Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.

.@OpenRouter co-founder Alex Atallah on the "everyone is building the same thing" take: "This reminds me of this tweet I saw... Everybody's building an agent loop with notifications, third-party connectors, context management, memory, sandboxes, agentic web search, an always-on agent on top." "This product is showing up everywhere. It is showing up everywhere, but these are just the new table-stakes primitives." "A 2005 version of that tweet would be: 'Oh, everybody's building the same thing. A database, a users table, a sign-in page, a sign-up page, a profile page, a logout page. Everything's the same.'" "There's like a lot of differentiation really. There's table-stakes needs for AI just like there's table-stakes needs for the web." @alexatallah @amasad

Introducing Factory Benchmarks: The first model bench generated from your own coding tasks. Measure, test and improve coding agents by replaying past agent runs, and cut cost-per-PR by 63%+ Here’s how it works 🧵

Many people seem to believe that if instead of a 'civilization', I had called them a 'swarm of matrices', there wouldn't be a problem worth worrying about.

how to build an autonomous landing page engine that self improves from competitor churn: 1/ set up the @firecrawl MCP with cron/routine to run scheduled scrapes. pull from G2. Trustpilot. Reddit. forums. extract EXACT cancellation phrases into a living churn database refreshed weekly. 2/ run multi agent analysis in claude. cluster complaints by purchase friction. run 5 synthetic buyer panels to score candidate copy against current PostHog baselines. test the skeptic. the price checker. the comparison shopper. 3/ auto generate modular page component packages. → a pain inversion headline using an exact 4 word churn phrase → an uncropped UI screenshot showing the specific fix → an objection-handling block tailored to that competitor's flaw. 4/ deploy Next.js edge middleware. personalize the page in real time. match the variant to the incoming ad creative ID. UTM parameters. search intent. PostHog user traits. 5/ connect the @posthog MCP to run HogQL power calculations. set a hard volume floor ie 400 conversions per variant to call a valid 20% lift. default stance is underpowered until the math clears. 6/ auto promote winning variants via feature flags. route 80% of ad spend to the winner. feed the winning copy back into your Meta ad pipeline. treat CRO like a continuous data engineering loop

you can remix every competitor's entire website in a weekend now with an seo agent here's the build: pull your 10 closest competitors grab every sitemap composite database of every page they've ever published cluster it into topics, not keywords. you're mapping their content architecture dataforseo api to see what's actually ranking page one vs what's just sitting there dead content gap analysis: what everyone covers is table stakes and what nobody is covering is your wedge now the part everyone skips record 30 minutes of you ranting about your industry. transcribe it. feed it in as source material add your own data. product numbers, customer benchmarks, anything with a real stat in it write the articles, the landing pages, the vs pages, the alternatives pages, the glossary build the internal link graph on the way out, hub → cluster, nothing orphaned publish all of it in one shot then monthly you refresh off search console decay + a fresh gap analysis. rewrite what's slipping, kill what's dead, expand what's getting impressions but no clicks. two things worth knowing: the vs and alternatives pages are the highest ROI slice of the whole build. bottom funnel, your competitors won't write them honestly, and they're exactly what people type into chatgpt. and ranking and getting cited are different jobs. ranking is depth and links. getting cited is structure direct answer up top, definition blocks, comparison tables, h2s that match the actual question an llm is not reading your 400 word intro the agent gets you parity. everyone's going to have this in 6 months the 30 minute rant and your proprietary data are the only inputs that can't be scraped back out of you that's the moat now

Vercel CEO Guillermo Rauch thinks the future of multi-agent work may look like one agent. Vercel gives employees one front door. behind it, the company harness routes each request to the right knowledge, tool, or specialist agent. employees shouldn’t need to understand the agent architecture. they should just ask the company. here’s how to build the shared harness behind it.

you can just remix all your competitors website content in a weekend now with an SEO agent how find your 10 competitors find their sitemaps build a composite database of all their pages build a content map of what you should write about research what is ranking page one for target kewyords competitors content is targeting use data for seo API to find all this data include a 30 minute transcription of your opinion on the industry in the source material write the articles and landing pages publish all this content in one shot every month refresh the content based on content gap analysis, and the related search console data you're now competing with your competitors on SEO and AI search

The Certifiably Insane Way to Build an AI Agent: 1. choose a category where mistake tolerance is roughly the same as it is in self-driving cars. we chose "email-based scheduling assistant." many people want this product, but they immediately fire him if he screws up an interaction with a prospect, a candidate, or a potential investor 2. you learn that the edge cases are too complex and too frequent to be solvable. ours: managing timezones for people who travel (and change travel plans) constantly. knowing when NOT to respond, when to text the customer on the side to verify something, when to follow up, which sub-calendar to use, when to bend the rules on availability, when we can schedule that one type of call during your commute but not the other type of call. sharing your availabilities without compromising your privacy. and on and on. 3. the product doesn't feel viable, but you don't want to give up. you spend hours in a hot tub in Marin with a friend who makes self-driving cars. you make a plan to do it the way they did: hold the steering wheel. you go home and build a human-in-the-loop platform and hire contractors to serve as a backstop and catch mistakes before they happen (and to help design a map of what a world-class EA would do in every weird scenario). you decide trust is the currency in your category, so it must be the thing you won't compromise on. the product must succeed at any scheduling request, no matter how complicated. 4. you instantly feel an overwhelming market pull. so you keep going, growing that team to 75 people working 24/7 to support the nonstop scheduling needs of your customers. tons of engineering time goes to scaling the human platform instead of building the product. 5. you try to raise a Series A and investors say you are insane. your gross margins are extremely negative. they believe this is a problem worth solving, but they don't believe it is as hard to solve as you say. they want AI, not humans. your competitors put "NO HUMANS IN THE LOOP" on their landing pages to call you out. you keep going. 6. you work day and night building the harness that can meet the quality standard your customers have come to expect. you create a massive synthetic gold dataset. audit it, and clean it, label it. repeat. then, experiments. fine-tuning. RL. ACE. DSPy. sub-agents. sub agents for your sub-agents. rebuild the harness. throw more tokens at the problem. 7. some weeks you make big progress. some weeks your evals climb a single basis point, but that's better than nothing. more experiments. more tokens. john coogan said the hot trend in 2026 will be dogged pursuits. that pushes you to continue the pursuit, doggedly. 8. then, one day, you realize you are scheduling thousands of meetings a day and approaching 50% autopilot with no increase in churn or complaints. you put 150 customers in a full self-driving experiment, and they use the product MORE than they were using it when they had the human backstop. you can really start to let go of the steering wheel. 9. you don't know yet if this was a hill worth climbing, but you are nonetheless stoked that you can see the top. you have created a proprietary map of what to do in a million different situations. nobody else has that map, and the models keep getting better at following maps. your plan was to bet on trust, and your product can be trusted. today was the first day Howie crossed 50% autopilot:

Today we’re launching the OpenAI Deployment Company to help businesses build and deploy AI. It's majority-owned and controlled by OpenAI. It brings together 19 leading investment firms, consultancies, and system integrators to help organizations deploy frontier AI to production for business impact. openai.com/index/openai-laun…