Gallup

Anthropic IPO Will Flag AI Backlash Risk

Anthropic is preparing an IPO prospectus that will explicitly list public backlash against artificial intelligence and data center construction as a material risk, sources told CNBC. The Claude creator, privately valued near $1 trillion, confidentially filed to go public in June and has been conducting “test-the-water” meetings with bankers and investors. In those meetings, the company’s CFO Krishna Rao faced questions about competition, margin pressure from open-source models, and the potential impact of slowed data-center development on revenue. Policymakers and voters in several states have pushed back on data center projects, and surveys show significant opposition to local AI data center construction.

Recycled Wastewater Can Cool AI Data Centers

A viral Liquid Death marketing campaign with Jason Kelce joked about using human urine to cool AI data centers, but experts say the underlying idea—using recycled wastewater for data-center cooling—is real and increasingly adopted. Engineers and water-reuse advocates describe technologies like membrane bioreactors, reverse osmosis and evaporative cooling that allow treated wastewater to substitute for potable water in industrial cooling. Local infrastructure limits adoption; Loudoun County (VA) data centers used about 200 million gallons of recycled water per day in 2025 (43% of local data-center usage), with the remainder from potable supplies. Industry actors including Meta are investing in wastewater infrastructure, and trade groups are pushing for tax credits to accelerate reuse projects.

Purpose of Software in the Age of AI

Michael Burnett argues that despite enormous increases in computing power and visible efficiency gains, software has not produced sustained macroeconomic productivity growth. Reviewing historical economic research and productivity data (citing Solow, Krugman, Gordon and others), he contends the purpose of software is to increase the density of information in digital outputs rather than to raise value output per hour. Burnett revisits the "productivity paradox," discusses Baumol's cost disease, and hypothesizes a "disengagement curve" driven by search/chat workflows. He warns that AI and LLMs will act as force multipliers for software production but are unlikely to change software's fundamental relationship to measured productivity, even as they create more digital output and new efficiencies.