Jan Stevens

For those tracking the America closed source vs China open weight model debate: In an initial pilot on modernizing an application from PHP to Next.js, Opus 4.8 with 8090’s Software Factory was simultaneously 1.4× cheaper and 1.5× faster than Opus 4.8 alone. Pairing our Software Factory with the cheaper GLM 5.2 model cut costs 16.4×, though it ran 3× slower than Opus 4.8 alone. Results are directional (n=1 per arm), and next steps are to rerun with proper controls and 10–15 buyer-relevant legacy modernization tasks developed with our Sales and GTM teams. More to come but the important question it raises is why any American public company with shareholders burning money on closed source models when the open weight ones are so much cheaper? We will rerun this on American open source models next (aka Nvidia)…

The Nemotron family just passed 100M downloads! Huge thank you to the community building with us and showing what’s possible with open models. Cheers to OSS 🍾

Central bankers now fear the AI gold rush could seed the next major financial shock. Bank for International Settlements (BIS) just issued one of its sharpest warnings yet about debt building behind the AI boom. The danger is not AI itself; the danger is building a leveraged supply chain around revenue that has not yet proved durable. The risk is that if AI demand disappoints, data-center spending could slow, borrowers could struggle to repay, and stress could spread from tech into credit markets. AI demand pushed hyperscalers to spend heavily on chips, data centers, and power capacity, and that spending supported growth, trade, and easy financial conditions while equity investors priced in years of high earnings growth. Debt changed the shape of the boom because hyperscaler bond issuance topped $100B in 2025, while off-balance sheet vehicles shifted data-center obligations toward private credit funds, insurers, and other non-bank lenders. Circular financing adds another weak point because chipmakers, hyperscalers, AI labs, and compute providers can fund each other while also booking future sales from each other, which can make real demand harder to read. A capex slowdown could hit suppliers first, then credit markets, then households, because US stocks make up about 64% of the MSCI Global index and household equity exposure is higher than in past cycles. Private credit raises the systemic risk because direct lenders have quadrupled AI and IT exposure in 5 years to about 15% of portfolios, while some retail-facing funds already face redemption pressure. AI can still deliver real productivity gains, but the financing stack now assumes that delivery arrives fast enough to support huge fixed costs.

Be leopold > wife is chief of staff at Anthropic > get insider info at the dinner table > learn that Anthropic is going to be buying $20b of compute in Australia > become the largest shareholder of the two companies building compute in Australia > $SHAZ $IREN Beast.