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The gap between individual AI productivity and team performance

As a product manager at Upsun with a computer engineering background, Kateryna Dvornichenko had spent months researching competing tools in the agentic development space, running tests, comparing features, and building a picture of where the market was heading. She realized the tools were impressive, but something kept standing out. "Collaboration was not the strong point of any of them," she says. "Everyone stays on their own machine with their own setup.".

AI can write database code fast. Here's how to keep it safe before production.

AI can write database schema changes in seconds, but nothing should reach production until it's validated, tested, and approved. In this discussion, Ken Muse (GitHub), Steve Jones (Redgate), and Huxley Kendall (Redgate) show how a governed pipeline keeps AI-generated database changes safe without slowing teams down.

From Attention to Action: How Digital Systems Shape Demand

Between the moment someone types a query and the moment they act on the result, a sequence of independent systems runs: query rewriting, intent classification, retrieval, an auction, a click-through prediction, a page render measured in hundreds of milliseconds, and a routing decision about where the resulting tap goes. None of those systems is designed to persuade. They are designed to estimate, price, and allocate.

The End of Browsing: How AI Is Rewriting Digital Discovery

Sometime in 2025, a quiet threshold was crossed: the majority of Google searches in the United States began ending without a single click. Data from SparkToro and Similarweb puts roughly 58.5% of US searches as resolving on the results page itself, and for news queries the zero-click rate climbed from 56% to 69% in a single year. The search box still works the way it always has. What changed is that we've stopped leaving it.

Why More Technology Is Becoming a Service Instead of a Product

A growing number of technology purchases no longer end at checkout. A phone gains new AI features months after launch, a vehicle receives software updates from the cloud, and a security camera may lose important functions if its online service disappears. The physical product still matters, but increasingly it is only the visible edge of a much larger system.

Your next internal developer platform is a library of agent skills

What happens when AI agents become direct users of your infrastructure? Michael Kutsch, Staff SRE and Team Lead for Cloud Foundations at PostHog, argues that the next internal developer platform may be a library of agent skills. Instead of forcing every task through a portal, his team is giving agents structured context, reusable workflows, and deterministic scripts they can call when reliability and governance matter.

Why Most Enterprise AI Agent Programs Overspend, and What Actually Fixes It

Enterprise AI agent programs overspend because leaders optimize token costs, which represent just 20-25% of variable run costs. Human oversight accounts for 70-75%. The real fix is reducing exception rates and embedding governance directly into agent execution from the start.

AI gross margin: how AI spend hits SaaS profitability

AI gross margin is what remains of SaaS profitability after inference, model routing, and AI infrastructure land in cost of revenue. The numbers have moved. AI products averaged 45% gross margin in 2025 and are projected near 53% in 2026, against the 70% to 85% that SaaS built its valuations on. The compression is real, measurable, and manageable for companies that can see their cost to serve.