Andreas Timm

Tag: Workforce

  • MIT Called It a Disenchanted Intern. METR Says Check the Growth Rate.

    MIT Called It a Disenchanted Intern. METR Says Check the Growth Rate.

    Something happened this week that I keep turning over.

    MIT published findings this month showing that when 41 AI models were tested across more than 11,000 real workplace tasks, the result was, in their words, like a “disenchanted intern” — hitting minimum benchmarks about 65% of the time, but never exceeding 50% success on tasks requiring genuinely superior-quality output. If you work in software, marketing, legal services, or knowledge work of any kind, that’s the snapshot.

    METR — a nonprofit focused on measuring AI capabilities — published a different kind of snapshot. Their metric is the “time horizon”: the maximum length of autonomous task a frontier AI can reliably complete. In 2019, the best AI could handle roughly a two-minute task without human intervention. By the end of 2025, that had grown to roughly an hour. The doubling time across that whole period: around seven months.

    METR’s January 2026 update tightened that number further. Post-2023, the best estimate for the doubling period is now 130 days — closer to four months.

    My read on this:

    The MIT study and the METR data aren’t in conflict. They’re measuring different things at different timescales. MIT is taking a photograph. METR is measuring the shutter speed. And the shutter speed is getting faster.

    I don’t think the “disenchanted intern” framing is wrong — it describes today accurately. What I’m less sure about is the assumption, implicit in most of the coverage I’ve read this week, that “today” is a stable state. An intern who gets twice as capable every four months is not the same resource at the end of the year as they are today.

    What I keep returning to is the gap between the current snapshot and the trajectory — and the opportunity that opens up in that gap. The MIT data is a photograph of now. The METR data is the shutter speed. Anyone building workflows, designing teams, or structuring how they work around AI capability today is working from a reference point that will be measurably out of date within a single planning cycle. That’s an opportunity signal at a scale and pace most planning assumptions don’t account for.

    Three things I’m watching:

    1. Where the doubling curve hits friction. Every exponential eventually meets a wall — physical limits, data constraints, regulatory friction. METR’s time-horizon metric is useful precisely because it measures real-world task completion, not synthetic benchmark scores. When the doubling cadence breaks, that will be the signal that the curve has met something real. I expect that to happen. I just don’t know when.

    2. Whether “minimally sufficient” matters or not. MIT’s 65% minimally sufficient rate sounds modest. But most enterprise workflows run on people who are minimally sufficient most of the time. The threshold isn’t excellence — it’s “acceptable at scale, around the clock, at near-zero marginal cost.” That bar is lower than it sounds, and closer than the headline number implies.

    3. The infrastructure spend as an access unlock. Alphabet, Meta, Microsoft, and Amazon are projected to spend nearly $700 billion combined on AI infrastructure in 2026 — roughly double what they spent last year. That capital isn’t just building capacity for the current snapshot. It’s funding the cost compression that makes the next several capability doublings broadly accessible. When the infrastructure matures, the cost floor drops — and the surface area for building on top of it expands with it.

    The disenchanted intern framing is apt today. My expectation is that it’s a better description of 2025 than it is of 2027.

    References

  • Every knowledge worker is a manager now

    Every knowledge worker is a manager now

    Every knowledge worker is a manager now. Agentic AI has turned individual contributors into managers of AI agents, and first-line managers into leaders of managers of agents. The job descriptions have not caught up yet. The operating models have not caught up yet. The reskilling plans have not caught up yet. All of that is lagging the capability frontier by twelve to eighteen months — and the organizations that close that gap first will operate at a structurally different throughput than the ones still writing job descriptions for the jobs that existed in 2023.

    The shift: agentic AI crosses the line from tool to colleague

    For the first year and a half after ChatGPT, the thing called “AI” in most organizations was a better search box. A more patient editor. A faster rough-draft generator. Useful, but still a single-interaction tool. You asked, it answered, you moved on. The job of the knowledge worker did not fundamentally change — they just had a slightly sharper pencil.

    What changed in the eighteen months leading into 2026 is the arrival of agentic models. The word “agent” in that context is not marketing. An agent is a system that can do a sequence of things, hold state across those steps, make decisions about what to do next, use tools, and come back with a completed multi-step task. That is a categorically different interaction than “ask question, get answer.” It is closer to “give a junior colleague an outcome to produce and trust them to produce it.” The commercial consequence of that shift is the subject of this post.

    Knowledge-worker image candidate K02-HC-pipeline: HC2 — INPUT-AGENT-OUTPUT-JUDGE-SHIP pipeline with human at JUDGE
    Input → agent → output → judge → ship. The human stays at the judgment node.

    The role change: ICs become managers of agents

    The individual contributor job has silently changed. Writing short summaries of long content — once a junior-to-mid task — is now an agent task. The human role is to specify the outcome, check the output, and decide what to do with it. Meeting preparation — the pre-meeting brief of background, context, attendees, prior touchpoints — is now an agent task. The human role is to feed the context, review the brief, and adjust the framing. Drafting a first pass of almost any structured document — a proposal, a plan, an analysis — is now an agent task. The human role is the editor, not the author of the first draft.

    The common thread is that the IC’s job has shifted from doing to specifying outcomes and judging output. Those are management skills. Not in the metaphorical sense — in the literal sense. Framing a task clearly enough that someone (or something) else can execute it. Evaluating whether the execution meets the specification. Deciding when to iterate and when to ship. These are exactly the skills that used to distinguish a first-line manager from a senior IC, and they have become baseline requirements for an IC working with agents.

    Knowledge-worker image candidate K03-HC-editor: HC3 — colleagues editing agent outputs + overlay text
    The new role for the IC: editor of agent output.

    The org change: first-line managers become leaders of managers of agents

    If every IC is now a manager of agents, then every first-line manager is now a leader of managers of agents. Their job is no longer to supervise execution — the agent is doing the execution. Their job is to coach the humans on their team in how to specify outcomes, how to judge output, how to know when an agent is producing garbage, and how to scale their orchestration over time. That is a completely different job than the first-line management job of three years ago, and it requires a different skill set.

    Two structural consequences follow. First, the middle management layer compresses because a first-line manager leading managers-of-agents can reach further than one managing direct executors — the coordination overhead per report drops when the reports are themselves operating on a multiplier. Second, the definition of “span of control” stretches, but not infinitely: the Dunbar layers still govern the number of humans a manager can hold relationships with, even if each of those humans is now operating agents underneath them. The org chart can get flatter. It cannot get unbounded.

    Knowledge-worker image candidate K05-WILD-conductor: WILD — human conductor directs an orchestra of AI agents
    One human, many agents — the conductor metaphor for first-line management at scale.

    The strategic consequence: orchestration is now a baseline skill, not an advanced one

    The skill that used to distinguish senior managers from junior ones — the ability to frame work so someone else can execute it and judge whether their execution is good — is now a baseline IC capability. Orchestration is the new baseline. Writing is the new baseline. Judgment about output quality is the new baseline. The organizations that will operate at structurally higher throughput over the next five years are the ones that reskill their IC population around these baseline orchestration skills, rather than hiring more specialists who each do one thing well.

    Talent leverage, not headcount, becomes the scoreboard. A commercial organization that operates at 300 humans with strong orchestration capability can outproduce a commercial organization that operates at 600 humans with legacy IC job descriptions. The difference is not about working harder. It is about operating model. The 300-human organization has fewer Dunbar breakpoints, shorter decision loops, less cross-functional friction, and a higher per-seat agent-multiplier. All of that is the consequence of a single structural decision made at the job-description layer.

    So what boards should do

    Three actions sit on the CEO agenda over the next two quarters. First, rewrite the IC job descriptions for every knowledge-worker role in the organization so that orchestration and output judgment are explicit baseline capabilities, not bonus ones. Second, rewrite the first-line management job description so that coaching for orchestration is the core of the role, not supervision of execution. Third, audit the reskilling plan against the assumption that every knowledge worker in the organization is now a manager and needs to be trained as one — because the capability frontier has already shipped and the only question is whether the organization catches up in quarters or in years.

    Boards that do not require a reskilling plan at this scope are budgeting against an operating model that does not exist anymore. The plan does not need to be perfect. It needs to exist. The gap between organizations that have this plan and organizations that do not is the structural competitive advantage of the next five years, and it is already being measured — in throughput, in decision velocity, in the quiet retention of the top performers who can see the gap coming.

  • AI is becoming the narrative for layoffs. It is not yet the cause.

    AI is becoming the narrative for layoffs. It is not yet the cause.

    What was announced

    Through the week of February 16–22, 2026, the AI-cited layoff story moved from edge case to mainstream framing. AI was cited as the rationale for 4,680 February job cuts in the U.S. — roughly 10% of the month’s total. Baker McKenzie announced 600–1,000 layoffs (up to 10% of global headcount) framed as a pivot to AI-augmented service delivery. Dow disclosed 4,500 cuts in January with explicit AI-strategy framing. A Harvard Business Review piece in the same window argued that companies are laying off based on AI’s potential, not its measured performance. An Oxford Economics report from January concluded that many AI-cited layoffs were the consequence of past overhiring, not present AI productivity.

    What it means

    Two things are happening at once. First, AI productivity is real for specific workflows and starting to show up in unit-cost reductions. Second, “AI” is becoming the public-facing rationale for cost actions that boards and CEOs have wanted to take for other reasons — overhiring during 2021–2022, deteriorating margins in slower-growth segments, restructuring to a target operating model that was already in motion. The two stories overlap, and the public communication does not distinguish between them.

    For employees, the framing matters because “we are restructuring” and “AI is replacing your role” carry different signals about whether the function comes back. For investors, it matters because the market is pricing AI-cited cost reductions as durable while restructuring-cited cost reductions are typically priced as one-off. CEOs who choose the AI framing get a multiple uplift. That incentive structure tells you why the framing is becoming dominant.

    Andreas’s view

    My read on this: the next 12 months will see a steady drift toward AI-as-explanation in layoff communications, regardless of whether AI is the underlying driver. The reason is not deception — it is signaling. CEOs need a forward-looking story that the cost base will stay reduced, and “AI productivity” is a cleaner story than “we hired too aggressively in 2022.” The public record will eventually reconcile this; quarterly earnings will reveal which companies actually shipped the productivity gain and which simply downsized.

    I don’t think the workforce numbers are yet the right metric to watch. The right metric is the ratio of revenue per employee in the months after the cut. If revenue per employee climbs durably, the AI framing was substantively correct. If it plateaus or reverses while operational quality declines, the framing was a positioning move and the company will be hiring back inside 18 months — at higher cost and lower morale.

    The way I see it: when a CEO presents an AI-cited workforce action, the productivity model behind it should be specific enough to name which workflows, which output measures, which time horizon, and which control group. Where those answers are vague, the action is restructuring with AI vocabulary. That is not necessarily wrong, but the distinction matters — and I think it matters most at the board level, where the conversation should reflect what is actually driving the decision.

    Three things I’m watching

    Three things I’m watching as this plays out:

    1. I’ll be watching whether companies maintain a clear internal distinction between AI-driven productivity actions (with a workflow-level model behind them) and AI-framed restructuring actions (justified by other reasons). Both can be valid; conflating them confuses execution, and the ones that keep the distinction clean are more likely to deliver what they promised.
    2. The companies that track revenue per employee monthly for the 12 months following any AI-cited workforce reduction will have the clearest view of whether the productivity gain actually materialized — and I’ll be looking at that number as the most honest signal in the public record.
    3. I’ll be watching how specific companies get in their external communication around AI-related workforce changes. Vague “AI is making us more productive” framing tends to erode credibility internally faster than a precise statement of which work has been automated and which has been redesigned — and over the next year, that credibility gap will start showing up in retention and hiring data.

    References and related signals