9 theses on AI | Sarthak Munshi

1The horizon problem

AI is already good at automating quick tasks: ones where it gets feedback fast and doesn't lose track of what it's doing. METR tracks this: the length of task an AI agent can reliably finish has been doubling roughly every four months through 2025. But even METR says they can't reliably measure anything past 16 hours yet. Nobody actually has good data on long tasks. The real problem with long tasks is what researchers call the credit assignment problem: it's hard for the AI to figure out which of its earlier actions caused something to go right or wrong, especially when feedback is rare. Step count isn't the real limit either. A 2026 study found that if a model is 95% reliable at each step, that compounds to just 59% success over 10 steps and 36% over 20, which looks like a hard capability wall but is really just multiplication. Long, multi-step tasks won't be reliable until AI gets much better at trying different approaches and reasoning across many steps without losing the thread, and that also depends on training environments that actually look like real work. On TheAgentCompany, a benchmark that simulates a real software company, even the best agents only finish 30% of tasks on their own. Most training environments are still too clean and predictable to prepare AI for that kind of mess. Nobody, including the labs shipping these agents, actually knows when long-horizon reliability arrives. Plan around that uncertainty instead of the marketing timeline.

Build for hours, not daysDesign AI workflows around short, verifiable tasks today. Pilot longer autonomous runs in parallel, but don't bet the roadmap on them until the reliability data catches up.

2Job losses vs. new company creation

Money that used to pay workers is shifting toward AI instead, and you can already see the gap between the big picture and the personal one in the data. Stanford's Brynjolfsson-Richardson tracking shows jobs for 22-to-25-year-olds in the most AI-exposed roles shrinking about 3.8% a year, while the job market overall barely moves, down only about 0.2% a year. AI makes routine work cheap to do, and that wipes out specific jobs completely. If it's your job, the good macro numbers don't help you. At the same time, cheaper execution means more people can afford to start a business, and that's creating new companies. Indeed's Hiring Lab says almost the same thing: "the defining labor market challenge ahead is labor reallocation, not creation." They think AI only causes 27–35% of this shift, though, with the rest coming from fewer young workers overall. If your job is repeatable execution, you're exposed, full stop, no matter what the macro numbers say. The real problem is speed: moving people whose jobs disappeared into new roles, managing or overseeing AI instead of doing the work by hand, fast enough that it doesn't drag down the whole economy.

Reskill nowIf your day-to-day is repeatable execution, start moving toward orchestration and oversight before the choice is made for you.

3Specialized AI, not general AI

The future of enterprise AI is small models built for one job, running on a company's own servers. Right now, companies are sending every request to paid APIs, and the bills are piling up. Palo Alto Networks' CEO put it plainly in July 2026: even though the price per AI request has dropped 98%, his company's total AI bill tripled anyway, because AI agents chain together many requests to finish one task, and that eats up the savings. He wants prices to fall another 90% before AI makes sense at real scale. It's not just him: MIT's NANDA initiative studied 300 companies using AI and found 95% saw no real impact on profit, and PwC's 2026 survey of CEOs found almost the same thing, with 56% unable to point to any real benefit yet. That's getting better, not staying stuck: the number of S&P 500 companies that can prove AI is paying off went from 21% to 40% in a year. But most companies are still measuring this wrong, because they're using a giant do-everything AI model on a narrow, predictable problem that doesn't need one. Every company still paying frontier-model rates for a narrow, repeatable task is burning money it doesn't need to burn. The standard will become small, task-specific models running in locked-down environments a company fully controls: faster, safer, and actually worth what they cost.

Divest from general APIsAudit what you're routing through a frontier model's API today. Anything narrow and repeatable should be on a small, owned model within a year.

4Systems skills, not just code

AI can write code now, so just knowing how to write code stops being special. There's already real data on what that's doing to codebases: GitClear looked at 211 million lines of changed code and found the amount getting rewritten or thrown away up 39%, duplicate code up 8x, and actual cleanup work dropping from a quarter of all commits to a tenth. Somebody has to fix...