Developers became 180% more productive with AI. Yet the number of apps people actually use didn’t increase. Both metrics were measured across 100,000 developers, and together they form the most uncomfortable tech data point of 2026. The study comes from the NBER. Researchers cross-referenced actual activity on GitHub with each developer’s AI usage data, isolating the impact of each generation of tools. The surge in code output is staggering: → Code autocomplete: +40% → Conversational agents: +140% → Autonomous agents: +180% Almost three times more code coming from the same person. It looks like the greatest productivity revolution the world has ever seen. But then the researchers asked the question almost no one wanted to ask: How much of that output actually became a product in someone’s hands? When you track what happens to that 180% gain, it drops to +50% when measured by number of projects. When you get to what’s actually launched, only +30% remains. Almost all the gain vanishes along the way. And it gets worse. Look at the Financial Times chart. The line for app launches skyrocketed. Meanwhile, the number of apps with real user adoption stayed flat, and user ratings began to decline. AI has flooded the world with software nobody opens. The greatest production capacity in history is being spent creating things that die on launch day. Why does this happen? Code has always been the easy part of this story. The hard work comes after: reviewing, ensuring it works, deploying it, and convincing someone to use it. AI brutally accelerated just one link in this chain—and exposed just how much everything else still depends on people. What this means for builders going forward is direct: AI has made writing code so cheap it’s become a commodity. Anyone can produce it now—and quickly. What will be worth its weight in gold over the next decade is “taste” and judgment: knowing what deserves to exist before you spend your first hour building it.

we went from: "will AI replace us?" to: "which AI should I use today?" that happened fast.

Behavioral economics, the most powerful academic field attracting attention from top American Ivy League universities like Harvard, Yale, Penn, Cornell, and Columbia, as well as global giants such as Google, Amazon, and Netflix. By scientifically understanding why people act the way they do, you’ll be unstoppable in every situation. There’s only one choice left—learn it.

A good reminder to the AI fanatic developers who say as long as the unit tests, smoke tests, or whatever test pass then the AI slop is fine. Bullcrap! "Tests prove that the code passes the test suite, nothing else. They do not prove by themselves that the code is correct, secure, maintainable, efficient, etc. Those are much harder to measure and have a ton to do with organization, architecture, culture, shared knowledge of the maintainers, etc. All of which is lacking during and after this [AI powered] rewrite [of Bun.]"

Elon Musk believes programming languages will completely disappear this year because AI no longer needs them.

People leaving OpenAI for Anthropic, billions spent on custom chips for AGI Meanwhile the average business still loses leads because nobody replied for 6 hours the edge isn’t the model anymore, it’s who actually plugs it into the work

Boris is naming the shift my own business has been built around for the last few months The old way to scale an agency: hire a junior, train them on your last 10 projects, hope they retain enough that the 11th project doesn’t start from scratch Repeat with a senior to manage the juniors Repeat with a VP to manage the seniors Every layer adds overhead, every layer dilutes the knowledge that lived in the founder’s head. By the time the agency is 30 people deep, the original quality is gone and the founder is doing nothing they trained for The new way: you accumulate the data from every build into a structured library, then you write loops that run against it. Agents looking at past patterns, past objections, past architectural decisions, past failure modes. New client request comes in, the loops propose what to ship before anyone on the team has read the brief. Not from theory, from 50 builds the loops have already processed. The headcount math breaks. Hiring scales linearly with overhead. Loops scale with the size of the data library, which compounds with every build. What this looks like in practice for me right now: Every client kickoff feeds into a structured project log Every architecture decision is tagged and described Every issue we hit in production gets logged with the fix Every prompt that worked on a particular kind of integration is stored against the integration type By the time client number 50 comes in, the system has already processed 49 versions of “how this kind of build usually breaks in week 6” The loops aren’t autonomous I review every output They don’t make architecture calls. They surface candidates. They draft documentation. They propose feature additions based on what worked on similar prior builds. The human stays in the loop on every decision that touches client value But what they unlock is a kind of leverage that hiring never produced A senior engineer brings 10 years of pattern recognition The loops carry every pattern from every build I’ve ever shipped, queryable in seconds The engineer’s pattern recognition gets better over their career The loops’ get better every Monday Boris is naming this transition at the mode level the shift is exactly the same at the agency level The work is no longer prompting the AI. The work is writing the loops that prompt the AI, and accumulating the data the loops run on Most agencies in 2027 will be one of two things: Either headcount scaling shops still hiring juniors or loop scaling studios with a fraction of the team and 10x the throughput per builder Not fully autonomous yet Not pretending to be either But the architectural shift is real and most operators haven’t internalized that it applies to their business too, not just to the AI building it

Try this: randomly rap your knuckles on a wall so the noise keeps the lions out of your neighborhood. No lions right? If you take that as proof your strategy is working then you understand the investment strategy of most of the AI industry right now 😎😆 #CryptoSatire #CryptoVerse #AI #AgenticAI #AIInfrastructure #Anthropic #Claude #OpenAI #ChatGPT #AgenticEconomy