An AI answers 377 open mathematical questions at once – and the media declare the end of mathematics. Leading mathematicians, however, see the beginning of a 'Mathematics 2.0'. What can be learned from this for the AI transformation of businesses.
The 'Math Apocalypse'
John McCarthy, one of the founding fathers of modern AI research, appears to be becoming a tragic figure in the history of mathematics 15 years after his death. A brilliant mathematician himself, he co-initiated the Dartmouth Workshop in 1956 and the first use of the term Artificial Intelligence (AI).
70 years later, mathematics is becoming the target, not to say the battlefield, of AI labs. An OpenAI announcement from October 6 states: "We are releasing a broad selection of new mathematical results achieved by an internal frontier model" (→ OpenAI Announcement). This selection includes, all at once, answers to 377 previously unsolved mathematical questions. In the media, this reflexively led to speculation about the end of mathematics.
From 'Math 1.0' to 'Math 2.0'
As is often the case in fundamental crises, mathematicians are now also asking themselves what mathematics actually is. One of the leading figures in this debate is Terence Tao, Professor of Mathematics at the University of California, Los Angeles (UCLA) and Fields Medal recipient.
In a lecture shortly after OpenAI's announcement (→ Math 2.0), he expresses the view that mathematics as such is not ending. Only a section of its history, characterized by the search for proofs ('Math 1.0'), is coming to an end. He sees the future of mathematics ('Math 2.0') in the creative use of AI by human mathematicians to expand the horizon of research:
„Once we adopt AI more imaginatively, the research frontier of mathematics will expand significantly.“
The Foundational Crisis Reaches Businesses
In business, the focus is less on proving theorems and more on competitiveness, growth, efficiency, and sustainability. Four years after the ChatGPT moment, many companies have recognized the potential utility of AI as a universally applicable technology and have made the AI transformation of their own business a top priority for leadership.
Given the capabilities of AI systems available today and expected in the near future, executives and department experts are also facing a kind of foundational crisis:
- What does HR mean when support and reporting can be largely automated?
- Is operational market research still needed when the insights machine becomes a reality?
- What does accounting do in the face of autonomous ERP systems?
- And what will IT do in the next-but-one stage of vibe coding?
For almost all professions, AI is a challenge, and for an increasing number of people, it is an imposition. In her book 'Brave New World' (Bequeme neue Welt), Sibylle Anderl interprets the word 'Zumutung' (imposition) in an unusual way: "I also find the word very beautiful because it contains 'Mut' (courage). It requires an additional impulse, sometimes indeed courage, because we are engaging with something new, unfamiliar, and do not know if we will even cope with it" (→ Interview Anderl in human).
Where True Courage Lies
True courage, however, does not lie in testing as many AI tools as possible as quickly as possible, ticking off AI use cases, and automating processes. As with Big Data, it becomes clear: more AI penetration does not automatically lead to better decisions, more satisfied customers, or greater efficiency.
On the contrary: often, the simple availability of AI-generated content leads to more 'noise' – and thus not to less, but to more uncertainty in strategic decision-making processes. Even AI-first companies like Klarna recognize that a human-less customer support department does not meet customer expectations (→ Klarna increasingly relies on human customer service again).
Just as Terence Tao sees a Mathematics 2.0 on the horizon, leaders with AI expertise and a vision for the future of marketing, sales, service, production, logistics, finance, HR, and IT are needed. This requires courage to change and an honest dialogue with all stakeholders. Because AI transformation means the parallel development of technology, organization, people, and culture.
Niklas Kühl, Professor of Information Systems and Human-Centered AI at the University of Bayreuth, puts it this way (→ Interview human):
„The big challenge is change management. How is the organization structured? What role do employees play? How do workflows change, and what IT strategy underpins it all? All of this must fit together. At the same time, new reports constantly emerge about what AI can now do and what problems arise. Adapting an organization under these conditions is difficult – also for the employees.“
Security Through Vision
„Security arises from a vision,“ says Florence Gaub (→ Interview Futures May 2026). With this, she provides a clue as to how leaders in politics and business can create an important prerequisite for willingness to change. This vision is one of the essential elements of the new HAI² Leadership Curriculum, which aims to accompany you and your company on your path to your '2.0'.
More about the HAI² Leadership Curriculum
About the AI Logbook
With the AI Logbook, I regularly report on current developments in AI technology and their relevance for businesses – from the perspective of Human-Centered AI. I look forward to your feedback, questions, and suggestions.
Sincerely, Siegfried Leiner