🦔Andreessen Horowitz laid off most of its 34 person team that helps its startups recruit talent, Axios reports. The few who are left will move into individual investing groups like growth and crypto, and the firm declined to comment. My Take I think a16z is bracing for a tougher stretch. In the past week CoreWeave’s bonds hit distress levels, Firmus pulled its IPO, and Character AI filed for bankruptcy three years after a16z led its $150 million round at a $1 billion valuation. The biggest venture firm in tech no longer paying a central team to help its companies hire means it expects those companies to need fewer people, or to have less money to pay them. Venture firms collect management fees for years no matter what, so a cut like this usually reflects what they expect next. Some of it is AI taking over recruiting and the work itself, but I think most of it is money getting harder to raise for anyone outside the biggest AI labs. I expect more firms to quietly shrink their support staff over the next few months, and the startups that make it through will be the ones that can pay for their own hiring out of revenue. Hedgie🤗 axios.com/2026/10/14/andrees…

This piece is out! 4 sites, 10 days, and 6000 words on the politics of data centers in the Midwest: jasmi.news/p/no-data-centers… I talked to union leaders and real estate brokers, activists and local officials, politicians from Kathy Hochul to Abdul El-Sayed. A few takeaways: - Activists are as pissed at their local officials as at AI companies - The bigger the dollar signs, the more skeptical people are - Past corporate failures (Foxconn, GM, DTE) prime community distrust - Even data center proponents regret the NDAs - Media narratives turn local grievances into national movements - Residents fear getting left with the bag if the bubble pops The AI buildout is showing up in an environment of extreme distrust, and it slots perfectly into existing worries about risky tech bubbles & dark money in politics. Rationally speaking, I often agreed with the proponents; emotionally, I empathized with the opposition more. It was also just very fun (WI is esp lovely) and I highly recommend similar field trips to SF friends

A PE firm asked us to look at an AI company before close. The pitch deck said "proprietary AI platform." The data room showed $4.2M ARR growing 40% year-over-year. The management presentation had 14 slides on their "foundational model architecture." We opened the codebase and found an OpenAI API wrapper with a system prompt and a React frontend. The "proprietary model" was gpt-4o with temperature set to 0.2. Six hundred lines of glue code any senior engineer could rebuild in a weekend. They were asking 12x revenue. The PE firm walked. Buyers are deploying capital faster than their diligence teams can evaluate what they're buying. Across hundreds of engagements, I keep running into four gaps that standard due diligence doesn't cover: [1] Model dependency. If the target runs on a third-party model (OpenAI, Anthropic, Google), you need to know what breaks when that provider raises pricing, deprecates the version, or a competitor ships the same capability for 80% less. Valutico calls this "agentic substitution risk." I call it the thin-wrapper problem. If you can swap the underlying model in an afternoon, the "proprietary AI" is worth the React frontend it's wrapped in. [2] Training data liability. Bartz v. Anthropic settled at $3,000 per book for 500,000 pirated works, $1.5 billion total. You acquire an AI company, you inherit how they sourced their training data, the datasets they scraped, the copyrighted material they ingested without permission. EU AI Act violations carry fines up to 7% of global turnover, and most data rooms don't contain a single document on training data provenance. [3] Talent concentration. In a traditional SaaS company, losing two engineers is a setback. In an AI company, the model pipeline lives in three people's heads. I've seen shops where one ML engineer's departure would mean the company can't retrain or update its core product. Standard HR diligence counts headcount but doesn't map who holds institutional knowledge about the model architecture. [4] Integration failure rates. RAND analyzed 2,400 enterprise AI initiatives and found 80% failed to deliver intended business value. MIT found 95% of generative AI pilots never scale to measurable P&L impact. Acquiring an AI company and plugging it into a portfolio company's stack is a multi-quarter engineering project carrying that same 80% failure rate. PE firms I talk to have a financial diligence playbook and a commercial one. Almost none have a technical playbook built for AI codebases. You open the spreadsheet and see ARR and growth. You don't see whether the company built what they're selling. We've been inside enough of these to know the difference.

UNC is moving to a bidshift model where extra shifts are bid on (employee A says I'll do it for $X, employee B says I'll do it for $Y) instead of assigned. This is a prime Cogwork example. Pricing pressure and a marketplace that incentivizes wages down. The initial reception has been pretty poor but we'll see if that ultimately changes the policy or not.