Philip Brittan

Entanglement, Inc. launches Maritech Intelligence Systems, Inc., a direct result of our recent acquisition of Applied Ocean Sciences (AOS) “This is what happens when decades of deep ocean science meets quantum-logic AI at scale. Maritech unlocks maritime intelligence capabilities that simply weren’t computationally possible before—at a moment when the oceans matter more than ever.” — Jason Turner

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I’ve been working on the theory of why small, shippable factories are the perfect inversion to the centralized prefab factory model. It all collapsed into one line of math (credit to Gilles Retsin): Factory Efficiency = Usage × (Throughput × Product Value ) / ( CapEx + Deployment OpEx) The idea is that the closer you can drive Usage to 100 % while keeping the denominator tiny, the faster the flywheel spins.  A shippable microfactory tackles this by primarily focusing on a high Usage factor. Because the microfactory can be packed up afterwards and reused, its CapEx gets amortized over multiple projects, effectively raising its overall Usage across the year. If one project alone doesn’t fully occupy the factory, it can simply roll over to another job to maintain high Usage. Its CapEx is often an order of magnitude lower than a centralized factory (< $1M vs. $10-50M min), so the breakeven throughput is more achievable on a small pipeline of work. The real breakthrough here is portability. There is near-zero stranded capital and almost no idle time. To flesh this theory out, I wrote 3000 words on the topic for Brad Hargreaves and Thesis Driven. The article breaks down: - why “fixed factories, shipped goods” is being inverted to shipped factories, fixed goods - what a sub‑$1 M robotic cell does to CapEx per home versus a £45 M off‑site plant - early production/cost data on a shippable microfactory from Mollie Claypool, Gilles Retsin, Sam Baker and the Automated Architecture (AUAR) team - the hybrid reality of robots tackling the heavy structural components and human crews assembling faster and cheaper - technical constraints to pull this model off and future considerations on the optimal business model (own vs. rent) Major shoutout to Gilles Retsin for helping me pull this together. https://lnkd.in/eRTnbZPa

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Cboe Global Markets is rolling out an innovative approach to prediction markets. "Today's event contracts only offer two binary outcomes: "yes or no," "all or nothing." Cboe's prediction markets will introduce a third dimension – a new middle ground – enabling customers to engage with defined risk, while gaining the opportunity to earn a partial payout when they are directionally correct, even if the result is not precisely on their target." Polymarket & Kalshi have sprinted to integrate and become part of all retail trading apps and media. The large exchanges are coming and likely bringing a lot of liquidity and ultimately institutional traders.

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New York City-based Betaworks has closed its third fund at $66M to invest in early-stage AI startups. Betaworks, a staple of the NYC tech ecosystem since 2008, will focus its new fund on investing in pre-seed and seed-stage AI companies. The firm has been investing in AI since 2016 and has backed companies like Hugging Face. Fund III is larger than the firm's previous funds ($46M Fund II in 2020 and $48M Fund I in 2016) and saw many of its previous limited partners return to invest, a strong signal in a challenging fundraising environment for VCs. The fund will seek to make at least 25 pre-seed to seed investments, as well as 50 investments into startups as part of the Betaworks Camps program. Congratulations to the Betaworks team. TechCrunch: https://lnkd.in/d--hfMpa #VentureCapital #AI #EarlyStage #SeedFunding #NYCTech #Fundraising

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Tokenization reaching Wall Street is a headline. Building compliant, liquid, enforceable on-chain markets is the real test. Without infrastructure, issuance is just digitization, argues Ariel Shapira https://lnkd.in/g9h-gt9b

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2) Agentic Deployments on the Rise. While not inclined to call for a significant inflection, we think agentic deployments in production environments will rise noticeably in 2026. There is still much to solve for but our confidence in a continued ramp of agentic deployments stems from: a) rapid progress in how LLMs access the tools and the data they need in a standardized manner due to the rise of Model Context Protocol (MCP), AI Skills and AI Command Line Interfaces (CLI). Furthermore, continued advancement in agent frameworks such as LangChain, LlamaIndex, CrewAI and Autogen are making it easier for software vendors and enterprises to build out their fleet of AI agents. The steady advance of agentic deployments fosters demand across key markets within our coverage including: a) data infrastructure, which provides agents with fresh, accurate data to make decisions and execute actions as well as the means to manage state in the context of a transactional application; b) observability, which becomes increasingly mandatory due to agentic deployments having a more fragile failure model and to the need to audit, trace, manage and report on every action an agent takes; and c) workflow automation to route agents to the right data sources, enforce guardrails and ensure more predictable outcomes within a defined IT or business process.

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The path to AGI is shifting from pure intelligence to owning the entire stack. My conversation wiht Eiso Kant, Co-CEO of poolside (new on the pod 🎙️): Some highlights (all links to full episode below): * Intelligence is a Commodity, Infrastructure is Destiny: Eiso argues that AGI intelligence will become less distinguishable over time. The real, sustainable competitive advantage lies in controlling the three layers: Energy, Compute, and Intelligence. "If you're a foundation model company and you're not building physical infrastructure, you're cosplaying your business". * Project Horizon: The Gigawatt Scale: Poolside is moving to secure its future with Project Horizon, a multi-gigawatt AI factory data center complex. This addresses the core bottleneck—lead time—by owning the physical build. They are pioneering an "incrementally delivered hybrid modular" construction approach for unmatched speed and cost-efficiency. * The Next Frontier in Training: "Reinforcement Learning to Learn" (RL2L): Moving beyond current RL methods, Poolside is developing a generalized objective they call RL2L (internally "Bondi"). This technique seeks to "reverse engineer the web... into the thoughts and actions that created it"—a massive new scaling axis designed to push reasoning capabilities much earlier in training. LINKS: YouTube: https://lnkd.in/ehGd-3qx Spotify: https://lnkd.in/eQHhvSde Apple Podcasts: https://lnkd.in/eeMr866G

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With the growing presence of AI, ZK tech and blockchain as the single source of truth/data protection layer is an even more important use case than ever before.

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