Bassem Asseh

Anthropic’s Claude popularity with paying consumers is skyrocketing techcrunch.com/2026/03/28/ant…

« The debut of OpenAI and Anthropic shares would be a landmark moment in the AI boom, which has boosted the shares of major cloud and chip firms that cater to OpenAI, Anthropic and other key developers. » theinformation.com/articles/anthr…

Anthropic’s revenue surge looks staggering, but it’s powered by a simple formula: extrapolating a few weeks of explosive growth into a full year. thein.fo/3PKJgzh

APRIL IS GOING TO BE SICK GPT-5.5 CLAUDE 5 MYTHOS DEEPSEEK-V4

Google released new tools for its Gemini artificial intelligence assistant that will let users upload chat history and context from other AI apps, a move to lure users from rivals like OpenAI’s ChatGPT and Anthropic's Claude bloomberg.com/news/articles/…

Mistral releases a new open-source model for speech generation techcrunch.com/2026/03/26/mis…

🦔Microsoft has told managers in its cloud unit and North American sales groups to suspend new hiring, according to The Information. The freeze applies to any candidates without an existing offer and is being driven by cost cutting ahead of the fiscal year end in June. Divisions building Copilot and other AI products are still hiring. Microsoft has about 228,000 employees globally and reported slower cloud growth alongside record AI capital spending last quarter, a combination that has been making investors nervous. My Take Microsoft is pouring record capital into AI infrastructure while cloud growth is slowing, and the math on that only works if AI starts generating returns fast enough to justify the investment. Freezing hiring in cloud and sales while continuing to build out Copilot is a clear signal of where they think growth is supposed to come from, and it isn't people. I've been skeptical about whether the ROI on AI spending materializes at the scale being bet on, and Microsoft is one of the clearest examples of that tension. They've committed billions to OpenAI, billions more to data centers, and are now cutting headcount costs to protect margins while they wait for it to pay off. Amazon is doing the same thing, Meta is doing the same thing. The pattern across every major tech company right now is identical: replace people with AI investment, show the market you're being disciplined, and hope the returns show up before the questions get too loud. We're getting closer to finding out whether that bet was right. Hedgie🤗

After @Pinterest @Airbnb @NotionHQ @cursor_ai, today it’s @eoghan @intercom publicly sharing that they’re finding it better, cheaper, faster to use and train open models themselves rather than use APIs for many tasks. And hundreds of other companies are doing the same without sharing. Ultimately, I believe the majority of AI workflows will be in-house based on open-source (vs API). It took much more time than we anticipated but it’s happening now!

I think that if companies are not failing at all with their AI efforts it is a sign that they are not being ambitious enough. This is a fundamentally new technology that we do not know how to use well. Achieving breakthroughs will require experimentation, which require failure.

Local inference...

I want it

Cursor can now create new components and frontends in Figma using your team's design system.

“We’ve also been moving off legacy systems with poor, slow, outdated, and inconsistent APIs.” If you’re building software that can’t work fully headlessly in a way that agents want to use, you’re not prepared for what the future of software is going to look like. Agents will use software 100X more than people, and people will more and more interact with their data and workflows via agents across many different platforms. This is the real risk but also opportunity for platforms right now. Software doesn’t go away, but it becomes the guardrails and business logic for what agents are able to operate on. But if you can’t connect to wherever the agents want to do that work, you’re DOA.

Would be so cool if OpenAI open-sourced Sora as they're shutting down the app! Would be an amazing contribution to the field and make all the efforts of the teams working on it even more meaningful!

Ryanair, HelloFresh, and Gather[.]dev are joining us at Leap Week. Here's the full lineup of who's coming: @Railway. Building and deploying a production-grade app from scratch. Zero to production in one session. @Firecrawl. Extracting structured data from anywhere on the web and pushing it into your backend automatically. @Clay. Turning enrichment data into personalized landing pages at scale. Built live. @HelloFresh and @Gatherdev are joining Ben for our Bridging Bytes roundtable on how technical leaders are giving teams real access to AI while managing governance, data, and risk. And @Ryanair is sitting down with us for a live conversation on how one of Europe's largest airlines manages data infrastructure at scale. Nine sessions. Three days. Starts Tuesday. Register at leapweek.directus.io

Firecrawl is joining Leap Week. On March 25, we're showing how to pull structured data from anywhere on the web and push it directly into your backend. @firecrawl handles the extraction. Directus handles the rest.

How can AI application startups compete with the big labs and incumbents? I shared some of our thoughts on this @a16z with @Kantrowitz 👇

« In other words, Palantir offers so-called data integration services like what Snowflake has; consulting services that everyone is trying to emulate; and an “AI action layer” similar to what OpenAI and Anthropic (and many other firms) want to sell to businesses that want to use AI agents. » theinformation.com/articles/palan…

Open source @Qdrant just raised $50M and what it means is that vector database (or what Andre Zayarni prefers to call the '..information retrieval layer for the AI age') is more relevant than ever before. With insights from Kamen Kanev (GlassDollar) and Herbert Turner (&AI) venturebeat.com/data/agents-do… via @VentureBeat

Two Turing-class AI researchers just raised $2B in three weeks to bet against every LLM company on the planet. Fei-Fei Li closed $1B for World Labs on February 18. LeCun closed $1.03B for AMI Labs today. Both building world models. Both arguing that the entire generative AI paradigm is a statistical parlor trick. And the investor overlap tells you this is coordinated conviction, not coincidence. Nvidia backed both. So did Sea and Temasek. The math on AMI is absurd. $3.5B pre-money valuation. Four months old. Zero product. Zero revenue. The CEO said on the record that AMI won’t ship a product in three months, won’t have revenue in six, won’t hit $10M ARR in twelve. He described it as a long-term scientific endeavor. Investors gave him a billion dollars anyway. This tells you everything about how the smart money is actually modeling AI’s future. They’re not pricing AMI on a revenue multiple. They’re pricing it on the probability that LLMs hit a ceiling. And if you look at the investor list, Nvidia, Samsung, Toyota Ventures, Dassault, Sea, these are companies that need AI to understand physics, geometry, and force dynamics. A language model that can write poetry is worthless to a robotics company trying to predict what happens when a mechanical arm applies 12 newtons at a 30-degree angle to a flexible surface. LeCun raided his own lab to build this. Mike Rabbat, Meta’s former research science director. Saining Xie from Google DeepMind. Pascale Fung, senior director of AI research at Meta. He walked into Zuckerberg’s office in November, told him he was leaving, and four months later half of FAIR works for him. Meta is reportedly partnering with AMI anyway, which means Zuckerberg thinks LeCun might be right even while Meta keeps scaling Llama. AMI’s first partner is Nabla, a medical AI company, building toward FDA-certifiable agentic AI. That’s the use case that makes world models existential. LLMs hallucinate. In healthcare, hallucinations kill people. You can’t prompt-engineer your way out of a model that generates statistically plausible text when you need a system that actually understands how a human body works. Two billion dollars in three weeks. Two of the most credentialed researchers alive. And a thesis that says the $100B+ already poured into scaling LLMs is optimizing the wrong architecture entirely. If they’re wrong, investors lose money. If they’re right, every company building on top of GPT and Claude for physical-world applications just bought the wrong foundation.