Stocks are Crashing—Here's How That Changes AI in 2025 скачать в хорошем качестве

Stocks are Crashing—Here's How That Changes AI in 2025

My piece on this: https://open.substack.com/pub/natesne... My site: https://natebjones.com/ My links: https://linktr.ee/natebjones My substack: https://natesnewsletter.substack.com/ Takeaways: 1. The Intelligence–Distribution Gap Is Widening AI model releases are accelerating, but distribution and implementation are not keeping pace—creating a growing dislocation that’s becoming the central tension of 2024. 2. Capital Markets Are Bottlenecking Innovation Economic uncertainty, a frozen IPO market, and retreating VC investment are causing companies to pull back on speculative AI spending, especially on complex agent infrastructure. 3. Complex Agents Require Heavy Infrastructure From memory management to routing and orchestration, useful agents aren’t plug-and-play. The technical burden is still high, and few companies are resourced to take it on during a downturn. 4. Model Overload Is Real With Claude 3.5 and 3.7, Gemini 2.5, LLaMA 4, and multiple OpenAI models in the mix, model diversification is at an all-time high—but strategic clarity about which one to use is rare. 5. Copilot Wins on Distribution, Not Capability Most companies will default to tools like Microsoft Copilot not because they’re best-in-class, but because they’re already embedded and easy to roll out in enterprise environments. 6. Margin-First AI Will Dominate This Year AI investments that improve the bottom line quickly—out-of-box voice agents, ticket resolution bots, cost-saving automations—are the only ones likely to survive 2024’s market conditions. 7. Middleware Is the Sleeping Giant The biggest opportunity right now is in middleware: infrastructure that makes it easier to deploy, manage, and maintain AI systems. Model makers aren’t investing here—builders should be. Quotes: “We’re living through a dislocation that capital markets are accelerating—the gap between AI intelligence and AI distribution has never been greater.” “During good times, companies invest in closing that gap. But when capital is tight, they won’t build what won’t deliver margin now.” “Even though the models will keep coming, the strategy to use them is going to be rarer and rarer.” Summary: This episode explores the growing dislocation between accelerating AI intelligence and lagging enterprise distribution. While model makers like Meta, Google, and OpenAI are releasing increasingly powerful models, most companies are pulling back on AI investment due to economic pressure and uncertainty in the capital markets. Complex agent infrastructure remains difficult and expensive to build, and only solutions that drive immediate margin impact will get prioritized. The result is a widening gap—and an emerging opportunity for those building middleware and margin-first AI products. As hype fades, the future belongs to those who can deliver durable implementation. Keywords: AI agents, capital markets, stock market crash, AI deployment, LLM infrastructure, model releases, Claude 3.5, Claude 3.7, Gemini 2.5, LLaMA 4, OpenAI models, Copilot, Microsoft, middleware, agent orchestration, economic pressure, VC slowdown, AI strategy, AI implementation, autonomous workflows, enterprise AI adoption