Tala is a company you are not paying enough attention to Unsecured onchain credit. Embedded in wallets. $10bn lent. Exceptional risk adjusted results in "high risk" markets

5/ Fix two is the superpower. The models that ate junior tasks also collapsed the cost of learning and experimentation. Explore more fields. Test more ideas. Discover what you’re unusually good at, then use AI to do more with it. Superpowers, if you deliberately use them.

Safer, more reliable AI requires closing the measurability gap between what humans care about and what agents can measure. If our preferences and context don't shape what agents optimize, being "in the loop" is just rubber-stamping.

At this moment in Q4 2026 it's net good for cyber-defense that we have open weights models that are good at dual use cybersecurity tasks. - If there's been an attacker productivity bump due to AI 2022-26 it's small and close to being in the statistical noise - In contrast, there's a ton of measureable progress in defenders using models to find and fix bugs in highly leveraged codebases - Many defenders have struggled to get access to the best unguardrailed closed weights cyber models - ... and when they do the tokens are dramatically more expensive than open weights tokens at the same cyber intelligence level; CISO orgs are cost centers and these costs are often prohibitive - Ultimately we'll need open weights models on premise on air gapped networks and on small devices where they can detect breaches and attacker implants - We'll also need open weights in situations where defenders can't send secret data to the closed weights model providers (who have security problems of their own!) As long as these conditions persist, we should be pushing for diffusion of frontier dual use open weights cyber capabilities, because they net benefit defenders.

1/ Once you see that verifying AI’s work is the bottleneck, and that checking runs on tacit knowledge, you can predict the hiring shift before seeing the data. We did, back in February. Anthropic’s junior share of technical hires: 27% to 9%.

6/ These dynamics are visible inside @Anthropic than in aggregate labor data. Why? Diffusion. The lab is the extreme case of adoption. The broader economy still has to reorganize around AI, as with other general purpose technologies. That lag is our window to act!

Equilibrium final exam question, though: if working with agentic AI is a form of management (it is), hence requires higher skill (it does), but the distribution of experience/skill in the economy is fixed in the SR, what happens to the *distribution* of hires across firms?

2/ Output is priced in tokens. Verification is priced in human hours. Anthropic estimates that reading a week of Claude output would take 424 hours per person! A week has 168. That’s the verification constraint. And reading is the easy part!

3/ Junior employment grew more slowly where Claude handled more complex tasks. Our model’s Missing Junior Loop: firms need top expertise while automating the work that produces it. They’re hiring from a stock they’re doing less to replenish.

OpenCode is currently under a sustained cyberattack, and mid-incident they ran into the same problem Hugging Face did: the frontier models they were using for defense began refusing to help fight attacking agents because analyzing the attacks started triggering safety guardrails.

Railroads had their own @DarioAmodei: Tom Scott, the "Pennsylvania Napoleon." Standard Oil was most of his traffic. Scott moved into refining to capture value (labs and pharma?). After the price war the Pennsylvania paid Rockefeller 20c a barrel, even on his rivals' crude (!).

some recent reads: 1/ catalini.com/writing/ai-veri… > “taste” and “judgement” are vague, @ccatalini offers verification as a sharper frame: verification checks whether AI’s output reflects human context and intent; if we outsource or skip it, we transfer power to agents 2/ nitter.siddz.com/saranormous/status/210… > agents will break business models built on customer inertia and inattention by just acting - what takes their place? 3/ creativeconvictionpress.subs… > fave newsletter on why tech needs creativity @thisisrayouf and @Libbiefrost 4/ ai-perez-curve.vercel.app/ > as inference gets cheaper, pricing power shifts from intelligence and deployment to context and distribution (thank you for the rec @AkhilRamgopal!)

So how do you survive the moatpocalypse? Start from one observation: anything that can be measured will be automated.