A couple years ago I had a conversation with 𝐃𝐚𝐫𝐢𝐨 about the future of AI and specifically Large Language Models (LLMs) and their potential capabilities... linkedin.com/feed/update/urn…

Super cool that @veritasium covered this in a video. UNH has a great story of impressive UNH professors - linkedin.com/pulse/when-ai-c…

The big winner in all of this is going to be open weights models. This is a huge win for the field, as a risk that was entirely theoretical and untested 2 days ago (that a model could be pulled back), now has a new precedent that’s been set. The game theory the US should highly consider, and the risk with regulating AI at the model layer vs. applied layer, is that other countries now have even more incentive to develop sovereign AI. If at any moment a model can be become unavailable to your country’s users or businesses, this poses very real risk on relying on technology from a particular country. As a result, it forces major countries to charter their own path on AI development, which reduces America’s leadership role in this tech stack over time. The most likely solution that other countries will rely on is open weights models, which currently is generally not coming from the US. America should be considering all of these downstream implications as it decides how and where in the stack to be regulating AI. At the same time, we should be doing a ton more OSS innovation.

Liftoff! First $SPCX trade complete 🚀

Congratulations to @Greg406 on being named one of @BostonGlobe's 2026 Tech Power Players. Greg has been with .406 since 2007, nearly the beginning. Smart, diligent, thoughtful, and incredibly kind. You couldn't ask for anyone better in your corner. bostonglobe.com/2026/06/09/b…

RT @LatentSpin: Is your AI agent actually learning, or is it just being reminded? LatentSpin founder, @ThomasHazel, breaks down the line e…

There’s a new “FAAMG for AI” acronym going around: 𝐌𝐀𝐍𝐆𝐎 - Microsoft - Anthropic - NVIDIA - Google - OpenAI It’s clever. But it feels incomplete. For AI, expanded map: 𝐌𝐀𝐍𝐆𝐎 𝐌𝐀𝐗 - Microsoft - Anthropic - NVIDIA - Google - OpenAI - Meta - Amazon - xAI / SpaceX If the answer is AI, what was the question? linkedin.com/feed/update/urn…

Fable 5 is state-of-the-art on nearly all tested benchmarks, with exceptional performance in software engineering, knowledge work, scientific research, and vision. The longer and more complex the task, the larger Fable 5’s lead over our other models.

Three forces are reshaping the AI cost structure : 1. Foundation labs are moving up the stack into applications 2. Frontier model prices keep rising for the smartest models 3. Open-source models have crossed the good enough threshold for most use cases The natural response from AI buyers is substitution. 🧵

When AI baseline intelligence drops to near-zero cost, standard models become table stakes. Your true moat isn't a cleaner tech stack, it's the implicit, unwritten institutional knowledge inside the brains of your senior workers (aka knowledge workers). Stop trying to replace humans with out-of-the-box LLMs; that just lowers the barrier to entry for your competitors. Instead, use your veteran experts to guide, refine, and inject domain-specific constraints into your AI workflows. This creates a proprietary, compounding value loop that no competitor can buy off the shelf. The future belongs to enterprises that treat human capital as the ultimate training loop to build a smarter institutional brain. At LatentSpin we're building an agent platform based on continuous learning. Knowledge workers build and teach agents much like a new hire. This compounds intelligence within the agents and also the business. Follow us this summer and look for our early access program... #EnterpriseAI #FutureOfWork #AIStrategy #GenerativeAI #LatentSpin #buildinpublic

𝐀𝐧𝐭𝐡𝐫𝐨𝐩𝐢𝐜 just dropped an important essay on 𝐫𝐞𝐜𝐮𝐫𝐬𝐢𝐯𝐞 𝐬𝐞𝐥𝐟-𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭, and I think it is a serious contribution to one of the defining questions in AI. Though there is a critical distinction worth making. latentspin.ai/insights/recur…

Open-weight models have overtaken closed models on OpenRouter. 69.1% of token volume now goes to open-weight models. 30.9% to closed. Competition is a discovery procedure — and developers are discovering the value of open models. 🧵

Introducing Claude Opus 4.8: it builds on Opus 4.7 with sharper judgment, more honesty about its own progress, and the ability to work independently for longer than its predecessors. Available today at the same price.

State of Enterprise AI 2026: @levie on Tokenmaxxing, The Rise of Headless, and AI-Proofing Your Job 00:00 Intro 01:18 Silicon Valley engineering vs. everyone else 05:35 Are enterprise CIOs actually bullish on AI? 08:51 Tokenmaxxing & why your AI bill is about to explode 11:34 The myth of falling token costs and AI spend escaping IT budgets 17:37 The $5B startup hiding in AI compute 18:14 The mosaic of models inside every enterprise 21:28 Why coding works and the rest of knowledge work doesn't 25:53 The Bob and Sally problem: access control breaks agents 30:31 Will enterprise AI really take 10 years to roll out 32:24 The capability overhang: why faster models slow diffusion 34:23 Data is the bottleneck (it always was) 39:02 The rise of internal forward-deployed engineers 41:23 Why the AI doomers are wrong about jobs 43:43 Headless software is inevitable 46:14 What replaces per-seat pricing 47:37 How Box itself is going headless 49:42 How the org chart actually evolves 1:00:33 Future-proofing yourself as an enterprise employee 1:06:40 Are we all just going to work for OpenAI and Anthropic? 1:07:11 Where startups can still win as the labs move up

As a weekly listener to ALL-IN, this week touches on a topic ( #ContinualLearning or #ContinuousLearning ) of LLMs/Agents. The next great unlock to come! @LatentSpin

Hoping for launch... T-1:13:45. @SpaceX

Today I probably tried one of the coolest tools since @OpenAI's chatgpt app. If you have not heard of @suno, you definitely need to check them out. Made some computer science nerds sound pretty cool - youtube.com/watch?v=zGKLlSHC…

New blog: The AI industry is debating "model vs. harness"... but that framing is already becoming outdated. We mapped out the 4 generations of AI agent architecture: Gen 1 >> Copilots (reactive, session-based) Gen 2 >> Agent Runtimes (autonomous, RAG, tool aware) Gen 3 >> Agent Operating Systems (persistent, context-rich) Gen4 >> Cognitive OS (the harness teaches the model) The real shift isn't smarter models or smarter wrappers. It's when execution becomes the source of future intelligence. Read the full breakdown 👇 latentspin.ai/insights/from-… #AI #AIAgents #LLM #GenerativeAI #MachineLearning #ArtificialIntelligence #AgenticAI