Teamwork Makes the (AI) Dream Work
AI is the life-and-shift of value from labor to capital
On January 30th, software stocks went into a ‘death spiral.’ (aka the SaaSpocalpse) The same day we had our celebration of 30 years of NYC tech innovation, technology stocks cratered, on news of coming innovation (if you believe the next day’s headlines, which largely blamed Anthropic’s Cowork launch, and specifically the industry plugins for sales, finance, data, marketing, and legal that dropped on Friday.) Markets looked at SaaS margins and asked a reasonable question: if an AI agent can do workflow automation better than your $50/seat software, what’s your moat? Investors didn’t wait for an answer. They sold. Software sector down 8.3% in a single session. (I want to add “This was not a bear trap” but that feels like such an LLM thing to say.)
OpenAI countered with Frontier, their own ‘co-worker’ solution integrated with ChatGPT Enterprise. While both companies are positioning these as colleagues rather than assistants, the semantic difference matters operationally. An assistant helps you do your job. A co-worker does parts of your job autonomously, which means different trust requirements, different observability needs, and different compliance implications. Also: different implications for how many of you there are next year.
Per usual, the panic may have been overblown (Cowork plugins aren’t replacing your entire go-to-market stack next quarter or even this year) but the market reaction forced the question into every board meeting: if AI can do this work, why are we paying for software that does it worse? Your CFO expects an answer. (He knows that it costs Salesforce just $38 to support each $300 seat.) Your board wants to know why you’re not replacing tools with agents. And your compliance team wants to know how you’re governing autonomous systems with write access.
For CTOs, the implication is straightforward: you need measurement infrastructure before you need virtual co-workers
Except the macro explanation is sitting in the corner quietly munching peanuts. Look at the VIX. Look at the S&P outside just tech stocks. Trump tariffs. Metals chaos. The software wipeout might have been triggered by Cowork, but the underlying conditions were already there. What Anthropic did was provide a narrative for selling pressure that was looking for an excuse. Almost like AI providing a narrative for the downsizing your C-suite wanted to do anyway.
Which brings us to the real question CTOs need to answer: is this actually about productivity, or is it just another excuse for headcount reduction? Because so far, AI has mostly been the latter. The dream everyone’s selling is “10× your team.” The operational reality we’re discovering is messier and more asymmetric. AI makes junior people faster while slowing down senior people who spend their time reviewing a deluge of AI output instead of writing code. With not much time left over for building better harnesses.
Capital Eats First
AI is a mechanism for converting labor into liquidity.
The most serious attempt at a structural analysis of where this goes was Monday’s Citrini Research memo—framed as a post-mortem from the future—which does something important and something frustrating in roughly equal measure. (Hence its virality.) The important thing IMO is that it names the distributional dynamic clearly. AI gains flow to capital, not labor. This is literally what AI is: a lift and shift between these two domains. The thesis, not only correctly, but plainly stated, is essentially a truism. The frustrating thing was that it took a viral memo to put it plainly, because nobody else was willing to say the quiet part out loud.
Let’s say it clearly: AI is a mechanism for converting labor into capital. Every dollar of headcount that becomes a dollar of compute spend shifts value from wages, distributed across workers and recycled through consumption, to capital returns, concentrated among compute owners and shareholders. This should not be a controversial economic observation. It’s what capital-labor substitution means.
Once you see that mechanism clearly, most ‘agentic’ use cases stop looking distinct. You don’t need a macroeconomic model to see it (arguably why the Citrini piece has none) and you don’t need Dromology to understand it. You just need to look at the market data. Google hitting $4 trillion market cap. IBM down 13% on the Anthropic COBOL announcement.
Claude Code can now analyze thousands of lines of legacy COBOL, map dependencies, document workflows, trace execution paths, identify risks; work that typically takes human teams months. Ninety percent of the world’s active financial transactions run on COBOL. The expertise pool maintaining those systems is aging out. IBM’s entire services business was built on the assumption that modernizing this infrastructure required expensive human specialists indefinitely. One announcement repriced that assumption by 13% in a single session, the largest single-day drop for IBM since before the dot-com crash.
A 13% drop on a blog post for a tool that hasn’t completed a single production COBOL migration yet is also a perfectly good illustration of markets pricing possibility as certainty. The intersection of hope and hyperstition? Bet.
Missing Macro
A risk map dressed in the grammar of a post-mortem
Which is exactly the problem with the Citrini piece: the missing macroeconomic model. The cascade (software defaults by mid-2027, mortgage stress by 2028, S&P down 38%) arrives with Bloomberg-headline confidence and no transmission mechanism. How fast do displaced workers exhaust savings? What’s the policy response function? At what threshold does private credit stress propagate to mortgages? Ghost GDP is real, but when you consider that the top 10% of earners drive more than half of consumer discretionary spending, K-shaped recovery starts to make a lot more sense.
So it’s more like a risk map dressed in the grammar of a post-mortem. But directional confidence with no basis for magnitude and timing isn’t real economic analysis, it’s provocation. And not coincidentally the hot topic at every board meeting this week.
Which brings us to what nobody in your board meeting will say out loud: AI agents aren’t “productivity tools” in any humanistic sense. Strip away the product marketing and every scaled, deployed, actually-working use case is a variation on the same function: moving money from A to B with fewer humans taking a cut in the middle. Agentic commerce. Automated procurement. Contract analysis. Insurance re-shopping. Workflow automation. These aren’t different products. They’re the same product: wealth transfer, made more efficient. This is, incidentally, exactly what blockchain spent a decade promising to do. The difference is that blockchain mostly failed to scale. AI isn’t failing to scale. (Though some would argue it’s scaling to fail.)
So the teamwork isn’t really between you and your co-worker, it’s between capital and the tools it just acquired to replace labor more efficiently. That’s the dream that’s being operationalized. That’s the dream CTOs have signed up to build, whether we’re comfortable admitting it or not.
Bottom Line
For CTOs, the more concrete implication is straightforward: we need measurement infrastructure before we need virtual co-workers. If we can’t measure what your human developers are actually producing versus what they feel like they’re producing, adding AI agents just multiplies the confusion. And when the CFO asks for ROI numbers on Cowork versus human headcount, you need to hand him numbers that reflect your company’s value chain, and not internal engineering metrics.
The software stock wipeout is a forcing function, but not the one all the GPT ghost written posts on LinkedIn are telling you it is. It’s not forcing you to adopt AI co-workers. In fact, it’s not forcing you to do anything. The choice before you, as CTO, is whether to understand your own value chain well enough to know the difference between real output and a cost relocation, or to keep repricing the question until the market answers it for you, somewhat drastically.
