AI Newsletter

According to current data, only 5% of companies achieve real returns from AI investments, while 42% abandoned their AI projects in 2025 – more than double compared to the previous year. In response, a new field called 'Tokenomics' is emerging, designed to make the return on AI spending measurable. The contradiction between billion-dollar investments and actual value is the central tension in the industry in 2026.

The five largest tech conglomerates are heading toward a combined negative cashflow of $125 billion, driven by massive AI infrastructure investments. This marks a historic capital outflow that raises questions about the sustainability of the AI investment cycle. Investors and analysts are watching whether spending can be covered by revenue growth – or whether a correction looms.

Anthropic CEO Dario Amodei voiced sharp internal criticism that new employees primarily joined for financial reasons rather than conviction in the company's safety mission. Combined with leaked meeting snippets, a picture emerges of growing cultural erosion at the leading safety lab – precisely at a time when Anthropic is rated as the best model with 92% probability on Polymarket. The tension between commercial pressure and idealistic founding mission is considered a structural risk for the entire safety ecosystem.

Microsoft has agreed in a multi-billion dollar deal with Mistral AI to build European AI infrastructure with thousands of NVIDIA Vera Rubin GPUs – the largest compute deal Europe has ever seen. Nevertheless, Mistral lags technologically behind Anthropic, OpenAI, and Google DeepMind, as well as Chinese providers, which structurally endangers Europe's AI sovereignty aspirations. The deal is politically sensitive: It uses US capital and US chips to secure European independence from US AI.

A viral Reddit debate shows: AI has drastically reduced the cost of product development, but simultaneously flooded the market with identical products – making real demand harder to find than ever. The most-liked comment puts it perfectly: 'AI lowered the cost of building, not the cost of finding real demand.' This corresponds with the broader trend that 42% of all enterprise AI projects fail: technical feasibility and market relevance are diverging further apart.