Briefs

The WSJ is calling it Phase 2 — the shift from chatbots that answer questions to agents that actually do work. Autonomous, multi-hour, no babysitting required. Claude Code, Cursor, Codex. "Vibe coding." Anyone with a laptop and a clear enough prompt can now build software.

The numbers back it up. Cursor hit $2B ARR at a $29.3B valuation. Claude Code is generating $2.5B ARR. Codex traffic grew 8x in two months. VC Tomasz Tunguz puts the near-term consumer agent market at $36B — with enterprise as an order of magnitude larger behind it.

The more interesting story in the article is who's actually building. Not just developers. A cardiologist built a patient navigation app. A lawyer automated building permit approvals. A dentist with no engineering background built practice management tools. The barrier to building software has effectively collapsed.

Here's what it doesn't say: deploying agents is the easy part. The hard part is what you deploy them into. Most organizations sit on fragmented data, siloed systems, and processes that were never designed to be automated. You can spin up 50 agents tomorrow — but without a coherent data strategy and an architectural layer that ties them to the right information at the right time, you get 50 expensive experiments, not a transformed operation.

The enterprises that will actually realize value from this wave aren't the ones that deploy the most agents — they're the ones that get the underlying architecture right: data accessible, processes rationalized, an integration layer that reflects how the organization actually operates. Most companies have started down this path. The challenge is doing it well — which requires a rare combination: deep understanding of the business, fluency in enterprise tech, hands-on experience with agentic AI, and the ability to drive alignment across functions and teams. Without that combination, even well-resourced efforts stall. It's also where I spend most of my time.

The trillion dollar opportunity is real. Getting there is hard — and the bottleneck isn't the technology.

The WSJ declared this week "The Week the Dread AI Jobs Wipeout Got Real." The proximate cause: Block — Jack Dorsey's fintech company — laid off roughly 1,000 people, and Dorsey was explicit that AI productivity improvements were a driver. The market responded predictably: the stock went up. Employees responded predictably too: with anxiety, anger, and a lot of posts on X.

What's notable isn't the layoffs themselves — companies restructure. What's notable is the explicitness. Dorsey named AI as the reason. That clarity may have helped the stock, but it did something else: it gave workers everywhere a concrete data point to file next to their own uncertainty. That's what distrust looks like before it becomes resistance.

The rational response for any remaining employee is to slow-roll AI adoption, hide productivity gains, and protect themselves. Not because they're bad actors, but because that's what rational people do when they don't trust the incentives. This is the other side of the coin from the 50% of workers quietly using AI and not telling their employers — when the implicit deal becomes visible, people protect themselves.

This tension can't be managed away with messaging. It has to be managed with actual design — and honesty. The organizations that get this right will be the ones that can credibly answer: what's in it for the people being asked to change? In a growing company, the answer can be genuine: we're expanding capacity, not shrinking headcount, and your productivity gains translate into leverage, scope, and growth. In a cost-cutting context, it's harder — but trying to make that case dishonestly is worse. Employees will sense it, trust will erode, and the productivity you're trying to unlock won't materialize.

The transition is real. The tension is real. You can't manage it by pretending the tension isn't there — you have to name it, and build toward outcomes people can actually believe in.

A research piece by Citrini Research went viral this week — 1,700 likes, 351 restacks on Substack, enough to spook markets on Monday (Dow -1.7%). The WSJ picked it up. Here's what it actually says.

The piece is a thought exercise, explicitly not a prediction. It's written as a June 2028 macro memo looking back at how things unraveled. The scenario: in late 2025, agentic coding tools took a step-function jump in capability. By mid-2026, enterprise procurement teams had seen enough demos to ask a dangerous question: "What if we just built this ourselves?" The long-tail of SaaS started collapsing. Companies most threatened by AI became AI's most aggressive adopters — not out of vision, but survival. Margins expanded. Stocks rallied. Profits funneled back into AI compute.

The problem was what happened next. White-collar workers who lost jobs didn't go back to spending. The consumer economy — 70% of GDP — withered. What the paper calls "Ghost GDP" emerged: productivity gains that showed up in national accounts but never circulated through the real economy. A negative feedback loop with no natural brake.

The paper closes: "But you're not reading this in June 2028. You're reading it in February 2026. The S&P is near all-time highs. The negative feedback loops have not begun... As a society, we still have time to be proactive. The canary is still alive."

This scenario is plausible — and the uncomfortable truth is that most people in a position to do something about it aren't saying so out loud. The window to act is now, while the feedback loops are still theoretical. For organizations adopting AI, the question isn't just "how do we get more efficient?" It's "how do we do this in a way that doesn't hollow out the consumer base our business depends on?"

The canary is still alive. That's the window.

The WSJ reported this week that selling specialized AI software has gotten harder. Enterprise companies are slowing down purchase decisions, pulling Finance and Legal into evaluations earlier, and asking sharper ROI questions. The underlying concern: if frontier models keep advancing, will the specialized tool they buy today still be relevant in two years?

It's a reasonable worry — and it's reshaping the market in real time. The tools getting scrutinized are point solutions built for a specific task. Frontier models (Claude, GPT, Gemini) sit in a different category: they're increasingly the reason those point solutions are at risk. Buying a narrow AI tool when a general-purpose frontier model might subsume its functionality within 18 months is a real strategic risk.

What this means for enterprise buyers right now:

• Slow down on specialized purchases. If a vendor's core value proposition is something a frontier model can already do reasonably well — and will do better next year — the TCO math changes fast.
• Think in layers, not tools. The durable investments are in the data, workflows, and integrations you control. The AI layer on top will evolve regardless of which model or vendor you pick today.
• Get Finance and Legal in early. Not to slow things down — to make adoption stick. Deals that skip those conversations tend to stall at procurement or unravel during contract review.

The AI market is maturing. The companies that navigate this well won't be the ones that buy the most — they'll be the ones that buy the right things, in the right sequence, with the right stakeholders in the room.

HBR introduced a useful term: workslop — the low-quality, AI-generated output that floods inboxes when organizations mandate AI use without thinking through the implications. The pressure is real: boards want leaner teams, execs feel the push to show AI ROI, and the implicit message is "do more with less."

The authors argue this isn't a people problem — it's a system problem. Their three-level fix:

• Culture: Rebuild trust through actual collaboration — feedback, questions, dialogue. Not just AI outputs.
• Practice: Create clear norms for when/how to use AI, with review processes that reinforce human judgment rather than offload it.
• Accountability: Someone needs to own the AI-human integration — people who understand both the tech and the people.

The irony they point out: to make AI work at work, we need to get better at being human.

Ethan Mollick (Wharton) shared a striking finding on the Prof G podcast: about 50% of American workers are using AI and reporting 3x productivity gains on tasks where they use it. But here's the catch — many of them aren't telling their employers. They're not using corporate AI tools. They're keeping it quiet.

Why? Because if you (and coworkers) prove you're 3x more efficient, you may be part of the next RIF. Often, the 'calculated' move is to pocket the gains and stay under the radar.

This is a real problem, and it won't be solved with mandates or monitoring. It requires honesty: leaders need to make the case — credibly — that AI adoption benefits both the company and the employee. That's easier at growing companies ("we're expanding, let's make you more productive and reward you") than at struggling ones where a RIF feels inevitable.

The adoption gap isn't a training problem alone, it's a trust problem.

MIT's Daron Acemoglu made a point this week that stuck with me: AI that actually helps workers requires deliberate design. It won't happen by default.

Three things that resonated: build domain-specific systems aligned with how experts actually work, design for skill development (not just task completion), and add friction to prevent blind reliance on AI outputs.

This maps to a best practice where you collaborate with AI tools rather than relying on them blindly. The best tools I've worked with don't try to replace judgment — they surface context and let the human decide. The worst ones optimize for "AI did the thing" without asking whether the thing was right.