DX space

Of course we can’t trust Elon at this point. That’s why it’s worth reading the arguments he was replying to, the reply that sent shockwaves through the AI investor class. Frankly, it hardly matters whether he’s doing financial engineering for his own ventures or just getting back at Sam over an old grudge (understandable, even forgivable) by throwing a wrench in the gears ahead of OpenAI’s planned IPO. Whoever lit the match, the smoke is real. And the so-called smart money is still debating whether there’s a fire.

Companies rushed in on AI. Now some are pulling back. Rapid AI adoption left workers cleaning up a flood of errors and low-quality work. The Guardian

Stocks price the story, bonds price the cash flows. IYKYK

There goes the first one 👍 link

⚠️ Forewarned is forearmed: • Understanding "AI psychosis" • Gen AI addiction syndrome Stay sane 🧠

Two brains are better than one https://med.stanford.edu/news/all-news/2026/09/two-separate-brains.html

Yet another sharp team applying active inference has come together to disrupt robotics. If scouting early-stage deep-tech is your job, you should reach out to them today. https://theblanket.ai/

Active Inference for Physical AI Agents - An Engineering Perspective Bert de Vries, 21 Mar 2026 "Physical AI agents, such as robots and other embodied systems operating under tight and fluctuating resource constraints, remain far less capable than biological agents in open-ended real-world environments. This paper argues that Active Inference (AIF), grounded in the Free Energy Principle, offers a principled foundation for closing that gap. We develop this argument from first principles, following a chain from probability theory through Bayesian machine learning and variational inference to active inference and reactive message passing. From the FEP perspective, systems that maintain their structural and functional integrity over time can, under suitable assumptions, be described as minimizing variational free energy (VFE), and AIF operationalizes this by unifying perception, learning, planning, and control within a single computational objective. We show that VFE minimization is naturally realized by reactive message passing on factor graphs, where inference emerges from local, parallel computations. This realization is well matched to the constraints of physical operation, including hard deadlines, asynchronous data, fluctuating power budgets, and changing environments. Because reactive message passing is event-driven, interruptible, and locally adaptable, performance degrades gracefully under reduced resources while model structure can adjust online. We further show that, under suitable coupling and coarse-graining conditions, coupled AIF agents can be described as higher-level AIF agents, yielding a homogeneous architecture based on the same message-passing primitive across scales. Our contribution is not empirical benchmarking, but a clear theoretical and architectural case for the engineering community." https://arxiv.org/abs/2603.20927

Borrowing money at a 17.5% interest rate to run a business that’s making losses is the chef’s kiss, truly iconic https://www.reuters.com/business/openai-sweetens-private-equity-pitch-amid-enterprise-turf-war-with-anthropic-2026-03-23/

Premium-grade cash evaporation

ActInf Inside™ https://youtu.be/yRV8fSw6HaE

Blossom by blossom the Active Inference spring begins. https://resiliencemedia.co/stanhope-ai-raises-8m-for-new-approach-of-ai-for-physical-applications/

"It's a time bomb, and I want to short it", The Big Short

September 2025 Newsletter 🦔🛠️🐚 Active Inference Institute https://activeinferenceinstitute.substack.com/p/september-2025-newsletter-active