{"id":3545,"date":"2026-04-12T10:01:00","date_gmt":"2026-04-12T08:01:00","guid":{"rendered":"https:\/\/kraftscharling.dk\/?p=3545"},"modified":"2026-03-31T18:03:23","modified_gmt":"2026-03-31T16:03:23","slug":"what-could-trigger-a-break-in-the-ai-bubble-2-effect","status":"publish","type":"post","link":"https:\/\/kraftscharling.dk\/en\/hvad-kan-udloese-en-brist-i-ai-boblen-2-effekt\/","title":{"rendered":"What could trigger a break in the AI bubble? \u2013 2 \u2013 effect"},"content":{"rendered":"

AI is one of the most transformative technologies of modern times, with a potential on par with the railway, electricity and the internet. AI not only increases productivity, but also reshapes decision-making processes, organisations and ultimately power structures<\/a>. For this reason, AI also follows a historically recognisable pattern: high expectations, massive capital allocation, and uncertain timing of returns. That dynamic is well known, but what is new is the scale, the speed and the concentration. So what could trigger a break in the AI bubble?<\/em><\/p>\n\n\n\n

This is the second of two blog posts on the topic.<\/p>\n\n\n\n

There are also risks related to value creation \u2026<\/h2>\n\n\n\n

AI also risks becoming a commodity. For most users, the differences between systems such as ChatGPT, Claude and Gemini are relatively marginal. This increases the risk that monetisation potential declines further, particularly for OpenAI and Anthropic. Gemini, by contrast, can leverage Google\u2019s targeted advertising model to support self-financing.<\/p>\n\n\n\n

There are also signs that top AI talent is increasingly moving between companies. This mobility contributes to the spread of know-how across actors, reducing durable competitive advantages and increasing the likelihood that the technology shifts from differentiated innovation to broadly accessible capacity. As a result, both open-source and smaller players can close the gap more quickly.<\/p>\n\n\n\n

AI may therefore follow the pattern of previous technologies: shifting from differentiated innovation to widely available capacity, a form of commodity. Value creation then moves rapidly from model development to distribution, data and customer access. This favours companies with existing cash flow and loyal user bases, rather than players such as OpenAI and Anthropic.<\/p>\n\n\n\n

\u2026 and the financing model is showing signs of strain<\/h2>\n\n\n\n

AI investments depend on both new capital and the continuous refinancing of existing investments. If this chain is disrupted, even fundamentally viable projects can become illiquid. Projects with long-term potential may be forced to scale down or halt, as their time horizon can no longer be financed.<\/p>\n\n\n\n

Until now, there has been an abundance of risk-tolerant capital. Big Tech has invested heavily, but private credit has been particularly aggressive. This includes large institutional investors such as SoftBank, as well as more traditional private equity funds.<\/p>\n\n\n\n

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There are now increasing signs of distress<\/a> among private credit investors. In 2026, firms such as Ares Management and Apollo Global have begun limiting withdrawals by their clients. Such restrictions are always a concern, and similar measures have been introduced by Blue Owl and Cliffwater.<\/p>\n\n\n\n

This makes the system sensitive to:<\/p>\n\n\n\n