Key highlights:
- Deutsche Bank predicts 2026 will be a difficult year for the AI industry, with multiple operational and technical risks.
- Companies like OpenAI face rising costs, infrastructure shortages, and public skepticism, despite large investments.
- Geopolitical tensions, regulatory challenges, and talent shortages could further slow AI adoption and profitability.
Deutsche Bank has issued a report warning that the favorable period for the artificial intelligence industry may be coming to an end. Analysts predict that 2026 will be marked by disappointment, supply chain disruptions, infrastructure challenges, system failures, and growing distrust in the AI sector.
Analysts Adrian Cox and Stefan Abrudan highlighted three key themes that will shape the industry’s development this year. Despite continued investment and technological progress, companies will face significant hurdles on the path to mass adoption.
Disillusionment with technology reality
Generative AI may eventually change the world, but not in the near term. As pilot projects transition into full commercial use, corporate users are encountering fundamental limitations of the technology. These include insufficient accuracy, difficulty applying AI in unpredictable real-world conditions, and cost inefficiency compared with human labor in many use cases.
While technology advances rapidly in areas such as programming, its benefits are often most visible to early adopters in Silicon Valley rather than corporate leaders expecting meaningful revenue growth or systemic operational improvements from AI implementation.
Tools like Anthropic’s Claude Code have drawn attention from experts, and some venture capital firms have suggested that artificial general intelligence is emerging. However, for most users, current AI tools feel more like incremental upgrades rather than revolutionary replacements, highlighting the gap between expectations and reality.
Most companies lack the integration capabilities or high-quality data needed for broad AI adoption. A more serious issue is the lack of robust monitoring and control for critical roles in sectors such as finance and healthcare. Employee readiness for change and customer adoption remain additional barriers to successful deployment.
Infrastructure pressures and public distrust
The report also emphasizes growing infrastructure challenges in 2026. AI relies on one of the most complex global supply chains in history, and any disruption, whether in components, high-speed memory, power supply, or skilled engineers could slow progress.
Monthly AI token usage has surged more than 100-fold over the past 18 months, showing strong demand but increasing pressure on capacity.
Cloud providers continue to invest heavily in data center expansion, and debt financing remains available. Yet shortages in high-speed memory and concerns about power supply, including water used for cooling could delay deployments and increase costs for laptops, cars, and smartphones.
Other potential bottlenecks include limited network capacity, rare-earth element restrictions, and pending regulations under the EU AI Act, as well as geopolitical tensions around Taiwan.
Public distrust is also intensifying. Lawsuits related to copyright, privacy, data center locations, and AI misuse are increasing. For instance, British police accused Microsoft Copilot of misrepresenting a threat, leading to a ban on Maccabi Tel Aviv fans from attending a European match, highlighting the challenges of AI reliability.
Concerns about job displacement are growing as well. A widely cited Stanford study found a notable decline in employment among recent graduates in AI-impacted fields since ChatGPT’s launch, though other research suggests occupational changes may precede widespread AI adoption.
Nonetheless, AI is increasingly being seen as partially responsible for employment stagnation, though company claims regarding job losses should be approached with caution.
Geopolitical tensions and industry evolution
The U.S.-China competition in AI could also shape the landscape. Chinese open-source models, like DeepSeek, offer cost-effective alternatives, while U.S. export policies and chip supply dynamics influence global AI deployment.
Deutsche Bank concludes that 2026 will be a test of strength for the AI industry, with its ability to overcome technical, operational, and public perception challenges determining its future trajectory.
Source:: Deutsche Bank Announces End of Its Artificial Intelligence Honeymoon