Original: Summary — Europe 2031 - Europe 2031 Project. Summarized by Claude AI on Jun 15, 2026.

The Take

Europe is sleepwalking into a structural position where it owns almost none of the AI stack, accesses frontier models only on American terms, and can be economically hollowed out and geopolitically cornered by 2031 — not because its leaders are venal or stupid, but because the institutional habits that built the Union (consensus, procedure, deferred hard choices) are catastrophically mismatched to the speed of AI development. The piece is a five-year scenario that doubles as a policy argument: the current European response is an order of magnitude too small, aimed at the wrong goals, and the only viable path out runs through leverage — being indispensable — not through the comforting but hollow rhetoric of “sovereignty.”

Also: Anthropic’s 80% code claim, and Claude’s quiet enterprise share. From: Broadcom's Investment in Spring to Combat AI-Fueled Security Challenges in the Enterprise As the chart shows, there’s been a huge jump in CVEs for Spring - this is what’s happening everywhere, you know. My work, Tanzu, has been focusing on this and has changed how they handle these rollouts. Now customers can get early access to the secured builds for Spring so they can deploy them as quickly as possible to fix these security problems.

Enterprises, IDC says, remain largely unsold on Anthropic’s Claude models, with only 19 percent using them extensively and 25 percent actively evaluating them. OpenAI and Google are better represented in enterprises, with about 42 percent and 38 percent of organizations" This means there’s lots of “headspace” to grow revenue, or disappointing and incomprehensible ROI, i.e., enterprises are finding it hard to figure out what to do with AI. Or both!

Right now, many companies already have the technology they need to go much faster. The blocker is company systems that are mostly designed to prevent things from happening. The power is centralized and all the team members are treated like a risk vector. Exhausting approval cycles, super tight boundaries on roles, unbreakable title-based hierarchies, and a whole tier of middle managers whose main job is to keep everyone in line.

One enterprise harness maker says the competing enterprises harness makers either suck or are non-existent. When you go to San Francisco and talk to them, their basic vibe is ‘we don’t have to solve your problem today because tomorrow you’re going to go away and all your problems are going to be solved,'" Karp charged. “It’s largely religious.” … “the product doesn’t actually work and it’s very expensive.” To that end, he added, most of the things that Anthropic brags about in public, for example, are successful because they’re “running on Palantir,” Karp charged

A census of 25,000 YouTube and TikTok videos finds pro-AI content outnumbers anti-AI 3:1, dominated by memes and productivity hacks rather than abundance or doom. Resisters care most about creative theft, not the x-risk or data center concerns driving elite debate. From Memes > Doom: How TikTokers and YouTubers See AI - Free Systems (Substack), June 2026. Adopters (pro-AI content) Content type % AI memes & effects 43% Career / productivity 25% Creative tools 15% Education and learning 8% AI companionship 4% Breakthrough science 1% Resisters (anti-AI content) Content type % Creative theft 22% Deepfakes and misinfo 19% Jobs displacement 13% I hate AI (general) 13% X-risk 8% Energy / data centers 6% Note: percentages don’t sum to 100 in either column - the piece doesn’t account for the remainder, presumably smaller uncategorized buckets.

Original: Your AI Bill Is A Context Problem by Forrester. Summarized by AI on Jun 11, 2026. The AI bill shock hitting enterprises isn’t a pricing problem you can cap your way out of - it’s the metered cost of your own knowledge not being machine-readable, forcing agents to rebuild missing business context on every loop. Capping spend treats a value problem as a price problem and kills the experimentation the spend was meant to fund.

Replace multi-year forecasts with real-time discovery of operational friction. Instead of a five-year requirement for a “targeting system,” identify the bottleneck–like a three-hour targeting approval process. Set a goal–like reducing the approval process to 30 minutes. And empower a team to solve it. In this Kessel Run example, the requirement was an outcome, not a feature list." Bryon Kroger 🔗 Rapid software delivery is possible inside DoW — Software Factory 2.