At Rise of AI 2024 in Berlin, Björn Bringmann, Managing Director and Lead at the AI Institute of Deloitte Germany, presents findings from Deloitte's quarterly AI surveys alongside outside data such as the Stanford AI Index. He argues that ChatGPT made AI tangible almost overnight despite remaining one of the most opaque models ever released, and reviews recent milestones including AlphaFold 3, open-source models like Llama reaching top-tier leaderboard scores, the EU AI Act, new Nvidia chips, and European generative AI players such as Mistral and Aleph Alpha. Using an AI ranking that scores countries on implementation, innovation and investment, he shows Germany performing reasonably on government strategy and operational metrics but slipping in research and commercialization, and highlights a large gap in R&D spending between the top US, Chinese and German companies. He walks through Deloitte survey data on German and global sentiment: perceived threat from generative AI to business models, high productivity expectations, shifting adoption priorities, talent and governance barriers, uneven employee access to GenAI tools, and views on whether AI will centralize or decentralize power. He closes urging German and European companies to move faster and pursue more ambitious "moonshot" projects.
Key takeaways
- ChatGPT made AI broadly tangible almost overnight despite remaining, in Bringmann's view, one of the most opaque models ever released.
- Germany ranks second globally on AI government strategy but fell in research (6th to 8th) and commercialization (8th to 11th) over two years.
- The top three US companies spend about 31 billion euros a year on R&D versus roughly 11-12 billion for Germany and China's top three.
- About 55 percent of companies give no more than 20 percent of employees access to a basic generative AI tool.
- A majority of respondents in Germany, Europe and globally expect generative AI to centralize rather than decentralize power.
Detailed notes
From explainability efforts to ChatGPT's overnight impact
Bringmann opens by noting that Deloitte now runs its AI survey every three months instead of annually to keep pace with the field. He observes that years of work on making AI models explainable, aimed at overcoming distrust of "black box" systems, produced steady but unspectacular progress, whereas ChatGPT made AI tangible almost overnight roughly a year and a half before this talk, even though it remains, in his view, one of the most intransparent models to date, showing that once people can use a system, transparency concerns become less central to adoption.
Recent achievements and European players
Bringmann highlights AlphaFold 3, calling it especially notable because it delivers direct impact in pharma and medical research rather than being another chatbot or voice assistant. He notes that Meta's open-source Llama model now scores close to the top proprietary, API-only models on leading benchmarks, arguing open-source progress will likely accelerate further given the larger number of contributors outside private companies. He also cites the EU AI Act as a flawed but valuable first step, new Nvidia chip generations expanding available compute, and Mistral and Aleph Alpha as Europe's two significant generative AI language-model players. He references German politician Christian Lindner publicly warning that Germany cannot afford to miss the AI wave as it has missed other technology waves, and cites statements from figures including Eric Schmidt and Amazon CEO Andy Jassy describing AI as still underhyped in the long run.
Germany's position: implementation, innovation and investment
Citing an AI ranking that scores countries on implementation (talent, operations, infrastructure), innovation (research and development) and investment (commercialization and government strategy), Bringmann says Germany ranks second globally on government strategy, largely due to the EU AI Act, and 13th on operational excellence, ahead of the United States at 28th, though behind China overall. Comparing the current ranking to two years earlier, he notes Germany improved on most measures but fell in research, from 6th to 8th, and in commercialization, from 8th to 11th, even though absolute investment rose, which he reads as Germany moving too slowly relative to competitors. He also cites average annual R&D spending among each region's top three companies: about 31 billion euros for the top three US companies, 12 billion for China, and 11 billion for Germany, and notes that Germany's top spenders are concentrated in automotive rather than software, with Volkswagen's R&D spending at roughly half of Alphabet's alone.
Survey findings: threat perception, priorities and barriers
Bringmann presents Deloitte survey results showing German business leaders initially feeling more relaxed than the global average about generative AI threatening their business models (about 50 percent seeing no threat versus a 36 percent global average at the end of the prior year), a gap that narrowed toward more realistic concern three months later. Across regions, roughly 90 percent of respondents expect a significant productivity increase from AI and about 80 percent expect substantial transformation within three years. Top adoption priorities shifted from efficiency and productivity, which fell from about 63 percent to roughly 50 percent in Germany, toward using AI for fraud and risk management, which rose from about 10-15 percent to nearly 30 percent, while using AI to accelerate software development fell from about 25 percent to 18 percent. The most cited barrier, in Germany and globally, is a shortage of skilled talent, followed by employee resistance and governance and regulatory concerns; German respondents' confidence in already knowing the right use cases dropped sharply between survey waves, which Bringmann reads as growing realism about how hard use-case selection actually is.
Uneven tool adoption and societal concerns
Bringmann reports that simple GenAI tool rollout inside companies remains highly uneven: about 55 percent of companies give no more than 20 percent of employees access to a basic generative AI tool, while only about 16 percent of companies give 80 percent or more of employees access. He also presents survey data on whether respondents expect generative AI to centralize or decentralize power: a large majority across global, European and German samples expect centralization, with the United Kingdom showing the least concern, though still leaning toward centralization; just over 30 percent expect decentralization. He notes public trust concerns are shifting from whether a model itself can be trusted to whether the companies using it can be trusted, illustrated by Stack Overflow users objecting, and being blocked from removing their answers, after learning their contributions were used to train a commercial large language model under terms they had clicked through without expecting that use.
Missed opportunities and recommendations
Bringmann recommends companies invest in people, set up governance properly, avoid fixating on a single use case, and adopt a beginner's mindset of strategic experimentation, adding that AI should not be siloed in one part of a company and that efforts should target broader transformation rather than isolated pilots. He argues companies should not wait for the technology to feel finished before starting. On missed opportunities, he notes that AI investment skews toward marketing and commercial applications with comparatively little directed at societal problems such as climate change, poverty or health, that German companies tend to move slowly because of internal pressure to justify investment upfront, unlike faster-moving US subsidiaries of the same firms, and that Germany lacks ambitious "moonshot" projects, citing early German self-driving car research in the 1980s that was not followed through. He closes by calling for Germany and Europe to move faster and pursue bigger bets.
Quotes
The future is here, it's just not very evenly distributed.
Björn Bringmann
It's really hard to shrink your way to success.
Björn Bringmann
If you want to be around in two years, you better start now.
Björn Bringmann
Questions answered in this talk
How does Germany rank internationally on AI compared to the US and China?
According to Björn Bringmann at Rise of AI 2024, Germany ranks second globally on AI government strategy and 13th on operational excellence, ahead of the US at 28th but behind China overall, though it has slipped in research and commercialization rankings over the past two years.
How widely have companies actually rolled out generative AI tools to employees?
According to Björn Bringmann at Rise of AI 2024, Deloitte survey data shows roughly 55 percent of companies give no more than 20 percent of employees access to a basic generative AI tool, while only about 16 percent of companies give 80 percent or more of employees access.
Do people expect AI to centralize or decentralize power?
According to Björn Bringmann at Rise of AI 2024, a large majority of survey respondents across Germany, Europe and globally expect generative AI to lead to further centralization of power, with just over 30 percent expecting it to lead to decentralization.