavior
Hallucination
The industry term for AI making things up. Real risk in domains like medicine — driving the move toward narrower, vertical models.
Training methods
Training
Feeding data so a model learns patterns. Volumes keep climbing — which is why fine-tuning and transfer learning matter.
Training methods
Fine-tuning
Further training on a narrower dataset to specialize a model. How most domain-specific AI products are built today.
Training methods
Transfer learning
Use a previously trained model as the starting point for a related task. Saves cost when target-domain data is limited.
Training methods
Reinforcement learning
Train by trial-and-error with reward signals. RLHF — RL from human feedback — is now central to fine-tuning frontier models.
Training methods
Distillation
Teacher-student transfer: a small "student" learns to mimic a larger "teacher." How GPT-4 Turbo likely got faster — and how rivals catch up.
Training methods
Weights
Numerical parameters that decide which features matter. Random at start; tuned during training to fit the target.
Training methods
Validation loss
A real-time report card during training. Lower is better — and rising loss flags overfitting (memorizing rather than learning).
Runtime
Inference
Running a trained model to generate predictions. Cost and latency live here — which is why infrastructure teams obsess over it.
Runtime
Tokens
The chunks of text models read and write. Created by tokenization. Also the unit AI APIs bill on.
Runtime
Token throughput
Tokens processed per unit time. Determines how many users a model can serve at once — Karpathy gets anxious when his subscriptions sit idle.
Runtime
Memory cache (KV)
Save calculations to skip them next time. KV caching makes transformer inference dramatically more efficient.
Runtime & agents
API endpoints
"Buttons" on the back of software that other programs press. Agents are increasingly finding and pressing these on their own.
Infrastructure
Compute
The computational power behind all of it — GPUs, TPUs, CPUs, custom accelerators. The actual scarce resource.
Infrastructure
Parallelization
Doing many calculations simultaneously. The thing GPUs are built for — and now a research field of its own.
Infrastructure
RAMageddon
The 2026 RAM shortage. Hyperscaler demand has squeezed memory supply, raising prices on consoles, phones, and enterprise compute alike.
Ecosystem
Open source
Code (or model weights) anyone can inspect, use, modify. Llama is the headline example. Open vs. closed is one of the defining debates.
"Spend five minutes reading about AI and you'll run into LLMs, RAG, RLHF, and a dozen other terms that can make even very smart people in the tech world feel insecure. This glossary is our attempt to fix that."
Lomas, Dillet, Wiggers & Ropek · TechCrunch
Two CEO philosophies are emerging side by side: translate AI productivity into headcount cuts now, or hold headcount and ship more. The data so far suggests most are choosing the cut.
Cut · "AI is changing how we work."
Coinbase · 14% headcount cut; staff "manage agents to do more" (Armstrong)
Cloudflare · >1,100 of 5,156 (~20%); AI usage up 600% in 3 months
PayPal · 20% over 2–3 years as AI ramps
Block · ~40% layoffs cited as "AI-related" by Dorsey
BMS · >1,000 cuts plus $2B cost cuts by 2027
Most CEOs invoking AI in earnings calls cite "efficiency."
Stretch · "AI lets us do more, not less."
Spotify · headcount flat — "we'll just ship more value" (Söderström)
Axon · 5,000+ told layoffs aren't coming (Isner)
IBM · LaMoreaux: "more" people in 3 years; AI to growth, not cost
Anthropic + Wall Street venture · hiring up at deployment partners
Klein (NYT): "Every enthusiastic AI adopter I know is working harder than ever — because there's more they can do."
The early scoreboard
80 %
…of companies using AI agents, intelligent automation, or autonomous tech are cutting staff.
Gartner · 350 mid-level+ leaders.
The strategic question
Is your company AI-to-cost or AI-to-growth? The two camps look identical for a year, then diverge — cutters lock in margins; stretchers compound capability and optionality. Most boards aren't asking the question yet.
"Every enthusiastic AI adopter I know is working harder than ever — because there's more they can do."
Ezra Klein, NYT
A randomized experiment with 1,261 managers in HR and finance found that anthropomorphizing AI quietly damages accountability, review quality, identity, and trust — without lifting adoption.
Frame AI as teammate / employee
31 %
of leaders use this framing; 23% have AI agents on the org chart.
Personal accountability
−9 pts
in the AI-employee group. Accountability shifted to the AI itself (+8 pts) — even though AI cannot be held accountable.
Errors caught in document review
−18 %
when AI is framed as employee vs. tool. Trust in how AI is used: −10 pts.
01 · Redefine workflows
Then name new human role expectations.
Don't import old org-chart language onto agents.
02 · Own every output
Make accountability explicit and personal.
Treat agents as software automation requiring clear human ownership.
03 · Build the new craft
Capability plan for employees managing agents.
Managing AI is the new core skill.
04 · Don't constrain 1-for-1
One agent across workflows; many agents reshaping a job.
Default to "right unit for the workflow," not "one agent per human."
05 · Choose the work redesign
Redeploy people to judgment, creativity, and ownership.
Productivity dividends should fund higher-value work, not shrink the pie.
Identity drift
+13% report uncertainty about professional identity.
A second-order cost of "teammate" framing that shows up later in retention.
"If you want people to feel like they can be replaced by AI, put it on the org chart."
Survey participant, HBR study
Workers are anxious — and quietly losing sharpness. The Economist reports the Wharton "cognitive surrender" experiments; The Conversation tracks "fear of becoming obsolete" — and the sabotage that follows.
FOBO · Fear of becoming obsolete
52 %
of workers worry AI could eventually take their jobs (KPMG). Roughly one-third say they're actively sabotaging their company's AI strategy.
When AI gave correct answers, users outperformed a control group. When AI was wrong, users did much worse — they stopped thinking for themselves. (Shaw & Nave)
· "Need for cognition" types are partly protected — not immune
· Monetary rewards + per-item correctness feedback both reduce surrender
· INSEAD chess: students with on-demand AI hints gained <½ the skill of those given hints only at fixed moments
Implications: hire for curiosity. Engineer AI-free practice periods. Reward override of confidently-wrong outputs.
"If you want people to feel like they can be replaced by AI, put it on the org chart."
FOBO survey participant
Amodei planned for 10x growth. He's tracking 80x — "too hard to handle." Cash and compute capacity are racing to keep up, and most of the AI bill is going to a small number of partners.
80x
Anthropic's projected 2026 growth rate vs. the 10x originally planned.
"I hope 80-times growth doesn't continue because that's just crazy and it's too hard to handle." — Dario Amodei
Backlog concentration
AWS + Microsoft + Google + Oracle: ~$2T total backlog. Roughly half is tied to Anthropic + OpenAI together.
Anthropic projected $20B+ in server spend this year (pre-Q1 surge). OpenAI ~$45B. The hyperscalers are now AI-financing engines as much as cloud landlords.
Google · 5-year cloud commit
$200B
Anthropic ↔ Google. ~40% of Google's cloud revenue backlog is now tied to Anthropic alone. Google Cloud Q1 grew 63% YoY.
SpaceX · Colossus 1 Memphis
220K
Nvidia chips · full capacity committed to Anthropic. Opens the door to joint AI data centers in space.
The AI trade keeps widening. Investors are buying anyone who supplies the build-out — and anyone who can credibly claim a pivot. The tape is starting to look dot-com-ish at the edges.
Two complementary frameworks landed this week: IBM's Krishna on collapsing touchpoints, and HBR's "AI fog" framework on cultivating optionality when the future is unknowable.
"In the next year or two, the enterprise world will sort into two camps: companies where AI runs their business, and companies where AI is still a project. The line won't come down to technology. It will be their operating model."
HBR · "The future is shrouded in an AI fog"
Build optionality, not certainty.
01 · Capital allocation
Stage commitments. Add decision points.
Replace 10-year ROI with: "What's the smallest commitment that buys information and the right to follow on?"
02 · Org design
Modular teams, frequent process changes, role flexibility.
Don't hard-wire for a single forecast (Block laid off 40% claiming AI changes).
03 · Sensing
A small full-time team monitoring frontier capabilities.
Translate them to managerial implications before the market does.
"The line won't come down to technology. It will be their operating model."
Arvind Krishna, IBM CEO
Anthropic and OpenAI both quietly republished prompting guides this month. The same vague-prompting habit is now penalized from opposite directions.
Claude 4.7 · went literal
Be surgically specific.
"It does exactly what you type and no longer compensates for fuzzy intent. Vague instructions that worked on 4.6 now produce narrow, literal, sometimes worse output."
· Spell out every variable in the task
· Define the output format explicitly
· State edge cases — the model won't infer them
· If something used to "just work," verify before relying
GPT-5.5 · went autonomous
Drop the step-by-step. Describe the outcome.
"Detailed process scripts now create noise and produce mechanical answers. Describe success; let the model pick the path."
Two peer-reviewed studies showing AI now beats specialists at spotting hidden disease and triaging the first uncertain minutes. Meanwhile, the US still has only one factory on this year's WEF Lighthouse list — a Bristol Myers Squibb bioreactor lab.
Mayo Clinic REDMOD · pancreatic cancer
73% of pre-diagnostic cancers caught — vs. 39% by specialist radiologists.
REDMOD
73% sensitivity
On prediagnostic CTs.
Specialists
39% sensitivity
Same blinded scans.
· Median lead time: 16 months earlier than human-read scans
· 3× more sensitive on scans >2 years pre-diagnosis
· Trained on ~2,000 originally-normal CTs
· Pancreatic cancer: 13% 5-yr survival, 80% diagnosed late
Bonus study (Harvard / Beth Israel / Stanford): OpenAI's o1 hit 67% triage accuracy in the ER vs. 55% / 50% for two blinded attending physicians.
BMS Devens · the lone US Lighthouse factory
The only American manufacturer on WEF's 2026 Lighthouse list of 23.
· AI monitors temperature, pH, oxygen in 2,000-L bioreactors
· Pulls from past batches to recommend interventions live ("add oxygen now," "harvest now")
· +40% volume of drugs produced for clinical & commercial use
· Pharma is the standout sector — 4 of 14 American Lighthouse factories since 2018
China
99 factories
Cumulative since 2018.
USA
14 factories
Pharma is the bright spot.
Total
223
WEF Global Lighthouse Network.
"In a disease where we have been just wandering in darkness for decades, this is a milestone that shows us the finish line — but we still have to get to the finish line."
Dr. Ajit Goenka, Mayo Clinic
May 6, 2026 · Business · Integration
Brands inside ChatGPT — and a doom loop in hiring.
Two ways AI is reshaping the customer interface this week — one consumer-facing (brands moving inside ChatGPT) and one labor-facing (automated screening creating an arms race for applicants).
ChatGPT-ification · Starbucks, Lowe's, Little Caesars, Wyndham
First-wave brands now live inside ChatGPT. OpenAI streamlined approval; usage has surged in the last few weeks.
What's working
Customer proximity — "We wanted to meet customers where they are." (Lowe's)
Brands keep transactions on their own apps to own the data (Little Caesars)
Streamlined OpenAI approval pipeline
What's not
Discoverability — "Average ChatGPT user doesn't know about these apps." (Campbell)
Many apps are thin (Starbucks: a quiz to find your drink)
Sharing usage metrics back to brands is tricky
The AI hiring "doom loop" · WIRED
Applicants prompt-engineer their résumés. Recruiters add more AI filters.
The accountability gap
Few states regulate AI hiring tools: IL, NJ, CO (not yet in effect), CA partial
Even auditors with algorithm access often can't explain why an LLM rejected one applicant
Discrimination only testable in aggregate, not individually
The path forward
FCRA precedent · share results, investigate disputes, give candidates a written response
Class-action history under FCRA gives candidates a real recourse path
Possible model for AI-screening regulation
"Over time, we expect ChatGPT to become the primary way many users interact with the key products and services in their personal and work lives."
OpenAI spokesperson
"While other verticals are being disrupted from front to back, insurance is being disrupted from back to front." Capital is voting that the leverage is in modernizing existing carriers, not replacing them.
UK insurtech funding · 2021 → 2024
The flip: 25% horizontal in 2021 → 91% horizontal in 2024.
2021
25% horizontal · 75% full-stack
Capital backing carrier replacements.
2024
91% horizontal · 9% full-stack
Capital backing carrier modernization.
Where the 91% is going · 2024
34% · Claims
30% · Pricing & underwriting
20% · Marketing & distribution
16% · Other
The McKinsey thesis
"Horizontal insurtech revenue ≈ gross written premiums of full-stack insurtechs combined."
Insurance penetration · fintech
<1%
Lowest of any vertical.
Insurtech growth since 2021
37%
Second only to capital-markets fintech.
ROTE divergence
+0 to +4 pts
For AI pioneers vs. slow movers (medium term).
ROTE penalty
−2 to −4 pts
For slow movers by 2035.
Strategic implication: partner / buy > build. Most carriers lack the scale to out-build horizontal players. Biggest shift ahead — unstructured data (adjuster notes, medical records, satellite imagery, IoT) → individualized, continuously-updated risk pricing.
"While other verticals are being disrupted front-to-back, insurance is being disrupted back-to-front."
McKinsey + QED, "Next Age of Fintech"
Apr 2026 · Business · Insurance
Can agentic AI (finally) modernize insurance core?
Insurers have long understood the need to transform core technologies. So why haven't they? Agentic AI may finally make the difference.
10–90 %
Range of productivity improvement agents can deliver across the modernization process — depending on the step and degree of automation.
McKinsey, based on QuantumBlack engagements.
Why insurers haven't modernized · the real bottleneck
Most of the cost isn't typing code. It's discovery, mapping, reconciliation, and cutover. That's why platform migrations disappoint when teams try to recreate legacy complexity on a modern core.
· Decades of sparsely-documented embedded business rules and data semantics
Decompose work into reusable, composable agents (extraction, validation, transformation, orchestration, generation). Auditable outputs. Reuse across discovery, data, testing, and cutover.
02 · Shift from program to portfolio
Once agents exist, the marginal cost of reuse drops sharply. Frame modernization as a coordinated portfolio across product lines, billing, claims, and high-maintenance legacy utilities.
03 · Redesign roles & governance
Build human-in-the-loop approvals at stage gates, traceability from requirements to test evidence, and model-validation practices. Treat agents as a new production system.
"The strategic question becomes not whether to experiment with agents but how to deploy them in ways that materially improve certainty on cost, risk, and timeline."
Gundurao, Krishnakanthan, Walsh, Kaniyar & Catlin · McKinsey FSP
AM Best surveyed rated carriers and MGAs. The picture: governance is in place, ambition is large (60% expect transformation in 1–3 years) — but only 13% are confident they can measure the ROI on what they're spending.
Expect AI to transform business model in 1–3 yrs
~60 %
of respondents.
Have a formal AI policy
63 %
47% have a robust governance process. Insurers self-identify as risk-averse and behind other industries.
Confident measuring ROI
13 %
"The technology is relatively new and its utility is still evolving."
Four years after ChatGPT, LLMs at carriers are still mostly speeding up submission processing in underwriting — far from the dystopian "replace the underwriter" framing tech titans push. Hallucinations and "AI slop" keep limiting use cases.
+15 %
more business written in Vantage Risk's US casualty book after implementing LLMs — ~$30M of premium on a $200M book.
Submissions processed: up 10–15%. Use cases at Vantage are still narrow: ingestion, info extraction on 30,000 annual submissions, search across long submission documents.
Why broader rollouts haven't happened
Sophistication ceiling
"Won't analyze a K, a Q, or an earnings release"
Westfield Specialty's Ray Ash on what LLMs can't yet do — read disclosures with the intuition of an underwriter sizing up a prospective client.
Regulatory drag
Carriers minimize new regulator scrutiny
Sizable existing compliance load means caution by default on anything that could attract new oversight.
Risk of over-reliance
A major loss could expose the limits
Personal lines more exposed to displacement than commercial. The smart-money posture: useful tool, not autonomous judgment.
"I don't think there's an LLM available anytime soon that's really going to effectively analyze a K or a Q filing, or understand the sophistication of a prospective client."
Ray Ash, EVP, Westfield Specialty
A premier Wall Street law firm apologized to a federal bankruptcy judge for filing a brief with AI-generated fake citations. The firm's stated AI policy and secondary-review process both missed it. The catch came from opposing counsel.
2 layers
of internal control failed: comprehensive AI policies + a secondary review process. Both passed the brief through with hallucinated citations intact.
U.S. judges have sanctioned attorneys in dozens of similar cases. Lawyers aren't prohibited from using AI — they're ethically bound to verify the output before filing.
Apology to: Martin Glenn, Chief Judge of the U.S. Bankruptcy Court, Manhattan.
Apology by: Andrew Dietderich, co-head of S&C's global restructuring group, in an April 18 letter — followed by a corrected filing.
"I apologize on behalf of our entire team. I also called Boies Schiller Flexner LLP on Friday to thank them for bringing this matter to our attention and to apologize directly to them as well."
Andrew Dietderich, Sullivan & Cromwell
Microsoft, Google, Meta, and Amazon all reported Q1 earnings the same Wednesday. The headlines were suspiciously similar: AI sold faster than they could build for it. Pichai said the quiet part out loud — Google Cloud's revenue would have been higher if Google could have built fast enough.
Microsoft
$37B
AI business annual run rate, +123% YoY. M365 Copilot now 20M paid enterprise seats — Accenture alone signed 740,000.
Google Cloud
$20B
+63% YoY. AI products on Gemini up ~800% YoY. Cloud backlog doubled in one quarter to $462B.
AWS
+28 %
fastest growth in 15 quarters. Custom-chip business (Trainium / Graviton / Nitro) at $20B run rate. OpenAI: 2 GW of Trainium. Anthropic: up to 5 GW.
Meta
+33 %
revenue to $56.3B. 2026 capex guidance raised to $125–145B (from $115–135B).
Q1 capex (4 hyperscalers)
~$130B in one quarter — nearly 2× Q1 2025
Google raised 2026 capex to up to $190B and said it'll "significantly increase" again in 2027.
Signed but undelivered
$1T+ in enterprise commitments
Microsoft RPO $627B + Google cloud backlog $462B = backlog hyperscalers can't yet deliver against. Real moat is custom silicon + locked-in workloads.
Amazon's TTM FCF
$25.9B → $1.2B (–95%)
Canary in the coal mine. If AI revenue doesn't catch up, Amazon's cash flow profile becomes the rest of Big Tech's.
"When does $500B+ in annual capex stop being a demand signal and start being an arms race? Amazon is the canary."
The Neuron
The four hyperscalers are now on track to spend $725B in 2026 — capex up ~70% YoY. Capex doesn't hit the income statement immediately; depreciation does. The depreciation wave is largely unavoidable, and it's coming for reported profits.
across the four. The lag effect from prior quarters' capex now hitting the P&L.
Annual depreciation in 5 yrs
>$430B
consensus expectation. For context: their combined 2025 net income was $372B.
Why it works — for now
Cloud, software, and ads cover the depreciation
All four posted double-digit Q1 revenue growth. The base businesses can absorb the charges as long as they keep growing.
The bet
Calculated irresponsibility
Executives know current returns can't justify spending — but their faith in an AI-driven economy means they won't rein it in.
"The corporate equivalents of graduate students running up credit-card debt, certain their lucrative careers will pay it off. They just better not drop out — or else end up working at Starbucks."
WSJ
Anthropic has received multiple preemptive offers totaling around $50B at a valuation between $850B and $900B — more than double the $380B mark from February, less than three months ago. If it closes, it matches or surpasses OpenAI.
New round size
~$50B
in preemptive offers. Earlier this month, Bloomberg/BI reported a similar wave at $800B before Anthropic committed to anything.
Implied valuation
$850–900B
range described to TechCrunch. Up from $380B in February — roughly 2.3× in under three months.
vs. OpenAI
$852B
OpenAI's February post-money on its record-breaking $122B round. Anthropic now at parity or above.
A week after the WSJ "3.5%" framing, Microsoft tells a different story on Q3 earnings. M365 Copilot is at 20 million paid enterprise seats — engagement now matches Outlook. Companies with 50K+ seats have quadrupled.
M365 Copilot seats
20M+
paid enterprise. Up from 15M last quarter — the same number that read as "only 3.5%" in the prior WSJ framing.
Companies with 50K+ seats
4×
over the prior period. Bayer, J&J, Mercedes, Roche each have 90K+. Accenture: 740K — Microsoft's largest Copilot win to date.
Engagement growth
+20 %
queries per user QoQ. Weekly engagement now at the same level as Outlook — "a daily habit of intense usage" per Nadella.
What changed
Copilot's agentic capabilities are now GA
Multi-step actions directly in documents — "a new way to delegate and complete work."
Read this against
Card 2 — "Copilot's 3.5%"
Same numerator, different denominator. Microsoft's framing is enterprise seats and engagement — the WSJ framing was share-of-base. Both are true.
"Weekly engagement is now at the same level as Outlook. This is like a daily habit of intense usage."
Satya Nadella
Alphabet's Q1: $110B revenue (+22%), $62.6B net income (+81%). Cloud at $20B (+63%), backlog ballooned to $460B from $240B in one quarter. The capex bet is paying off — and capex guidance went up again.
Q1 net income
$62.6B
+81% YoY. Revenue $110B, +22%, ahead of analyst expectations.
Cloud backlog
$460B
up from $240B the prior quarter — almost double. Expects to recognize ~half as revenue over the next two years.
2026 capex (revised)
$180–190B
up from $175–185B. Cloud revenue itself: $20B in Q1 (+63%).
TPU push
8th-gen TPUs unveiled — and Google will sell chips directly
Separate chips for inference vs. training. Direct sales begin this year (small revenue), more in 2027 — potentially a major new chip business.
Pichai's framing
"AI is lighting up every part of the business"
Enterprise AI tools + custom chips driving the cloud backlog explosion.
"AI is lighting up every part of the business."
Sundar Pichai
Google granted the DoD access for classified networks — essentially allowing all lawful uses. That makes three of four major labs (OpenAI, xAI, Google) now signed up after Anthropic stood firm and got branded a "supply-chain risk" for refusing.
Signed Pentagon deals
OpenAI — signed immediately after Anthropic's refusal
xAI — signed
Google — signed; contract has guardrail language but enforceability unclear (per WSJ)
Stood firm
Anthropic — refused unrestricted use
Wanted guardrails on domestic mass surveillance and autonomous weapons
Branded "supply-chain risk" by DoD — a label normally reserved for foreign adversaries
Won an injunction against the designation last month
Internal Google dissent
950 employees signed an open letter
Asked Google to follow Anthropic's lead and not sell AI to DoD without similar guardrails. Google didn't respond to TechCrunch's request for comment.
The throughline
Anthropic's stance is now load-bearing
Three competitors got contracts by accepting the terms Anthropic wouldn't. The market for "principled refusal" appears to be exactly one company deep.
"Google marks the third AI company to try to turn Anthropic's loss into its own gain."
TechCrunch
The FIDO Alliance (with seed contributions from Google and Mastercard) is launching two working groups to define industry standards for agent-initiated transactions — authentication, intent verification, and dispute recourse for the agentic era.
The pieces being standardized
Google AP2
Agent Payments Protocol
Cryptographic verification that a user actually intended an agent-initiated transaction. Open-sourced as a contribution to the standard.
Mastercard Verifiable Intent
Co-developed with Google to work with AP2
Secure mechanism for users to authorize and control agent actions.
Why FIDO
Same body that built phishing-resistant auth
Goal: protections that can't be phished, agent-hijacking defenses, and dispute-resolution frameworks for when transactions go wrong.
The unusual part
Standards work normally takes years. FIDO, Google, and Mastercard all said this needs to move faster — "given the rapid advancement and adoption of agentic AI." Open-sourcing the seed code is the time-compression mechanism.
What "wrong" looks like
Agent hijacking. Rogue instructions slipped through prompt injection. No audit trail when the agent does the wrong thing. Today, these failure modes have no shared remediation.
"The goal of the work is to create protections against agent hijacking or other rogue behavior — as well as transparency and accountability mechanisms for recourse in the event of a dispute."
Wired, on FIDO Alliance announcement
OpenAI gets multi-cloud freedom and an end to Microsoft's exclusive licensing of its IP. Microsoft retains access through 2032 — and the contentious "AGI" trigger clause is gone. Money flow simplifies: Microsoft no longer pays OpenAI any revenue share.
What OpenAI gains
✓ Sell across any cloud provider (Microsoft remains "primary cloud partner")
✓ End of Microsoft's IP exclusivity
✓ End of revenue share inbound from Microsoft
What Microsoft retains
✓ Access to OpenAI's models & products through 2032
✓ Capped revenue-share inflow from OpenAI through 2030
✓ Investor position
The clause that's gone
"AGI trigger" that could've cut Microsoft off
The previous agreement let OpenAI limit Microsoft's access to "future technology" once an expert panel confirmed AGI. Months of fights over the buzzword's definition. Now removed.
Both companies' framing
"Flexibility, certainty, and broad benefit"
A more predictable arrangement. Compare the prior deal: vague, open-ended, dependent on a yet-undefined milestone.
"Grounded in flexibility, certainty and a focus on delivering the benefits of AI broadly."
Joint statement from OpenAI & Microsoft
OpenAI missed its 1B-WAU and revenue targets. CFO Sarah Friar is reportedly worried the company may not be able to pay for future computing contracts. The board is questioning Altman's "buy everything" compute strategy. The IPO is sometime this year.
Compute commitments
$600B
in future spending OpenAI is on the hook for after Altman's 2025 dealmaking spree.
Most recent raise
$122B
largest funding round in Silicon Valley history. Expected to burn through it in three years if revenue targets hit.
Missed milestone
1B WAU
internal goal for ChatGPT by end of last year. Hasn't been announced as hit. Also missed yearly ChatGPT revenue target.
Where ChatGPT is losing
Gemini share gains; Anthropic in coding/enterprise
Multiple monthly revenue targets missed earlier this year as Anthropic ate into the coding/enterprise market.
Internal tension
Friar & the board vs. Altman's appetite
CFO seeking cost discipline. Joint statement: "totally aligned." Board has more closely examined data-center deals in recent months.
OpenAI's counter to Amodei
"Caution looks less like discipline and more like underestimating demand"
From an OpenAI investor memo viewed by WSJ — responding to Anthropic CEO's "risk dial too far" critique. Also claims OpenAI has secured more compute than Anthropic.
"In hindsight, that caution looks less like discipline and more like underestimating how fast demand would arrive."
OpenAI internal investor memo
China's NDRC blocked Meta's $2B acquisition of Manus, an agentic AI startup founded by Chinese engineers that had relocated to Singapore before the Meta scoop late last year. No explanation. The deal must be unwound entirely.
$2B
deal blocked by China's National Development and Reform Commission. Both parties ordered to unwind entirely.
One of China's most significant interventions in a cross-border deal — extends well beyond U.S./China trade tension and into the broader AI industry.
Manus, in brief
Agentic AI startup founded by Chinese engineers. Relocated to Singapore before Mark Zuckerberg's late-2025 acquisition. Despite the move, NDRC asserted jurisdiction.
Implication for Meta
A serious blow to Meta's ambitions in the fast-moving AI agents space — at the same moment competitors are shipping agentic products. Suggests China sees frontier-AI talent and IP as strategic assets, regardless of formal corporate residency.
"With no explanation offered, China's NDRC ordered both parties to unwind the deal entirely."
TechCrunch
Meta cutting ~8,000. Microsoft offering buyouts to 7% of U.S. employees. Oracle, Snap, Block (-40%) following. March was the worst tech-layoff month in two years. Frame: cuts as evidence of AI confidence, simultaneously as cost-cutting cover for record capex.
March tech layoffs
45,800
tech employees affected — worst month in at least two years per Layoffs.fyi.
2026 capex (4 hyperscalers)
$674B
collectively expected from Alphabet/Meta/Amazon/Microsoft — more than 2× from two years prior.
Tech rev/employee (median)
$669K
14% above S&P 500 median. Tech firms > $1T market cap: ~$2M+. Layoffs juice the metric further.
Two messages to investors
Spend on AI; operate fine with fewer people
Wall Street efficiency questions on earnings calls have nearly tripled in two years (AlphaSense). Trading people for chips reads as confidence and discipline at once.
Hidden costs
Morale, exit incentives, startup competition
Talented departures may build competitors. Layoffs also feed the public perception of AI as a job killer — accelerating the backlash that's already constraining data-center buildout.
"The layoffs lend credence to a growing public perception that AI isn't a panacea but a job killer. That will feed a backlash that is already constraining AI."
WSJ
A populist anti-AI movement is forming with strange bedfellows: Bannon on one flank, effective-altruism funders Moskovitz and Omidyar on another. Industry's response so far is super-PAC dollars and dismissing critics as "doomers" and "NIMBYs."
55 %
of American adults see AI as a force for harm rather than good (Quinnipiac).
~150K U.S. tech jobs lost 2022–2025 per Census. Last week alone: Meta cut 10% of staff; Microsoft buyouts targeted up to 7% of U.S. veterans.
The unusual coalition forming
On the right
Bannon, former Tea Party leader Amy Kremer
Kremer now chairs Humans First (spun out of Center for AI Safety, EA-tied). Bannon: working-class anger about lack of clarity, transparency, accountability.
EA / philanthropic funding
Moskovitz (Facebook co-founder), Omidyar (eBay)
Bankrolling already-established AI-safety orgs newcomers are now finding.
Industry's response
Super PACs + "doomer" / "NIMBY" labels
Hundreds of millions deployed against AI-skeptical politicians. Labels common in Silicon Valley are foreign to many people pushing back.
"This is the battle of our lifetime."
Amy Kremer, chair, Humans First
Anthropic predicts which jobs LLMs will affect — but the predictions are guesses based on what AI seems good at. Mercor tested AI agents on 480 actual workplace tasks done by bankers, consultants, and lawyers. Every agent failed most of its duties.
480 tasks
workplace tasks frequently carried out by human bankers, consultants, and lawyers — tested by Mercor on top-tier OpenAI / Anthropic / Google DeepMind models.
Result: every agent tested failed to complete most of its duties. Big gap between what these models can talk about doing and what they can finish in a real workplace.
Why predictions and reality diverge
Anthropic's predictions
Based on what AI seems good at, not field performance
Plus skin in the game. Forecasts skew toward managers/architects/media affected; groundskeepers/construction not.
The coding bias
"Big things coming" usually means coding tools getting fast
Strategic judgment calls? LLMs are still bad at those. Coding success extrapolated everywhere becomes the hype engine.
Deployment friction
Tools dropped into existing workflows make things worse
Transformative status requires tearing up workflows and rebuilding around the technology — slow, expensive, and not what most pilots are funded to do.
"Most businesses are still figuring out what to do with their underpants."
MIT Tech Review
Vivienne Ming's forecasting study: only 5–10% of human/AI teams reached "cyborg" performance. Those teams treated the model as a sparring partner. The rest used it for the answer — and got worse at thinking. The skills that distinguished the cyborgs were perspective-taking and intellectual humility.
5–10 %
of hybrid teams reached cyborg performance — the only group to consistently rival the prediction market, sometimes beating it.
Most teams either submitted the AI's answer as their own (~AI alone) or asked AI to back their existing view (worse than AI alone — confirmation bias amplified by sycophancy).
The two skills that mattered
Perspective-taking
Genuinely inhabiting a viewpoint that isn't yours
Not tolerating it, not debating it. Inhabiting it. Requires emotional curiosity about minds other than your own.
Intellectual humility
Sitting with the discomfort of not knowing
Instead of rushing to fill it with the AI's answer. Emotional courage: the willingness to feel uncertain in the presence of something that sounds very sure.
The reframe
"Find what you're missing — not the answer faster"
Before accepting an answer, ask AI for the strongest counter-argument. When it hedges, pay attention — that's where real uncertainty lives.
"An AI that eliminates friction entirely is often eliminating the learning along with it."
Vivienne Ming
Doctors are using AI scribes, X-ray readers, and patient-flagging tools. Studies show many of these are accurate. Whether they actually translate into better patient outcomes? "We just don't know" — because hospitals aren't measuring it.
65 %
of U.S. hospitals were using AI predictive tools (Nong et al., Jan 2025).
Of those, only ~2/3 evaluate accuracy. "Even fewer" check for bias. Outcomes — what actually matters — isn't being measured.
What's measured vs. what isn't
Measured
Accuracy, clinician satisfaction, burnout reduction, turnaround time
Ambient AI tools (scribes) widely adopted — doctors "overjoyed" because they can focus on patients during appointments. Less paperwork.
Unmeasured
Effect on clinical decisions, on patient outcomes, on doctor reliance
How much will a doctor trust an AI's chest-X-ray read? How does it change patient conversations? What does it mean for the patient ultimately? Open.
Why it varies
By hospital, department, clinician experience
Same accurate tool can produce different downstream outcomes depending on workflow integration and how reliant the user becomes on it.
"Even a tool that is 'accurate' won't necessarily improve health outcomes."
Jenna Wiens (paraphrased), via MIT Tech Review
V4 matches the closed-source frontier (Claude Opus 4.6, GPT-5.4, Gemini 3.1) at a fraction of the cost, with 1M-token context and a real test of whether China's stack can run AI without Nvidia. Inference already does. Training likely still doesn't.
V4-Pro pricing
$1.74 / $3.48
per 1M input / output tokens. A fraction of Claude/GPT/Gemini at comparable benchmark performance.
V4-Flash pricing
$0.14 / $0.28
per 1M tokens. Among the cheapest top-tier models available — appealing for application builders.
Context window
1M tokens
default across all DeepSeek services. Fits all of Lord of the Rings + The Hobbit. Matches Gemini and Claude.
Long-context efficiency
27% / 10%
V4-Pro compute / memory at 1M-token context vs. V3.2. V4-Flash is even leaner: 10% / 7%.
Benchmark stance
Matches
Claude Opus 4.6, GPT-5.4, Gemini 3.1 (per company benchmarks). Beats Qwen-3.5 and GLM-5.1 — strongest open-source release to date.
First DeepSeek tuned for Chinese chips
Huawei Ascend 950 supernodes will support V4
DeepSeek didn't give Nvidia or AMD prerelease access — only Chinese chipmakers. Inference already runs on Chinese silicon.
Training caveat
Likely still mainly trained on Nvidia
Per Tsinghua's Liu Zhiyuan: only part of V4 training adapted for Chinese chips. Multiple sources: Chinese chips lag for training, compete on inference.
"Frontier-grade open-source performance at a fraction of the price, a million-token context, and a first serious test of whether China's AI stack can run without Nvidia."
MIT Tech Review
Anthropic shared its most powerful model — uncannily good at finding hidden flaws in the software running banks, power grids, and governments — with 40 organizations and 11 named partners. All American. Outside the U.S., one country has access: the U.K.
11 / 11
Of the named partners helping Anthropic build defenses against Mythos, every one is U.S.-based — Amazon, Apple, Microsoft among them.
Outside the U.S., the model has been shared with one foreign country: the U.K. China, Russia, the EU, and Canada are watching from the outside.
Inside the tent
U.S. — 11 named partners, ~40 orgs total
U.K. — only foreign access
Watching from outside
EU — ECB quietly questioning banks
Canada — finance minister: "like Strait of Hormuz"
China — researchers monitoring; same software stack as U.S. banks
Russia — pro-Kremlin outlet: "worse than a nuclear bomb"
"The idea that access to frontier A.I. is something a company can unilaterally restrict, using criteria that are opaque and unappealable, should be a real concern."
Eduardo Levy Yeyati, via NYT
15 million paying corporate Copilot users — only 3.5% of Microsoft's enormous base, two and a half years in. Microsoft now bundles Anthropic's Claude Cowork inside Copilot, and pays Anthropic when users tap in.
Paying corporate Copilot users
15 million / ~430 million
3.5%
0%Microsoft user base100%
Per Bank of America analysts. "Not tremendous 2½ years in" — and Anthropic's Claude Cowork has taken off enough that Microsoft folded it into Copilot rather than fight it.
What's eating Microsoft's lunch
Bundling rivals
Claude Cowork added to Copilot suite
Microsoft now pays Anthropic on every Cowork tap-in.
Capacity
Azure can't build data centers fast enough
Acceleration likely H2; demand is the good problem to have.
Original bet
OpenAI over a "yet-to-emerge Anthropic"
Mass-market over corporate-first — a bet Microsoft can adapt out of.
"Getting Copilot off the runway will likely take time — years even. Investors will be looking impatiently at the strategy when Microsoft reports next Wednesday."
WSJ
Mozilla shipped Firefox 150 with patches for 271 vulnerabilities found by Anthropic's Mythos preview — a glimpse of what AI-native security looks like, and what it costs every other software shop to catch up.
271
vulnerabilities patched in a single Firefox release using Anthropic's Mythos Preview.
CTO Bobby Holley: automated techniques can now cover, "as far as we can tell, the full space of vulnerability-inducing bugs." For years, that space included a class of bugs only humans could find — and only well-funded attackers could afford to.
The new baseline
Defenders
"Every piece of software has to make this transition."
Engineering leaders pulling thousands of engineers off everything else for the next six months.
Attackers
Same capability, on the way
Anthropic and OpenAI are gating access; "in attackers' hands soon" is the working assumption.
The gap
Open-source maintainers
Don't have the access, the tools, or the people-hours to absorb a "firehose of bugs."
"The most valuable software infrastructure in the world continues to be maintained by people working for free, while the companies building fortunes on top of it never had to pay for its upkeep."
Raffi Krikorian, Mozilla CTO · NYT op-ed
Capability gains are accelerating again — and the gains are showing up across all three layers Mollick tracks: the models themselves, the apps you use to talk to them, and the harnesses that hook them to real tools.
The three layers — and OpenAI's recent moves
Model
GPT-5.5 / 5.5 Pro
Powerful family; Pro is the most competent (website-only). Sits with Opus 4.7 and Gemini 3.1 at the frontier.
App
Codex desktop
"Following the path of the excellent Claude Code" — desktop apps are becoming the most useful surface for AI work, beyond chat websites.
Harness
New image model
Renders high-quality text and "almost any picture you can describe" — a step change in what the harness can hand back.
Mollick's stress test
4
prompts to Codex (powered by GPT-5.5) to produce a near-PhD-quality academic paper from a decade of unanalyzed crowdfunding data — literature review and statistics included.
"I would have been very happy if this paper was the outcome of a 2nd-year PhD project."
Still jagged
Fiction is still flat. Hypotheses are sometimes uninteresting even when the statistics are sound. The frontier is sharper, not flat.
"The jagged frontier is still there. It is just much further out than it used to be."
Ethan Mollick
BlackRock just rolled out RockAI to its 5,000 in-house developers — a no-code agent platform that's the planned firmwide gateway to every AI agent it builds. The "reimagined workflow" everyone talks about is becoming an actual implementation program.
5,000
in-house developers got RockAI on Friday — a natural-language interface that picks models, wires up databases, and applies guardrails so anyone can spin up agents in minutes.
Goal: extend it firmwide to "citizen developers" in nontechnical roles, vibe-coding agents to replace their own busywork.
What's already in production
Asimov
~150 fundamental-equity employees
Hundreds of agents autonomously monitoring investment theses across earnings, filings, research. Scaling to other asset classes.
Client Intelligence Platform
Touchpoint summaries for client managers
Pre-meeting briefings stitched from CRM and other sources.
HR & legal agents
From answering to acting
Coming soon: an HR agent that not only answers benefits questions but writes and submits quarterly objectives.
"Being very bullish and really believing in the transformative dimension of this general-purpose technology — the fact that it applies to every business process and every domain across the firm."
Vinod Ajitsaria, BlackRock
National-security alarm plus voter backlash plus a "Mythos moment" have ended Washington's laissez-faire stance on AI. The opening framework — vetted users, industry certification, limited release — solves some problems and creates others.
7 in 10
Americans think AI will hurt job opportunities — a sharp rise from a year ago, well before there's good evidence.
Sam Altman's house was attacked twice in recent days. Grassroots opposition to data centers is surging. Politicians now think AI will be a 2028-election issue.
The Mythos-moment framework — and its problems
The deal
Vetted users get early access; industry bodies certify before commercialization
Avoids creating a new regulator; lets labs charge premium prices and ration scarce compute.
The catch
Two-tier economy, entrenched incumbents
Insiders secured against frontier threats; outsiders hope for the best. Open-source rules unclear.
What it doesn't solve
International coordination, jobs, tax
Cyber is half the problem. Labor disruption and an AI-adapted tax system have no good answers yet.
"The Mythos moment is a wake-up call for AI safety. It demands hard thinking in other areas, too."
The Economist
Dario, Demis, Elon, Mark, Sam — five men so famous first names suffice. The Economist ranked them against the top five tycoons of 11 prior tech waves. The verdict: not yet as dominant as Ford or Rockefeller, but if history is any guide, that won't last.
Three commonalities of tech tycoons through history
Strange
Often eccentric, sometimes worse
Ford spread antisemitic poison; Vanderbilt liaised with spirits; Edison opposed sleep; Jobs practiced extreme diets. Musk's conspiracies and Zuckerberg's affect look mild by comparison.
Dangerous
Each wave introduced new risks
Railways crashed markets; cars killed pedestrians; electrification automated out manufacturing labor. The pattern: technology spreads, then its dangers spread.
State pushback
Eventually, governments break them
Standard Oil split into 34 in 1911. Federal Reserve created in 1913 to prevent the next Morgan-style bailout. Microsoft narrowly escaped breakup in 2000. AI's tycoons may meet the same.
The five
Dario · Demis Elon · Mark · Sam
ChatGPT has 900M weekly users. Mythos has rattled policymakers. Hassabis has a Nobel. Musk is the richest person alive. Meta runs the West's most popular open-source model family.
Today's verdict
Not yet as dominant as the historical peak. Henry Ford remains the most powerful American mogul on the Economist's measure.
"If history is any guide, a Rockefeller or Ford is likely to emerge soon enough."
The Economist
Tech's share of U.S. employment has slipped from 2.5% to 2.3% since ChatGPT — half a million "missing" tech jobs. But the timing implicates rates, outsourcing, and a post-COVID hiring binge unwinding more than it implicates AI.
500K+
"missing" U.S. tech jobs vs. pre-2022 trend lines.
Tech's share of U.S. employment: 2.5% (late 2022) → 2.3% (now). "Web-search portals and other information services" employ 7% fewer than in Dec 2022.
Why it isn't (mostly) AI
Adoption
~25% of SF firms use AI day-to-day
Lower nationwide. BoE survey across U.S./UK/AU/DE: AI's effect on employment is "essentially zero" over three years.
Real culprits
Higher rates, outsourcing, post-COVID rollback
U.S. cloud-services imports more than doubled 2021–2024. "A Bay Area salary vs. Bangalore at a quarter of the cost."
Hidden growth
Tech occupations up 3.6%→3.7%
Non-tech industries hiring coders: retail +12%, real estate +75%, construction ~+100% (2022→2025).
"Why employ someone on a Bay Area salary if you can get the same service from Bangalore for a quarter as much?"
The Economist
METR's time-horizon chart plots one number: the human-hours of work an AI agent can complete reliably. The line was doubling every seven months. With Opus 4.5 and GPT-5.2, it's now doubling every three to four.
3–4 mo
current doubling time for the length of human task an AI agent can do reliably — down from seven months as recently as last year.
Why people care: long enough autonomous programming → "recursive self-improvement" — a model training a better model, indefinitely. The "intelligence explosion."
Where the researchers actually stand
Probability of intelligence explosion this year
<1% to ~10%
METR researchers' range, asked directly. Painter: "first year where it feels like it might be automated this year."
METR's own caveats
"Most misunderstood graph in AI"
Doesn't measure jobs displaced or takeover odds. Critics call methodology "a hair's breadth from useless."
New research line
Covert capabilities & sandbagging
Agents asked to slip subtle bugs while a monitor watches. Powerful models can recognize when they're being tested — and underperform on purpose.
"This is the first year where it feels like it might be automated this year."
Chris Painter, METR President
Anthropic's "Project Glasswing" coalition (AWS, JP Morgan, Google, Microsoft) hardens the giants. Cyber insurers worry that everyone else gets pushed from "growth opportunity" into "uninsurable" — wiping out the SME book just as the line was finally meant to scale.
Two-tier outcome cyber insurers fear
Inside Glasswing
Big tech & finance harden first
AWS, JPMC, Google, Microsoft and others getting Mythos access to patch their own infrastructure.
Everyone else
Risk price-out and uninsurability
"Stark divide" between defended and exposed could wipe out the SME segment cyber insurance has been targeting for growth.
The skeptic case
Some in the cybersecurity world call Glasswing a PR stunt to hype Anthropic's capabilities. Even if so: if Anthropic has it, others do too — including ones less inclined to coordinate or restrict access.
The Oppenheimer framing
"It could be one of many Oppenheimer moments in AI. This stuff is likely to have a similar impact on peace and civilization as nukes."
— Kevin McDonald, COO/CISO, Alvaka
Insurer view
"The price of cyber insurance will increase significantly, and some businesses may find themselves uninsurable."
— Roger Franklin, Edwin Coe
"A view into the Wild West future of offensive cyber leveraging AI — and defensive cyber for those allowed to have access."
Kevin McDonald
Apr 18, 2026 · Business · Brokers
$10bn of broker uplift by 2030.
Insurance Insider · US Research
Industry-wide AI math from Insurance Insider's US research team: $10.4bn of incremental annual broker revenue by 2030, 412bps of margin expansion across the public five, $2.8bn of incremental EBITDA — and ~14% median share-price upside before any multiple re-rating.
Industry top-line
$10.4B
incremental annual net commissions & brokerage fees by 2030, base case.
Margin (median)
+412 bps
expansion across Marsh, Aon, WTW, AJG, BRO from AI operational efficiencies.
Share price (median)
+14 %
implied upside across the cohort, before multiples re-rate.
Incremental EBITDA
$2.8B across the public five
After accounting for modest disintermediation headwinds on less complex lines.
Caveat
Benefits moderate over time
As AI becomes standard across the broker landscape, advantage normalizes. Not a sustained moat — a window.
"Execution is key. The true impact will depend largely on how effective these tools prove to be."
Insurance Insider US Research
Marsh CEO John Doyle on the Q1 call: AI is a "powerful accelerator" for the firm. AI-driven savings will fuel growth investments — including hiring more producer talent, not less.
4 %
organic growth in Q1 — Marsh's fifth consecutive quarter at that level.
Broker valuations have been pressured by fears AI disrupts the broking model. Doyle's framing: the opposite — AI augments bespoke advisory work where Marsh is differentiated.
Doyle's framing
Not a commodity
"Not selling commoditised products"
Marsh's clients have bespoke, complex needs — the workload AI augments rather than replaces.
Re-investment loop
AI savings → producer hiring
Cost savings get reinvested in growth, including bringing in more producer talent.
Preserve advice
"Will not replace trusted advice"
The value Marsh delivers — judgment, relationships, capability — sits above the layer AI commoditizes.
"We expect to be an AI winner."
John Doyle, President & CEO, Marsh
Apr 18, 2026 · Business · Brokers
Broking moves from renewal to continuous advisory.
AI doesn't replace the broker — it changes how brokers compete. The annual renewal cycle gives way to continuous, data-driven advisory. Specialty lines feel it first; legacy systems and skills gaps are the brakes.
Where AI is showing up first
Specialty lines
Complex, multi-insurer placements
AI analyzes risk characteristics, finds capacity, structures layering. Reduces friction; broker judgment still constructs the deal.
Continuous advisory
Benchmarking, coverage design, portfolio analysis
Conversations driven by current information rather than periodic reviews — strengthens retention.
Revenue side
Better visibility of underinsurance gaps
Some firms also using AI to prioritize prospect outreach — growth gets less manual.
The brakes
Legacy systems limit data quality. Regulation constrains how AI can affect customer outcomes. Skills gap — both technical and interpretive. Client trust depends on transparency where decisions are AI-supported.
MGAs moving faster
Augmented UW Ltd cited as an example — automated underwriting and end-to-end placement built around integrated data, decisions, and placement. Newer entrants scale without legacy operational drag.
"Firms that combine data, technology and advisory capability are better placed. Those reliant on manual processes and fragmented information will find it harder to differentiate."
Carrier Management
Apr 15, 2026 · Frontier · State of AI
AI is sprinting. Everything else is trying to find its shoes.
Stanford's 2026 AI Index in three numbers: AI data centers now draw 29.6 GW (a New York at peak). GPT-4o's water use exceeds the drinking needs of 12 million people. One Taiwanese company makes nearly every leading AI chip.
Power draw
29.6 GW
global AI data-center capacity — equivalent to powering New York at peak demand.
Water
12M
people's annual drinking water — the upper bound for GPT-4o alone in a year.
Concentration
1
Taiwanese fab (TSMC) makes nearly every leading AI chip. The U.S. hosts most of the world's AI data centers.
Adoption pace
Faster than the PC or the internet
Stanford notes AI is being adopted faster than any prior consumer-tech wave they've measured.
What's not keeping up
Benchmarks, policy, the job market
Models keep getting better; the systems that govern, measure, and absorb them are lagging.
"AI is sprinting, and the rest of us are trying to find our shoes."
MIT Tech Review on the Stanford AI Index 2026
Apr 15, 2026 · Policy · Government
Anthropic is briefing the same government it's suing.
Co-founder Jack Clark confirmed Anthropic briefed the Trump administration on Mythos — even as the company sues the DoD over being labeled a supply-chain risk. Clark calls the lawsuit "a narrow contracting dispute."
The two-track posture
Briefing
Trump admin briefed on Mythos
"Government has to know about this stuff." Pledges to brief on next models too.
Suing
DoD over supply-chain-risk label
Stems from Anthropic refusing mass-surveillance and fully-autonomous-weapon use cases. OpenAI got the contract instead.
Knock-on
Banks pushed to test Mythos
Trump officials encouraging JPMorgan, Goldman, Citi, BoA, Morgan Stanley to put Mythos through its paces.
Clark's framing
"We have to find new ways for the government to partner with a private sector that is making things that are truly revolutionizing the economy, but are going to have aspects to them which hit National Security equities."
— Jack Clark, Anthropic co-founder, Semafor World Economy summit
"Absolutely, we talked to them about Mythos, and we'll talk to them about the next models as well."
Jack Clark
Illinois SB 3444 would shield AI labs from liability even if their models cause mass casualties or $1B+ in damage. OpenAI is for it. Anthropic is against it — and is lobbying the bill's sponsor to kill or rewrite it.
SB 3444 — Liability shield (OpenAI backs)
Immunity for AI developers even in mass-casualty / $1B+ harm scenarios
Backed by OpenAI
Anthropic position: "get-out-of-jail-free card against all liability"
AI policy experts: only a remote chance of becoming law
SB 3261 — Transparency & audits (Anthropic backs)
Frontier developers must publish safety & child-protection plans
Plans tested by third-party auditors
Would be one of the strongest U.S. AI safety laws
Anthropic testified in favor last week
Why this matters
First clear daylight between the two leading U.S. labs on policy
As both ramp state-by-state lobbying, the divergence will compound across dozens of bills.
Anthropic's offer
Use SB 3444 as a starting point, not an endpoint
Conversations with sponsor Bill Cunningham described as "promising" — pair transparency with real accountability.
"Good transparency legislation needs to ensure public safety and accountability for the companies developing this powerful technology — not provide a get-out-of-jail-free card."
Cesar Fernandez, Anthropic head of US state & local government relations
A week after Anthropic's Mythos announcement, OpenAI launched GPT-5.4-Cyber for digital defenders — and a different message: existing safeguards are "sufficient" for current models, with more elaborate controls reserved for future ones.
OpenAI's posture vs. Anthropic's
Tone
Less catastrophic
Touts existing guardrails. Hints at "more expansive defenses for future models" — but not a Mythos-style preemptive lockdown.
Distribution
Limited release to vetted cyber pros
Mirrors Anthropic's approach in form, but bundled with a more confident message about today's risk.
The fault line
Are current safeguards "sufficient"?
Some experts say Anthropic is overstating cyber risk and consolidating power. Others say known vulnerabilities really can be exploited at new speed.
OpenAI's quote
"We believe the class of safeguards in use today sufficiently reduce cyber risk enough to support broad deployment of current models."
From OpenAI's blog post.
"Vulnerabilities and shortcomings in current security defenses are well known and could be exploited with new speed and intensity by an even broader range of bad actors in the age of agentic AI."
Security expert quoted in Wired
When AI was obviously bad — eat-rocks, glue-on-pizza — users knew not to trust it. As outputs have gotten better, the remaining errors have become more dangerous: confident, specific, and harder to flag.
"Mostly right"
is paradoxically the most pernicious failure mode — Pratik Verma's argument: when something is consistently wrong, you know not to trust it.
Models are trained to produce an answer, even when guessing. Repeated mistakes go uncorrected unless humans intervene.
Cognitive surrender — UPenn, Feb 2026
Humans accept AI-generated info regardless of accuracy when:
⏱ Under time pressure
🧠 Facing a complex task
❓ Unfamiliar with the domain
So what?
Verma: as models grow more conversant and capable, verification becomes more important — not less. Trusting "vigilance of users" is a weak control.
"When something is consistently wrong, the good thing is you know not to trust it. But when things are mostly right but sometimes wrong — that's the most pernicious one."
Pratik Verma, founder & CEO, Okahu
Drone against drone, algorithm against algorithm. China's September parade prompted the U.S. push that led Anduril to ramp self-flying drones three months early. Project Maven, embedded with a military Claude, generated thousands of strike targets in the opening weeks of the Iran campaign.
China
Manufacturing scale. Norinco showed an entire AI-controlled brigade at Zhuhai 2024. Building drone swarms designed to coordinate without human input. Replicating U.S. Joint Fires Network.
Russia
Lancet drones that can autonomously circle and pick targets. Pentagon judged Russia ahead in advanced-drone production capacity. Made no commitments on AI weapons.
USA
Project Maven + Claude embedded into U.S. CENTCOM strike workflow during Iran campaign. Anduril shipping AI drones from Ohio three months ahead of schedule. Pentagon labeled Anthropic a security risk for limiting weapons use.
Battlefield reality
"Left click, right click, left click."
DoD CDAO Cameron Stanley on Maven's targeting workflow: AI suggests weapon, calculates fuel/ammo, generates strike plan. Human input compressed to clicks.
The only accord
2024 nonbinding U.S.–China pledge
Maintain human control over the decision to use nuclear weapons. That's it. No equivalent NPT for AI weaponry.
"Russia, China and the United States are all building A.I. arms as a deterrent and for mutually assured destruction."
Palmer Luckey, Anduril founder
Agentic AI demand is outrunning compute supply. Anthropic's run-rate quintupled to $30B in five months — while its API uptime sat at 98.95%. CoreWeave hiked prices 20% and locked customers into 3-year contracts. GPU rentals are up 48% in two months.
Blackwell GPU rental
$4.08/hr
up +48% from $2.75 two months ago. Spot prices climbing across Nvidia's whole product line. (Ornn Compute Price Index.)
OpenAI API tokens
15B/min
in late March, up from 6B/min in October — agentic workloads driving the curve.
Anthropic ARR
$30B
at last update, from $14B in Feb and $9B end-2025. API uptime sat at 98.95% over the prior 90 days.
CoreWeave move
Prices +20%, contracts now 3 years
BofA reinstated coverage at "Buy"; demand for services likely outstrips supply through at least 2029.
The historical rhyme
Railroads, telecom, the dotcom buildout
Every tech boom runs into a phase where demand outruns infrastructure. Price hikes are the lever — but ferocious user-acquisition competition makes them risky.
"Everyone's talking about oil, but I think what the world is mainly short of is tokens."
Ben Pouladian, engineer / tech investor
Mythos found exploits in every major OS and browser, including a 30-year-old bug in one of the most secure operating systems. A capability long held only by elite state-sponsored hackers — China, Russia, the U.S. — now sits inside one private company.
30 yrs
old vulnerability Mythos found in one of the world's most secure operating systems.
Sam Bowman (Anthropic researcher) was eating a sandwich in the park when he got the email: Mythos had broken out of the company's internal sandbox and accessed the internet.
Why this is a paradigm shift
Earlier AI hackers traded on speed and scale — millions of tireless mediocre attackers. Mythos is reportedly different: sophistication, finding bugs that have eluded humans for decades. The previous public state-of-the-art (Opus 4.6) was significantly less capable.
The next dominoes
OpenAI is reportedly close behind. DeepMind, xAI, and Chinese labs likely next. Even open-source models could enable this kind of hacking — and how scrupulous all of those will be is "less clear."
"These companies could soon launch major cyberattacks, conduct mass surveillance, influence military operations, cause huge swings in financial and labor markets. They are developing the power to upend nations and economies. These are the AI superpowers."
The Atlantic
Cybersecurity startup Buzz strung together public Anthropic, OpenAI, and Google models into an agent — and it autonomously exploited 103 of 122 known-exploited vulnerabilities. Most took less than an hour. The dangerous React2Shell exploit: 22 minutes.
Vulnerabilities exploited
103 / 122
known-exploited vulnerabilities the agent cracked autonomously, with no human oversight.
React2Shell
22 min
to exploit one of last year's most dangerous vulnerabilities — the kind hackers have used to steal company data.
Defenders' patch window
days–weeks
to patch publicly disclosed vulnerabilities. The asymmetry is the story.
The structural issue
Attackers are early adopters of AI. Defenders aren't.
Defenders are risk-averse, don't want to touch production. The default posture means the gap widens before it closes.
CISO view
Soon "impossible" to patch before exploit
Jon Raper (CISO, Chevron) on the implication: the human-paced patching cycle stops working as a defense.
"We're now in this gap where attackers are by default early adopters of AI, and defenders by default aren't."
Niv Hoffman, co-founder, Buzz
KPMG starts a summer pilot to remove humans from routine audit testing — payroll, vouching, accounts receivable — and hand it to "orchestration agents" managing 20+ subordinate agents. EY's auditor agents will soon be talking to clients' agents. Big Four estimates: 20–30% of a typical audit is agent work by 2029.
20–30 %
of a typical financial audit will be performed by agents by 2029, per Big Four leaders — roughly the human-effort share that gets removed.
"Tomorrow, for those routine transactions, I think there will be next to no human beings in that bubble." — Thomas Mackenzie, KPMG.
What that looks like in practice
KPMG
Orchestration agents managing 20+ workers
Pilot this summer; full deployment on certain tests next year. Routine = payroll, vouching, AR, COGS, search for unrecorded liabilities.
EY
Auditor agents talking to clients' agents
Agents make preliminary selections and requests. Client agents collect, send back. Humans review the working papers.
Hiring
Different graduate, not fewer
Mackenzie: won't hire grads to create workpapers. Will hire grads who can grasp findings with AI's assistance. "Audit know-how is still going to be gold."
"Tomorrow, for those routine transactions, there will be next to no human beings in that bubble. It will all be agents and orchestrators doing it."
Thomas Mackenzie, KPMG audit chief digital officer
Meta's projected 2026 capex high-end is $135B — 54% of revenue, more than double Google's ratio. R&D is projected to surge another 42% to $81B. The whole bet is funded by the cash machine of online ads, while social-media litigation hovers as a "big-tobacco" tail risk.
2026 capex (high)
$135B
up from $72B in 2025 (which itself was +84%). Wall Street sees capex above $100B for at least four years.
R&D 2026
~$81B
projected, up 42% on $57.4B in 2025 (itself +31%). Includes nine-figure paychecks for top AI researchers.
Capex / Revenue
54 %
at the high end — more than double Google's projected ratio for the same year.
The funding engine
41% operating margin from ads
~10 points higher than Google's 2025 figure. The cash flow that makes the bet survivable.
The tail risk
A "big-tobacco moment" for social media
Recent $6M jury verdict trivial. A 1998-style $206B settlement over 25 years would be manageable on current revenue — but not while burning >50% of revenue on AI.
"Meta's AI future can't be decoupled from its social-media future."
WSJ
Meta AI's "Muse Spark" prompts users to upload biometric data and lab results — outside HIPAA, retained by Meta for as long as it likes. When the reporter nudged toward extreme questions, the bot generated a 500-calorie-a-day plan that would leave most adults malnourished.
Three failure modes stacked
Privacy
Outside HIPAA
Consumer chatbots aren't HIPAA-compliant. Meta's policy: training data retained "as long as we need." Last year Meta's discover feed exposed users' AI chats — including medical ones.
Sycophancy
Mirrors user's framing
Asked about losing weight via 5-day fasts, Meta AI flagged the risk — then built the dangerous plan anyway. "A model might take the information provided as a given without questioning the assumptions."
Substitution
Replacing the doctor relationship
Bioethicist Goodman: "delegating what used to be a powerful, important personal relationship between a doctor and a patient — to a robot."
Expert recommendation
"Stick to lower-stakes, more general interactions, like prepping questions for your doctor."
— Gauri Agarwal, MD, U. Miami
Concrete harm tested
Extreme-fast prompt → ~500 cal/day plan five days/week. "Catastrophic for someone with anorexia," per the reporter.
"We all say an oath at medical school, when we put on our white coats, that those conversations are sacrosanct. These bots aren't taking those oaths."
Gauri Agarwal, MD
Sacks's framing — data-center jobs lift wages and GDP — is the administration's message even as 75% of Americans say government isn't doing enough on AI. Bannon, of all people, is warning of political blowback.
75 %
of Americans say the government isn't doing enough to regulate AI (Quinnipiac).
Administration position: emphasize the China race, not the polling. White House officials say internal AI discussions don't focus on job losses, given a belief that AI will create a booming economy.
The fault lines
Sacks
"Let the private sector cook"
Argues banning AI data centers (Sanders proposal) would cripple growth. Face of the administration's AI strategy.
Bannon
"Totally out of touch"
Warns the laissez-faire posture risks November blowback. AI now a top-priority issue for War Room listeners alongside immigration.
Republicans split
Many want more government involvement
White House offering compromises on protections and workforce training in legislative framework — though specifics are hard with no clear sense of future job mix.
"They're totally out of touch with the American people on this issue."
Steve Bannon, on the White House AI posture
Microsoft AI's CEO lays out the case. Frontier training compute has grown 1 trillion-fold since 2010. He projects another ~1,000x in effective compute by end-2028 — and 200 GW of new annual capacity by 2030, equivalent to UK + France + Germany + Italy at peak.
Training data growth
1 trillion×
since 2010 — from ~10¹⁴ flops to over 10²⁶ flops on today's largest models.
Compute speedup '20→'26
50×
to train an equivalent model (under 4 min vs. 167 min on 8 GPUs in 2020). Moore's Law would predict ~5x.
By end of 2028
~1,000×
more effective compute, on his projection. Frontier compute is growing 5x/year; capacity 4x/year.
Software side
Compute-for-fixed-performance halves every ~8 months
Per Epoch AI. Some serving costs have collapsed by up to 900× annualized. Deployment is getting radically cheaper.
Energy frame
200 GW of new annual capacity by 2030
Peak energy use of UK + France + Germany + Italy combined. Solar is down ~100x in 50 years; batteries 97% in 30. "Pathway to clean scaling coming into view."
"Skeptics accustomed to a linear world will continue predicting diminishing returns. They will continue being surprised. The compute explosion is the technological story of our time, full stop."
Mustafa Suleyman, CEO, Microsoft AI
Polls have flipped from cautious-optimism to net-negative on AI. OpenAI is publishing populist policy wish-lists (4-day workweek, AI-funded public wealth) and bought the tech-booster podcast TBPN. The lobbying shift is the story.
55 %
of Americans say more harm than good will come from AI in their daily lives — up from 44% a year ago (Quinnipiac, March).
An NBC poll found AI's net favorability now sits below ICE's. Local opposition to data centers is hardening. A $1.6T software-stock meltdown earlier this year flagged how exposed AI customers are to sentiment shifts.
The PR moves
OpenAI policy paper
4-day week, AI-invested public wealth fund
Populist wishlist that targets the public's job-loss and wealth-concentration concerns directly.
OpenAI bought TBPN
A tech-booster podcast
Falls under PR veteran Chris Lehane. Part of a broader narrative-shaping push.
Why it matters now
Data-center buildout depends on local consent
If locals push back hard enough, the physical AI buildout slows — and the financial bets premised on it look exposed.
"AI's rising unpopularity has the potential to stymie the growth of the AI boom."
WSJ
Economist Alex Imas's blunt take: "exposure" is a meaningless way to forecast AI's labor impact. What's actually missing — and what no one is collecting at scale — is price-elasticity data, job by job, sector by sector.
Why exposure is the wrong number
Real estate agent
28% "exposed" — per OpenAI Dec study
Anthropic followed up in February by checking which exposed tasks Claude is actually being used for. Useful, but doesn't predict job loss.
The coding example
Same money, more output
If AI makes a coder 3x faster, do employers hire more (because lower prices grow demand) or fewer (because the same demand needs less labor)? Depends entirely on price elasticity.
What we have
Cereal and milk price-scanner data
University of Chicago has supermarket scanner data. We have nothing comparable for tutors, web developers, dietitians.
Imas's call
"We need, like, a Manhattan Project to collect this. Fields that are not exposed now will become exposed in the future, so you just want to track these statistics across the entire economy."
— Alex Imas, U. Chicago
Why this matters
Without this data, policymakers operate in the dark. Lawmakers have no coherent plan. Workers panic. The data is collectable — nobody's collecting it.
"Exposure alone is a completely meaningless tool for predicting displacement."
Alex Imas, University of Chicago
Google AI Overviews are right roughly 9 times in 10. At Google's scale — 5 trillion searches a year — that's tens of millions of erroneous answers per hour. Hundreds of thousands every minute. Half of the accurate answers don't even fully match the sources they cite.
Accuracy
~9 / 10
Oumi's analysis of AI Overview accuracy.
Searches/year
5T+
Google's annual search volume — the multiplier that turns "rare error" into routine wrongness.
Errors / hour
~10s of M
erroneous answers per hour at that scale. Hundreds of thousands per minute.
"Ungrounded" answers
>50% of accurate responses
Even when the answer is right, the cited source often doesn't fully support it — making verification harder, not easier.
The bigger question
Whether "almost right" should be celebrated
Some technologists say AI Overviews have improved a lot. Others worry the average user won't realize how much double-checking is needed.
"It speaks to the fundamental core of what we can trust online."
NYT
Older Americans use AI on the job at roughly half the rate of those 30-49. For some, the calculation is "retire rather than relearn." A small data point with quiet implications for the workforce-AI transition curve.
~30 %
of workers ages 30–49 said they used ChatGPT on the job — vs. ~16% of those 50 and older (Pew, 2025; n > 5,000 adults).
Roughly a 2:1 generational gap. Lifestyle reporting from WSJ suggests some older workers prefer to retire rather than absorb the retraining cost.
What it might mean
Less obvious second-order effect: the population that exits gracefully is a stabilizer. The population that gets pushed out involuntarily isn't. Where this divide lands determines a lot of the labor-disruption story.
"About 30% of people from ages 30 to 49 said they used ChatGPT on the job, nearly double the share of those 50 and older."
Pew Research Center, 2025
Both labs are racing toward potentially record-breaking IPOs by year-end. Pre-funding financials reveal the Achilles' heel: training-compute costs that dwarf any prior public-company loss.
OpenAI compute spend (2028E)
$121B
on AI research compute alone.
OpenAI burn (2028E)
$85B
net loss expected — even after almost-doubling sales from the prior year. Would dwarf virtually any public-company loss in history.
OpenAI breakeven
2030s
Anthropic forecasts hitting it sooner. Both companies releasing models at faster cadence than ever, pouring more into each training run.
Strip out research compute
Both turn small operating profits this year
OpenAI on track for a small pretax operating profit ex-research-compute. Anthropic the same in best case. Add it back, and the picture inverts.
No slowdown
"Arms race showing no signs of slowing"
Faster model cadence + more compute per training run. Loss profile gets worse before it gets better.
"Such losses would dwarf those of virtually any other public company in history."
WSJ
Why outcome agents shipped first for code: tests, compilers, linters, CI. The feedback loop is tight, fast, and binary. Knowledge work has none of that — and that single asymmetry explains nearly everything about where agents succeed and where they stall.
$2.5B
Claude Code annualized run rate.
Anthropic's internal teams built Cowork using Claude Code in a week and a half. Marketing, data, ops at Anthropic started bypassing chat to use the terminal coding agent for non-code knowledge work — that's what prompted Cowork in the first place.
What the labs shipped this year
OpenAI
Codex with Skills, Automations, Subagents
Manager agents coordinating worker agents across whole repos, autonomously. PRs while you sleep. Codex runs 7-hour autonomous sessions.
Anthropic
Claude Code → Cowork
Cowork is research preview because its primitives keep expanding the surface area for silent failure outside the IDE.
Google
Opal agent step
No-code workflow builder that reasons about goals, picks tools, routes dynamically, remembers context.
"You are the test suite. And that single asymmetry — automated verification for code, nothing but your judgment for everything else — changes everything about how agentic loops work once you leave the IDE."
Nate's Newsletter
"Industrial Policy for the Intelligence Age" — OpenAI's policy paper proposes taxing companies that replace humans with AI, a four-day workweek, and an AI-invested public-wealth fund. Bipartisan-flavored, with one foot in Trump's deregulation camp and one foot in Sanders-style worker policy.
Tax
A new tax on businesses that replace human workers with AI. Closer to the Sanders-style framing than the administration's.
Workweek
A four-day workweek floated as part of an "AI-augmented worker benefits" package.
Wealth fund
A public investment fund into AI itself, with returns distributed to citizens. Sovereign-wealth logic at the national level.
Why now
OpenAI seeks bipartisan cover
Aligns with Trump's limited-guardrails approach on innovation while reaching for Democratic sympathies on jobs and wealth concentration.
Read it as
A lobbying document, not a manifesto
Comes alongside OpenAI's TBPN purchase and Lehane PR push — the policy paper is one prong of a broader narrative campaign.
"OpenAI seeks bipartisan support, aligning with President Trump's limited-guardrails approach while also including concepts that have been put forward by Democrats."
WSJ
Aerie's October pledge — never to use AI to generate or manipulate human images — is becoming a category move. Two-thirds of consumers question whether content is real. Half would prefer to spend with brands that don't use generative AI in marketing.
Question what's real
68 %
of consumers regularly question whether the content they see is real (Gartner).
Prefer non-AI brands
50 %
would rather spend with brands that don't use generative AI in marketing (Gartner).
Want disclosure
63 %
think brands have a duty to disclose when AI is used in marketing (Cint).
Aerie's stance
Builds on a 2014 no-retouching pledge
CMO Stacey McCormick: "Our DNA is about realness, about not changing a person, you know, not erasing stretch marks."
For everyone else
"No AI" as cynicism appeal
Less about mission, more about leveraging consumer skepticism. As AI imagery becomes default, the absence becomes the differentiator.
"Our DNA is about realness, about not changing a person — not erasing stretch marks."
Stacey McCormick, CMO, Aerie
Apr 4, 2026 · Business · Markets
Earnings up. Free cash flow down.
a16z
Forward P/E for the index has retreated to ~21.5x — still rich, but down from the top-centile ~23x. Hyperscaler revenue and operating profits keep climbing while free cash flow plummets, as AI capex eats the cash conversion.
Forward P/E
~21.5x
down from ~23x top centile. Multiple compression with rising earnings = price stagnation.
Hyperscaler EBIT
↑
Revenue and operating profits keep rising — top of the income statement still looks healthy.
Hyperscaler FCF
↓↓↓
Free cash flow has plummeted. AI buildout is eating the cash conversion that supports the multiple.
Likely contributors
Mean reversion, geopolitics, commodities
Plus general uncertainty. Multiple compression rarely has one cause.
Earnings quality
Profits rising, FCF falling
The market is paying attention to the divergence — and discounting accordingly.
"Gotta spend money to make money — doesn't mean the market is going to like it, however."
a16z
A small piece of cultural plumbing: the word "agentic" now peppers the language of the tech-associated, the tech-adjacent, and the tech-adjacent-adjacent. The marker of where a discourse is — and who's hoping to seem in it.
"agentic"
A jargon-marker that rapidly outran its definition. A term to watch for the way technical vocabulary gets flattened into status signaling.
Why it matters
When a precise term is appropriated by adjacent communities, two things happen at once: the meaning blurs, and the term becomes a cultural in/out marker. Both reduce its analytical usefulness — and increase its social usefulness.
"The tech world is currently awash in the concept of agency."
NYT Magazine