Beyond the AI Hype

Artificial intelligence is entering a new phase. While attention often focuses on the latest model launches, the bigger story is how organisations adopt AI and convert its capabilities into real productivity gains.

Seventy years after the 1956 Dartmouth workshop that gave birth to AI, the technology is moving from breakthrough innovation to large-scale deployment. For businesses, investors and policymakers, making sense of falling AI costs, intensifying competition and expanding adoption is becoming increasingly important.

The cost of intelligence is falling

Capabilities that were once confined to expensive frontier models are increasingly available through more affordable alternatives. This is lowering barriers to adoption and enabling organisations to apply AI across a growing range of activities, from research and analysis to software development and operational workflows. Businesses can increasingly choose from a broader ecosystem of models tailored to different needs and budgets.

Open-source AI is driving competition

At the same time, the rise of open-weight and open-source models is reshaping the competitive landscape. Many organisations are expected to adopt a combination of proprietary and open models, balancing performance, flexibility and cost. Increased competition is helping to accelerate innovation while giving users more choice.

“ Open AI models do not need to beat every proprietary model on every benchmark. They need to be good enough, inexpensive and ubiquitous.”

Adrian Cox, Senior Strategist, Deutsche Bank

What history tells us

AI's history offers an important reminder: transformative technologies rarely follow a straight path. While advances can happen faster than expected, their full economic impact often takes longer to emerge as businesses adapt processes, build infrastructure and develop practical applications. Today's AI boom may therefore represent an early stage in a much longer transformation.

Another lesson from history is that making a resource cheaper and more efficient can increase overall consumption rather than reduce it – a phenomenon known as the Jevons Paradox. As AI becomes cheaper and more capable, organisations are finding more ways to use it, driving demand for infrastructure, computing power and AI-enabled services.

“ When capable AI agents become cheaper, people do not use less AI, they find more ways to use it.”

Yi Xiong, Chief Economist, Deutsche Bank

Looking beyond the headlines

The AI story is no longer just about who builds the most powerful model. It is about falling costs, intensifying competition, accelerating adoption and the broader economic changes these forces may create.

The key lesson from 70 years of AI is clear: technological revolutions bring both opportunities and disruptions. AI may prove transformative, but its long-term impact will be shaped by a combination of innovation, infrastructure, competition and how quickly businesses put the technology to work.

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