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If your startup sells an AI product built on OpenAI or Anthropic, your supplier is now your rival. Both just launched billion-dollar consulting arms that send their own engineers into the same big companies you're selling to, building AI systems on the very models you license from them, the same way AWS watches what sells on its platform and then ships its own version.
A startup inside Airtable ran its own launch almost entirely on its own agents, from cold outreach to the landing page to qualifying leads. The bet is that the model is now a commodity and the durable product is the harness around it, the skills, memory, and checks that make each run better than the last. The founders getting real leverage aren't aiming agents at busywork, they're rebuilding core workflows, so choose your scaffolding as carefully as your model.
AI has crossed from hype into real consumer spending. More than 40 of the top 100 App Store apps now have AI features built in, and they grew sales four times faster than the rest of the top 100, inside a $1.4 trillion market. What's pulling the money is that an AI upgrade pays off the instant you buy it, a higher limit or a smarter coach, so the spending is landing on everyday apps like photos, health, and shopping, not just the chatbots.
Many companies now have the technology they need to go faster. The blocker is the systems companies design to prevent things from happening. Rapid innovation can't happen in environments where employees aren't trusted. Companies need employees with agency - the ability to make decisions on behalf of the organization - or their AI innovation efforts will be wasted.
Software requires ongoing investment, but this is a feature, not a bug or mistake. Software is a depreciating intellectual asset, and companies need to recognize and budget for this. Every line of code in software needs to be supported until the last customer deletes it. One-time project-based budgets ignore this reality, which inevitably leads to financial and technical crises.
Search is moving from ten blue links to AI answers, and the usual SEO hire, a channel manager who files tickets and reports rankings, is built for the old game. What works now is product thinking, understanding what a person actually means when they ask, then shaping the product so an AI model trusts it enough to cite. AI made the technical grunt work cheap, so hire for judgment, the person who can walk into a product review and defend a roadmap, not a keyword report.
WordPress 7.0 shipped last month. WordPress.com sites have been automatically updated. Changes include post previews, navigation overlays, responsive block visibility, more design pieces, a font manager, and much more. The release also ships with AI features designed to assist in the editor, as well as real-time collaboration on selected plans.
Google has rolled out an upgrade to its 'Take notes for me' feature in Google Meet that gives users more control over what gets captured, making it easier to track outcomes after meetings wrap up. Users can now toggle specific sections, like Decisions or Next Steps. The new Decisions tracker helps to categorize outcomes like 'Aligned' or 'Needs Further Discussion'. The Summary section has been tightened up to be more concise and scannable. Screenshots of the changes are available in the article.
Skills are small bundles of instructions handed to models so they can run the same workflows every time. Teams will often notice they keep doing the same things every time, so they write the workflow down as a skill and stop doing it by hand. Over time, this can result in a huge library of skills. These skills will eventually become obsolete as models improve. Developers need to monitor skill development so they don't build up and become a second code base that nobody uses.
The market always wins, even if a great team has a great product. You can't change a market, but you can always pivot to a more favorable sector. This requires a high level of execution and can mean essentially starting the company all over again.
YC CEO Garry Tan recalls running low on Tesla charge in rural Texas and settling for 99.1% of the 2024 eclipse, only to learn that 99% is not 99% of the experience but zero percent of it. The same boundary applies to founders he now meets weekly with identical decks since AI collapsed the cost of building: the wedge, screenshot, and TAM math all match, but only some founders can carry the idea across into a real artifact where users feel the totality.
Paul Graham likes to show founders the odds their own valuation actually requires, and they're absurdly low.
Hanover Park has recreated core accounting systems for AI-native operation, enhancing efficiency by significantly reducing labor.
The people around you would help more than they do, they just can't see what you actually need.