AI 2030: Intelligence unleashes the future

Dear Readers,

2030 – that sounds like the distant future, but it is barely more than a handful of years away. Anyone looking at the development of artificial intelligence today can see an unstoppable upward trend: models are becoming larger, more powerful, and more expensive. Studies such as those by EpochAI are already talking about trillion-dollar budgets and gigawatt-scale power loads – dimensions more reminiscent of industrialized nations than of a single technology. The question is not whether AI will change our everyday lives, but to what extent.

In this issue, we look ahead: What will a world look like in which AI no longer just writes texts or generates images, but accelerates entire research projects, makes diagnoses, and prepares decisions? What opportunities does this present—and what risks if energy consumption, regulation, and social inequality do not grow at the same pace? This is precisely the tension we are exploring today.

All the best,

There are years that go down in history as turning points: 1450 with Gutenberg's printing press, 1879 with the light bulb, 1969 with the moon landing. 2030 could join this list – not because of a single moment, but because of the quiet but profound integration of artificial intelligence into almost all areas of life. AI is already being discussed as a “general purpose technology,” a key innovation that, like electricity or the internet, is transforming entire societies. But how far will this development go in the next five years?

The latest study by EpochAI, a research institute supported by DeepMind, offers a rare glimpse into the future. With detailed scenarios on computing power, data availability, and energy requirements, it paints a picture that is both fascinating and unsettling. The central question is: Will AI systems develop capabilities by 2030 that will transform them from mere tools into independent partners in science, business, and society?

EpochAI emphasizes that the basic dynamics of AI development can be summed up in one word: scaling. Since 2012, the computing power used for the largest models has doubled every 6 to 10 months – a curve that is steeper than the classic Moore's Law. By 2030, researchers expect so-called frontier models to be trained on a scale that seems almost absurd today:

- Costs: Training budgets of $100 billion are realistic; some scenarios even suggest over $1 trillion.

- Computational operations: 10^26 to 10^27 FLOPs (floating point operations) could be necessary – a hundred- to thousand-fold increase over GPT-4.

- Energy requirements: Estimates range up to 5–10 gigawatts of continuous load – enough to supply a medium-sized industrial region.

Such figures make it clear that AI is no longer a “garage innovation,” but has become a matter of global infrastructure. Cooling, chip manufacturing, and grid stability are becoming geopolitical issues.

In addition to computing power, the question of data remains crucial. EpochAI shows that the freely available supply of high-quality training data – books, scientific texts, Wikipedia – could be largely exhausted by the end of the 2020s. To circumvent this bottleneck, companies are relying on two strategies:

1. Synthetic data – AI systems generate new data sets, which in turn are used to train other models.

2. Specialized data sources – for example, medical archives, industrial sensor data, or simulations in physics.

By 2030, up to 50% of training material could be synthetic. However, this raises questions: What happens when AI only learns from itself? Researchers warn of a “model collapse” – a kind of inbreeding that could lead to distortions and loss of quality.

EpochAI outlines specific fields in which AI promises breakthroughs by 2030:

- Science: Language models could generate scientific software directly from prose, draft proofs in mathematics, or describe experiments in biology protocols. A chemist who currently needs weeks to simulate new molecules could see initial results in hours by 2030.

- Robotics: Advances in control, sensor technology, and model integration suggest that autonomous systems will take on more independent roles in logistics, agriculture, and care. The cost per robot hour could fall by more than 70%.

- Healthcare: AI-supported diagnoses will be used more widely. Studies predict that by 2030, around 30–40% of all clinical decisions will involve AI assistance – from X-rays to genetic screening.

- Economy: McKinsey estimates that AI could contribute between $13 trillion and $25 trillion to global economic output by 2030. In investment banks, up to 40% of today's activities are considered automatable.

The EpochAI study points to three major dangers that cannot be ignored:

1. Energy and the environment: Training in the exascale range currently causes CO₂ emissions equivalent to those of a small town. Without massive investment in renewable energies, the climate impact could be catastrophic.

2. Governance: The more powerful models become, the more important standards for security, transparency, and liability become. The EU AI Act is a start, but international agreements are still lacking.

3. Social inequality: Automation creates winners and losers. Historical examples – from industrialization to computerization – show that without redistribution, social divisions deepen.

The question of “artificial general intelligence” (AGI) remains. EpochAI believes it is conceivable that by 2030, models will reach a level of performance that surpasses human experts in some areas. But researchers urge caution: not only size, but also architectural innovations are necessary. Many experts estimate the probability of AGI in the next five years at 10–20% – a considerable, but by no means certain bet.

EpochAI's forecasts paint a picture of AI in 2030 that is characterized by extreme values: trillion-dollar budgets, gigawatt clusters, synthetic data streams. However, more likely than the vision of an omniscient “machine mind” is a scenario in which specialized systems take on central roles in science, industry, and medicine.

The initial question of whether AI will advance from mere tools to partners by 2030 can be cautiously answered in the affirmative. In clearly defined environments—laboratories, hospitals, factory floors—they will surpass us. But in more open contexts, humans remain indispensable: for creativity, tolerance of ambiguity, and value judgments.

The outlook is ambivalent: if energy issues are resolved, regulations established, and social tensions eased, 2030 could mark the beginning of an era in which humans and machines jointly open up new horizons. If these conditions remain unfulfilled, however, a decade of risks looms instead. The crucial question is therefore not what AI will be capable of, but how we will deal with this power.

Sources:

🔗 https://epoch.ai/blog/what-will-ai-look-like-in-2030?utm_source=chatgpt.com

🔗 https://engineering.nyu.edu/news/its-year-2030-what-will-artificial-intelligence-look?utm_source=chatgpt.com

🔗 https://business.uq.edu.au/momentum/4-ways-ai-will-revolutionise-the-world?utm_source=chatgpt.com 🔗https://www.businessinsider.com/how-ai-could-transform-investment-banking-wealth-management-2030-2025-7?utm_source=chatgpt.com 🔗https://www.axios.com/2025/05/21/google-sergey-brin-demis-hassabis-agi-2030?utm_source=chatgpt.com 🔗https://arxiv.org/abs/2508.00536?utm_source=chatgpt.com

The coming years promise a technological leap forward unlike anything the world has seen since electrification. AI will not only take over routine tasks, but also help us answer the big questions of science more quickly. Whether it's new drugs, more climate-friendly materials, or more precise medical diagnoses, systems that are still experimental today could become everyday tools for every researcher and doctor by 2030. The gains in speed and accuracy open up possibilities that were previously unthinkable.

A quiet revolution is also emerging in everyday life. Autonomous assistants that not only understand language but can also act in a context-aware manner will give people time – time for creativity, for relationships, for those activities that cannot be automated. If society and politics manage to shape these developments fairly, AI could bring not only prosperity but also more freedom: less work, less illness, more knowledge. An optimistic scenario that becomes more tangible the closer we get to 2030.