Our Quick Take

AI is moving fast from "cool demos" to actual business tools, but the companies winning aren't necessarily building the best models—they're getting better at using AI in practice. Technical advantages now last months instead of years, so the real edge comes from knowing how to deploy, secure, and measure AI systems effectively. While everyone's debating which model to use, the smart money is on teams that can ship AI features reliably and fix them when they break.

New & Newsworthy

1. Companies Are Actually Using AI for Real Work Now

Enterprise AI adoption jumped from 11% to 33% in two quarters. That's not hype, that's real companies solving real problems. Intuit's AI agents help businesses get paid 5 days faster. Capital One and LinkedIn are running AI agents in customer-facing apps. The pattern here isn't about fancy technology; it's about small teams (3-4 engineers) building specific tools that do actual work. The companies succeeding treat AI like infrastructure, not like a science experiment.

2. Your AI Advantage Will Disappear Faster Than You Think

Surveys of AI founders reveal a brutal truth: technical advantages in AI last months, sometimes weeks. Apple learned this the hard way—they're sitting on $133 billion in cash but still can't ship competitive AI features. Meanwhile, Amazon went from zero robots to one million in 12 years. The lesson? Building AI tech is getting easier, but shipping it at scale is still hard. Companies winning long-term aren't building better models; they're building better systems to deploy, monitor, and iterate on AI quickly.

3. Stop Building AI Models, Start Renting Them

Most AI projects fail because teams treat them like software projects. That doesn't work. AI needs constant experimentation, retraining, and measurement, more like biological systems than code. The companies getting this right aren't building their own models; they're renting foundation models and customizing them. This isn't just about saving money. It's about focusing engineering effort on the problems that actually matter to your customers instead of trying to recreate what OpenAI already built.

4. AI Security Isn't Optional Anymore

OpenAI's new ChatGPT agent can access your email and files autonomously. That's powerful, but it's also terrifying from a security perspective. Traditional security tools can't keep up when AI operates at machine speed across multiple systems. Companies are discovering shadow AI everywhere—over 12,000 AI apps already catalogued, with 50 new ones appearing daily. The organizations getting ahead of this aren't trying to block AI; they're building systems that let people use AI safely. That's becoming a real competitive advantage.

5. You Need to Know If Your AI Actually Works

Most teams deploy AI with "vibe checks"—they try it, it seems decent, they ship it. That's not enough anymore. Companies like Harvey built specialized evaluation frameworks and discovered their domain-specific AI achieved 74% of lawyer-quality work, crushing general-purpose models. Microsoft's Tay chatbot and Meta's Galactica failed spectacularly because they skipped proper evaluation. The teams winning are building systematic ways to measure accuracy, safety, and relevance before their AI embarrasses them in production.

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Signals from the Edge #7