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An AI crash could still leave behind power plants, data centers, open models, and a workforce trained to work with machines. Overbuilding is how new infrastructure usually gets installed. The crash only opens the door - the rebuild depends on who already owns the compute, models, and tools when prices reset. Spread the means, not the proceeds.
An ontology is a formal, explicit specification of a shared conceptualization. It is an agreement about what things exist in a domain, how they relate, and what we call them. As AI agents become the primary consumers of data, we need a layer that explicitly describes what things mean, how they relate, and what rules govern them. Structure knowledge and make it accessible, and the market will sort out the labels.
Recruiting feels perfectly aligned with AI capabilities because, at its core, it's mostly a data problem. This article looks at the state of the AI recruiting industry. Every AI recruiting company seems to run into the same problems: recruiting isn't very frequent, causing natural user churn; companies can use multiple platforms at once, so there's no loyalty or lock-in; a data moat is hard; and the long-term winner needs to become part of the broader recruiting window to scale. The space is still early, and markets can be unlocked with new capabilities.
'Talent' and 'Talent engineering' are two new roles that have recently emerged. Frontier labs, VCs, and startups are hiring for both roles. A 'talent' is a person who is great at attracting smart people and has deep relationships with all of them. A 'talent engineer' is someone who loves to build people tools and is also a great engineer. The highest talent density companies now hire based on a blend of talent and talent engineering rather than using the traditional model of recruitment.
You can't out-onboard a customer who only needed you once. 10% monthly churn means rebuilding 70% of your user base every year. The biggest lever is category choice, because a daily marketer workflow retains nothing like a one-time birthday-card job. Blended churn hides the split between sticky cohorts and tourists, so the work is steering ICP, onboarding, and acquisition toward the people who come back. Annual plans, dunning, and win-back flows help at the margin, but choosing a repeat-usage customer is the whole game.
A lot of founders are describing their company with the wrong game board. PLG, enterprise trust, marketplace liquidity, and liquidity financing each have different rules, clocks, and ways to die. Name the win condition first, then the feedback loops and constraints that follow from it. Strategy gets sharp the moment you can name the game everyone else is misplaying.
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X has released a Model Context Protocol (MCP) server that lets AI tools communicate with the X API. The new MCP doesn't add new capabilities beyond what X's API already offers - it just makes them easier to use for AI applications. The integration turns X into an information network filled with real-time data to retrieve and analyze. The MCP tool is not compatible with X's Write API endpoints, so it can't be used to post autonomously.
Claude Sonnet 5 can make plans, use tools, and run autonomously for long periods. Its performance is close to that of Opus 4.8 but at lower prices. Safety assessments show that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer to use in agentic contexts. It has a much lower ability to perform cybersecurity tasks than current Opus models. Claude Sonnet 5 is now available across all plans.
Nano Banana 2 Light, also known as Gemini 3.1 Flash Lite Image, is the fastest and cheapest Gemini image model. The model produces better results than previous generations, but it still makes some spelling mistakes. This post shows what it produced when prompted for a Where's Waldo-style image.
AI has created a new type of user that is rapidly consuming an expanding share of software. Agents intuit and compose software instead of navigating it. They care purely about capabilities and whether they can be invoked and chained reliably. The best product for an agent is just the best product for a developer - software companies need to start thinking like developer tools companies.
High-frequency trading firms succeeded by developing systems that effectively extracted predictive signals from market data at millisecond timescales. Companies are now applying similar approaches to the physical world, creating digital representations of industrial processes to optimize financing and trading of physical assets. The focus is on developing the "reality stack" to turn physical signals into actionable financial intelligence, reshaping markets and investment strategies.
Marc Andreessen argues that the primary obstacles to AI-driven productivity are institutional and infrastructure-based rather than technological.
While liquidity is heavily concentrated among a few top-tier names, structured company-run tender offers are increasingly utilized to distribute capital on a recurring basis without disrupting 409A valuations.
An Omnilux LED face mask and Dyson's "AI-powered British strawberries" explain more about growth than another prompt hack.