Deutsche Bank states bluntly that the "honeymoon period" for the artificial intelligence industry is over. While AI technology itself will endure, 2026 is poised to be the sector's most challenging year, with a convergence of three major themes—disillusionment, dislocation, and distrust—subjecting the market to a severe test.
According to analysis, Deutsche Bank's latest report from January 20 indicates that this year will be a "life-or-death" period for independent AI model companies. Despite OpenAI securing investment from SoftBank at the end of 2025 and pursuing a high valuation, its projected cash burn of up to $170 billion this year, coupled with concerns over the sustainability of its business model, has sparked market anxiety. Particularly noteworthy is Apple's decision on January 12 to choose Google over OpenAI as its AI product partner, a move that underscores the risks of shallower moats and narrowing pathways for independent model makers when competing with tech giants.
The International Monetary Fund (IMF) has also issued warnings, noting that a reassessment of productivity growth expectations from AI could lead to decreased investment, trigger sudden adjustments in financial markets, and subsequently impact household wealth. Deutsche Bank's analysis suggests that aside from a few companies like Anthropic, which have established a foothold with robust cash flows and enterprise-grade products, smaller independent firms (such as Perplexity) may ultimately be unable to escape acquisition by giants, as they struggle to bear the accelerating costs of computational power.
The report characterizes the market sentiment for 2026 as dominated by "disillusionment," "dislocation," and "distrust." As enterprise users transition from pilot projects to production, technological limitations, high costs, and supply chain bottlenecks are replacing the initial blind optimism, forcing investors to confront the significant gap between "AI concepts" and the translation into substantive profits.
A wave of disillusionment is hitting: enterprise applications are facing "jagged frontiers." Deutsche Bank's report points out that while generative AI is transformative, its full impact will not be immediately realized. As pilot projects move into production phases, enterprise users are confronting inherent limitations, including insufficient accuracy and difficulties handling unpredictable real-world environments. For many CEOs, the focus is on substantive revenue growth or systemic operational fixes, rather than mere efficiency gains in specific areas like code generation.
Although venture capital firms like Sequoia proclaim that "Artificial General Intelligence is here and now," Deutsche Bank contends that for the average user, the current AI experience is more akin to "switching to a more comfortable saddle" than upgrading from a horse to a tractor. Even benchmarks like OpenAI's GPTval Leaderboard suggest AI can handle work tasks, but when dealing with the complexities of the real world, AI remains a tool requiring human guidance and interpretation.
The difficulty of enterprise integration is severely underestimated. The report emphasizes that most companies lack the high-quality data and integration capabilities required for general AI, not to mention the monitoring mechanisms needed in sensitive sectors like finance or healthcare. The so-called "jagged frontier" effect persists, where AI excels at certain tasks but performs surprisingly poorly at others. This constrains large-scale adoption; while usage is higher in large enterprises, the path from pilot to positive return on investment remains tortuous for the broader market.
Supply-demand dislocation: computational bottlenecks and the funding dilemma for independent vendors. 2026 will also be a year of severe "dislocation," where imbalances between demand and production capacity will intensify. Data from hyperscale cloud providers like Google shows that token usage grew over 100-fold in the 18 months leading to last October. However, the complexity of the supply chain means a shortage of any one among hundreds of thousands of components—from high-bandwidth memory and energy supply to engineering talent—could derail the entire process.
Against this backdrop, financial pressure will concentrate on private AI companies. Deutsche Bank notes that while hyperscale cloud providers can fund investments through operational cash flows, independent model manufacturers face significant financial challenges. OpenAI has committed to spending $1.4 trillion on data center construction in the coming years, and until an IPO, its massive capital expenditures and relatively shallow moat place it in a precarious position.
Deutsche Bank specifically highlights that as inference and video generation become more commonplace, the marginal cost per interaction continues to rise. For smaller independent companies, the accelerating cost of computational power is almost unbearable. The report predicts that, with the possible exception of Anthropic due to its lower cash burn rate and developer-favored products, other independent vendors may be forced into the arms of Hyperscalers before the year ends.
A crisis of trust: geopolitical gamesmanship and escalating regulation. "Distrust" will be the third major theme permeating 2026. Legal disputes surrounding copyright, privacy, and data center location will surge, and public anxiety about AI misuse will escalate from a whisper to a roar. Furthermore, concerns about job displacement are intensifying; a Stanford study titled "Canary in the Coal Mine" points out that since the launch of ChatGPT, the employment rate for new graduates in AI-related roles has relatively declined by 16%.
Geopolitical competition will further complicate the market environment. Deutsche Bank states that the AI race will have a profound impact on investment. The emergence of China's open-source model DeepSeek demonstrates the possibility of extracting value from lower-cost chips, with China expanding its lead in cheap, accessible open-source models, which appeals to cost-sensitive users.
Simultaneously, the US is attempting to maintain ecosystem dominance through export controls, such as permitting Nvidia to export its H200 chips. However, the博弈 among governments promoting AI self-sufficiency is escalating. This battle for global standards, combined with the enforcement of the toughest provisions of the EU's AI Act, will force multinational tech giants to navigate the regulatory minefield with extreme caution throughout 2026.