A successful AI transformation is our shared responsibility

Nineteen theses as a picture of a profound transformation

I condensed nineteen theses from my notes on all the key notes and personal conversations I had the opportunity to hold during the Big Bang AI Festival 2025. Nineteen theses about the increasingly rapid and far-reaching development of artificial intelligence. Nineteen theses that will have a significant impact on the day-to-day work of managers in the next 10-15 years.

Since the picture that emerges from this is one of such profound economic, social and political transformation, I asked Perplexity Pro to validate these theses for me before I draw further conclusions for my clients and for my own work.

Here are the nineteen theses including the respective evaluation by Perplexity Pro (the corresponding sources are at the bottom) followed by my preliminary conclusion:

Thesis 1: The full force of the changes caused by AI will not reach us in 20-30 years, but in 2-3, i.e. 2028-29.

🟡🟢 The thesis has been partially confirmed: Numerous current analyses predict strong technological and economic momentum as early as the end of the 2020s at the latest, meaning the main potential of AI breakthroughs will set in much faster than in classic technology waves.

Thesis 2: The first outlines of an AGI are already recognizable in the fog of developments and will become reality in 5 years at the latest.

🔴🟡 This forecast is highly controversial. Individual researchers and stakeholders such as James Betker or OpenAI consider AGI to be conceivable by 2030, but many experts consider the challenge to be greater and estimate the time horizon to be 2040 or later. There is no majority among researchers in favor of AGI by 2030.

Thesis 3: Before the next economically and socio-politically stable phase begins, we have 10-15 years of sometimes rough transformation ahead of us.

🟢 Confirmed: Most current economic studies assume a prolonged period of intensive change over at least a decade. Model calculations speak of 2035 as the reference point for stabilization.

Thesis 4: The majority of people and their managers are not yet prepared for this transformation.

🟢 Correct: There is widespread agreement in politics, society and companies that most business participants and managers are currently not sufficiently prepared for the depth of the transformation.

Thesis 5: Business leaders have a key role to play in guiding people through the transformation, i.e. leveraging potential and at the same time taking a responsible approach.

🟢 That's right. This is emphasized in many studies and opinion pieces and is considered a key challenge.

Thesis 6: AI does not reduce company costs by 10%, but to 10%.

🔴 This statement is misleading or incorrect. AI sometimes leads to significant efficiency gains, but not to a reduction in total costs to 10% of the initial value. Conventional expert estimates assume potential savings in the range of 10-40%, rarely more, but never by a factor of 10.

Thesis 7: While the IQ of ChatGPT 4 was 100-120, the IQ of ChatGPT 5 is 150 and far above the IQ of 99% of all people.

🔴 This is a myth/marketing statement and not scientifically proven. There is no valid method for objectively determining the IQ of AI models, and all proposed numerical values are highly controversial.

Thesis 8: The demographic gap in 2030 is 7 million people, which the labor market in Germany lacks.

🟢 The figures vary, but the order of magnitude is realistic - current estimates range from 5 to 7 million workers missing by 2030.

Thesis 9: Robotics with integrated AI is advancing into many areas of application (such as medicine, production, household...) in order to close the demographic gap.

🟢 Correct: This is the current state of development and a key political and economic goal in Germany.

Thesis 10: The German robotics manufacturer Neura is technologically on a par with the best in the world.

🟡🔴 There are no indications that Neura is a leading international German robotics provider. The market leaders in robotics are still companies such as KUKA, Festo, Universal Robots and international corporations. The thesis is probably wrong or at least greatly exaggerated.

Thesis 11: Interest in Germany in AI and the creation of its potential in society, business and politics is growing massively, perhaps even exponentially.

🟢 That's right, almost all surveys and trend reports show a massive increase in interest in and use of AI in Germany.

Thesis 12: Despite all the prophecies of doom, Germany as a business location is in a good and increasingly better position to master the transformation in time.

🟡🟢 This is an optimistic assessment for the basic and future scenario, but one that is covered by studies (such as PwC, IW) - provided that regulatory and organizational adjustments are successful.

Thesis 13: There is a sense of new beginnings everywhere.

🟡🟢 This is a subjective assessment, but it is often supported by the media and studies. The tenor of the „AI leap“ in business and institutions is evident in many places.

Thesis 14: There is potential for new business models in almost every area.

🟢 Confirmed: AI is considered a cross-sectional technology and an enabler for new business models across industries.

Thesis 15: AI consumes more energy than we can produce.

🔴 Wrong: AI consumes 1.5% of our energy production today and will require between 3-6% of our energy production by 2035. AI will become a relevant consumer, but not a global energy monster.

Thesis 16: The risk of unemployment caused by AI mainly affects young people at the beginning of their careers, as AI is increasingly taking over standardized entry-level jobs.

🟢 Confirmed: Much of the work that can be automated by AI involves traditional entry-level jobs; younger people (and job seekers without work experience) are particularly affected.

Thesis 17: Regulation of AI (e.g. through international treaties or laws such as the EU AI Act) is already less of a hindrance in the medium term and more of an accelerator of successful business models.

🟢 This thesis is becoming increasingly popular. Many studies and market observers expect clear, confidence-building rules (e.g. EU AI Act) to prepare markets and accelerate innovation.

Thesis 18: The use of AI will now also accelerate strongly in small companies (66% of all large companies and 36% of all small companies use AI).

🟢 Almost correct: The latest Bitkom figures and other studies (2025) speak of around 50% companies with AI activity in total, but the proportion of large companies is actually over 60%, and around a third of small companies.

Thesis 19: The motivation for companies to invest in AI is not a question of saving on personnel, but one of gaining efficiency and mitigating the shortage of skilled workers.

🟢 Current studies (IDW, Bitkom) confirm this. It is primarily about facilitating routine tasks (85% of all companies), supporting complex tasks (70%), improving quality (65%) and mitigating the shortage of skilled workers (37%). This is followed by personnel savings (29%) and raw material and energy savings (20%).

A non-conclusion

In the great square of political, social, technological and economic challenges worldwide, any look into a 10-year glass ball is pure gambling. Even a look two years into the future is likely to be out of date in just two weeks. And the nineteen theses mentioned above, including the assessments by Perplexity, are of course anything but complete; for example, the long list of major risks and opportunities that will arise for us all is missing.

I therefore do not want to draw any opinion-forming conclusions from these theses. But perhaps they will be a good stimulus to get into conversation with other people in your environment, with your team or other managers and to consider together how you will shape the future.

In this global upheaval, it is above all the managers who (and their advisors) to lead people into the future with a great deal of responsibility.

References