Today, @MichaelElabd, @QuantumArjun, and I are excited to announce Trajectory. We are a research lab and product company building the platform for Continual Learning. Our platform unlocks the signal already sitting in product usage, so companies can continuously post-train large-scale agentic models that outperform the frontier. @trajectorylabs We’ve raised $15M from @Conviction, @BessemerVP, @radicalvcfund, @jeffdean, @drfeifei and more. We’re partnering with some of the best AI-native companies: @ClayRunHQ @Harvey, @DecagonAI, @mercor, @RogoAI to power their agentic systems, some of which we are already in production with. We’ve brought together a world class research team from DeepMind, OpenAI, Apple, Meta Superintelligence, Amazon AGI, Scale AI, and an elite product team from Stripe and Figma. AI will never again start on day one. Every correction, every retry, every edit will make products smarter. This is Continual Learning.

There’s a good chance your open source model is costing more than the frontier. Cheap tokens ≠ cheap tasks. Here, we introduce Intelligence Density, and Density Aware Training, our post-training technique to achieve less wasted compute, better learning, all with no knobs to tune. Enabled by default in every Trajectory model.

$META just unveiled $1,299 VR glasses weighing only 100g by moving the battery and compute into an external pack. Bigger shift is that Muse becomes the interface because Zuck says you can “just talk to it” while any flat surface can become a keyboard.

At Trajectory, we care about storytelling. The storytelling about continual learning, the storytelling about the research breakthroughs it’ll take to get there, and the storytelling about the product that we need to will into existence. Brand is part of how we tell it. Here’s a behind-the-scenes look at the work we did with @metalab to craft ours

Today, we're sharing new research on Solaris, our first Interface World Model. Solaris is a new kind of operating system that generates interactive interfaces frame by frame, in real time, with no code. We find that Solaris outperforms frontier LLMs when generating new interfaces, across structural similarity and information retention. Read more and request early access at the link below.

You can just RL a coding model to paint with javascript btw

Apple has updated the traffic light buttons with Liquid Glass in macOS 27 Beta 6

Intelligence and Experience are orthogonal vectors Terence Tao is perhaps the world’s smartest person, but drop him into an accounting firm or onto a construction site and on day one he’s not going to be very productive @trajectorylabs calls this The Experience Gap, and they have a way to close it @QuantumArjun explained how at our Sovereign AI event: 00:00 Introduction 00:12 Building the platform for continual learning 01:33 The experience gap: models have IQ but no tenure 02:52 Traceability → model spec → better models and harnesses 05:27 Four wishes for the agent ecosystem 06:34 Wish 1: Trace the whole tree — and capture the corrections 08:03 Wish 2: Evals from real traffic, graded in the real harness 09:26 Wish 3: Let the agents cook, and make tool responses informative 10:34 Wish 4: Get comfortable on open weights, experiment with routers 11:51 Why owning your intelligence shouldn't be consulted away 13:15 Demo: import a benchmark, train a model, deploy it 14:28 Q&A: What's the trainable object — weights, harness, or context? 16:08 Q&A: Continual learning without training on customer data 17:13 Q&A: Episodic memory and the hierarchy of feedback 19:37 Q&A: Where continual learning matters most

@KJHMiao and I held a post-training fireside chat with the @trajectorylabs team to discuss their vision for continual learning. I particularly liked the distinction between "experience" and "IQ". Another strong reason why so many firms these days are emphasizing the importance of AI that you own! 00:00 Intro 00:34 Where the continual learning vision came from 07:03 Why a platform instead of forward-deployed engineers 14:23 Where the name Trajectory came from 19:53 Labs optimize for IQ, we optimize for experience 25:14 Continual learning without touching the model 32:06 Hot take: the most underrated part of post-training 36:45 AI natives vs tech natives vs enterprises 42:58 The magic moment: wake up and it's smarter 45:09 Three possible worlds

You can work with Bots like you would a teammate. Give them a task, shut your computer, and reach them from anywhere.

Want world class research capabilities, but don’t have the resources of a big lab? At our recent Sovereign AI event, @gabepereyra shared @harvey ’s “moneyball” approach. Here’s the playbook: 00:00 Introduction 00:37 Building a research lab on a budget 02:28 Legal Agent Bench, contracting, and the diligence dataset 03:57 Domain experts guiding synthetic data generation 05:23 Why Harvey open sourced its datasets 06:55 Working with the neo labs – and why more than one 08:20 Post-training in-house: building "Associate 1" 09:44 The model serving matrix: 60 countries, fallbacks, SLAs 11:05 Deciding what stays in production 12:29 Simple open source switches and model routing 13:55 Moneyball: "If we win on this budget, we change the game" 14:53 Q&A: Training with sensitive data 17:16 Q&A: Competing for research talent 18:46 Q&A: Designing rubrics that actually challenge frontier models 20:19 Q&A: Where the pipeline breaks — data, research, or infra 22:59 Q&A: The tension in open sourcing a benchmark 25:02 Q&A: Biggest remaining open problems 27:10 Q&A: Competing with horizontal products

The OpenAI & Jony Ive device Quick render based on the latest rumors (allegedly it's donut shaped and with moving parts)

For my first post, I’m sharing a letter @NVIDIA signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models. images.nvidia.com/pdf/Open-W…

In 2018, Sarah Guo became the youngest general partner in Greylock's 60-year history. She was 28. Four years later, she quit to launch Conviction, a firm staked entirely on AI. Before ChatGPT shipped, she seeded Baseten and Harvey; each is now worth over $11 billion. In Conviction's first year, she wrote early checks into Sierra, Cognition, and Mistral; those three companies are now worth, together, $54 billion. Andrej Karpathy worked out of Conviction's office until Anthropic hired him in May. Guo has been close to Jensen Huang for over a decade. Her first two calls after starting the firm were to Sam Altman and Nat Friedman. And yet the investor closest to the AI frontier is betting against its biggest companies. The two big frontier labs, worth close to a trillion dollars apiece, no longer just want to build the models. They also want to build every product and company on top of them, leaving nothing for anyone else. The market is paying as though they might succeed. Of the $300 billion in venture capital deployed in the first quarter of 2026, the biggest quarter in the history of the trade, 65% went to only four companies: Anthropic, OpenAI, xAI, and Waymo. Guo is betting the labs can't build everything, and she spends her days making sure of it. She won Harvey its first client. She flew across the country to take a single Baseten candidate to a four-hour lunch. On one wedding anniversary, she spent the whole weekend on back-to-back calls, keeping two founders on the line so they couldn't speak to rival firms. Twice a year, she flies the world's brightest young founders to San Francisco and inducts them into the fight. In the months @domcooke spent reporting this piece, @saranormous had her fourth child, walked the Met Gala in 45 pounds of chainmail, and still answered her founders' texts within minutes. Guo's parents arrived from China in 1987 with $50, built a company, and took it public at $1.2 billion. Then it went bankrupt. Guo grew up inside that startup. She built its first website, did her homework in a cubicle, and slept over for bug bashes. She loved it. If two labs build everything, no one gets to do that again. Welcome to Sarah's Wager. Read it below.

What does the next training paradigm look like? 0:00:00 – The big research bet the labs are making 0:02:12 – Grindability is just as important as verifiability 0:06:10 – Will RLVR alone generalize? 0:08:41 – Getting the learning back to the weights 0:15:22 – Dreaming 0:17:23 – What 2027 looks like Also on YouTube, pod feed, and Substack.