What 2025 Will Bring for AI

From AI for science to digital twins and robotics, here’s what world-leading AI experts were excited about at AI House Davos.

We just got back from another year of AI House at Davos, a project our Merantix Group co-initiated with other great partners to drive forward progress in artificial intelligence.AI House is four fast-paced days of debate and collaboration on the most important technology in the world today — and I wanted to share some of the themes that were on the minds of the many investors, academics, entrepreneurs, and policy-makers that we had the fortune of meeting.

For this newsletter, I asked for some help from my colleague Thomas Wollmann, the CTO of Merantix Momentum, our AI solutions sister organization.

Here we go.

1. AI for Science

AI for science is a standout trend for us in 2025.

One billion year breakthrough

AI-assisted scientific discovery has been making breakthroughs behind the scenes for years, but its impact became impossible to ignore in 2024. A clear sign of this shift can be seen in last year’s Nobel Prizes, where AI wasn’t just a supporting tool—it was a major player in its own right.

In physics, John Hopfield and Geoffrey Hinton applied principles from physics to machine learning, laying the foundation for modern AI systems. Hopfield developed an associative memory network inspired by atomic interactions, allowing AI to reconstruct patterns by minimizing energy states. Hinton used the Hopfield network as the foundation for a new network that uses a different method: the Boltzmann machine. The model autonomously identifies patterns in data using statistical physics and enables AI to categorize images and even generate new ones, powering key advancements in deep learning.

AI also played a defining role in the Nobel Prize for Chemistry, with Google DeepMind’s AlphaFold2 solving the decades-old challenge of protein structure prediction. The project uses AI to predict the 3D structure of proteins from their amino acid sequences, a problem that had stumped researchers for 50 years. The impact of AI here can’t be understated. Mapping all 200 million known proteins would have taken over a billion years with human effort alone.

Beyond being a research milestone, this discovery is transforming drug development and improving drug design.

Currently, machine learning models interpolate existing knowledge—analyzing and recombining what is already known. But if AI gains the ability to design and conduct its own experiments, as we believe it will, artificial intelligence will generate entirely new knowledge, pushing scientific discovery far beyond human limitations.

Weather

We live in a world where weather is becoming increasingly unstable, with devastating effects on communities across the globe. Extreme storms, heatwaves, and other climate-driven disasters are harder to predict using traditional methods, which rely on historical data and physics-based models. Now, engineers no longer have to depend on guesswork and a handful of prototypes; AI allows them to process thousands of data points to create more accurate forecasts, helping to predict and protect against extreme weather events.

One example of this is NVIDIA’s CorrDiff. This correlator diffusion model works in two stages: first, predicting the mean of a fine-resolution weather field, then refining it with missing details. Trained on high-resolution radar data from Taiwan’s weather authorities, CorrDiff can scale extreme weather predictions from a 25km resolution down to just 2km, offering a significant improvement over traditional forecasting methods.

DeepMind’s GenCast is a diffusion-based generative model designed to enhance both everyday weather predictions and extreme event forecasting. Optimized for the Earth’s spherical geometry, it learns to generate future weather patterns based on real-time atmospheric conditions. Trained on four decades of historical weather data, GenCast can produce 15-day forecasts in just eight minutes on a single TPU, significantly outperforming the European Centre for Medium-Range Weather Forecasts' (ECMWF) leading ensemble system. DeepMind’s own testing reportedly showed GenCast was more accurate than ECMWF’s ENS in 97.2% of cases, underscoring the transformative potential of AI in weather prediction.

From insurance, to catastrophe prevention and every-day planning - weather prediction will become orders of magnitude more accurate in 2025.

2. Distributed Model Training

Much of today’s economic development is powered by technology. Technological advancement is powered by compute. Access to affordable compute will soon become an existential necessity for most nation states.

But that computing power is currently hoarded by a small handful of companies in the U.S. and China, where data centers can boast over 100,000 GPUs. In contrast, Europe’s largest has around 2,000. That’s why nations are scrambling to build technological independence with distributed model training.

Distributed AI model training is a process that accelerates the development of artificial intelligence models by dividing the training workload across multiple computing nodes, often referred to as worker nodes. These nodes operate in parallel, enabling the system to handle larger datasets and more complex models than would be feasible on a single machine.

Spreading the load across geographically disparate clusters of GPUs potentially solves the need for building new data centers and also avoids too much power being centralized in one location or country.

We think that the German Federal Agency for Breakthrough Innovation SPRIND’s initiative to develop decentralized methods of training powerful new AI models through open competition and funding is indicative of where this trend will continue to develop.

Either way, the big players have a wide technological and financial moat and the only way to break this stranglehold on innovation is to collaborate and decentralize.

3. Agentic AI

Agentic AI is one of the most talked-about advancements in artificial intelligence today, marking a major leap from passive assistants to autonomous decision-makers that bridge the digital and physical worlds.

Until now, AI has largely been limited to collecting and analyzing information, offering insights but leaving execution to humans. The next generation of AI agents changes that—by directly taking over hardware, making transactions, and automating real-world actions. This shift unlocks entirely new capabilities, turning AI from a tool that suggests actions into one that independently carries them out, fundamentally reshaping workflows, businesses, and even job markets.

Computer use agents will likely become standard in operating systems and office applications, much like how Excel macros once revolutionized workflow automation. But unlike macros, today’s AI can execute multi-step tasks autonomously—researching and booking travel, completing financial transactions, and updating schedules without human intervention. By handing AI control over personal and corporate computing power, users gain extreme efficiency, but at the potential cost of many traditional jobs.

At the same time, vertical AI is replacing human-driven, unstructured processes. This shift is particularly disruptive to roles that involve repetitive, manual tasks—often handled by interns, junior employees, or consultants—such as transferring data between systems or troubleshooting inefficiencies.

4. Synthetic data

2025 could be the year where AI models trained on vast amounts of real-world data start making a tangible impact across industries—from robotics and manufacturing to supply chain logistics and warehouse management.

One of the key technologies driving this transformation is the digital twin, a virtual model of a real-world environment that updates in real time using AI and sensor data.

Digital twins allow companies to test different scenarios, predict failures before they happen, and refine processes without disrupting operations. Nvidia’s Cosmos project is a prime example of this evolution, training AI models on 20 million hours of video and 9 trillion parameters to help machines understand the physical world with high accuracy. Paired with Nvidia’s Omniverse, which provides a simulation environment for industrial AI, Cosmos enables companies to develop and test automation solutions before deploying them in real-world settings.

This approach is expected to cut the time required to plan a new warehouse in half, while reducing manual labor and operating costs by up to 50%. In industries like retail, manufacturing, and consumer goods—where supply chain disruptions have caused major financial losses in recent years—this kind of AI-driven automation offers a way to increase resilience and efficiency.

This convergence of technology comes at a time where businesses are looking for savings anywhere they can find them. We’re feeling a lot of energy from business leaders and innovators to make 2025 the year when AI really shakes up the industrial sector.

5. Human-in-the-Loop Systems

Human-in-the-loop (HITL) systems will play a critical role in AI adoption as chat and voice interfaces mature - significantly impacting how millions of people work every day.

HITL systems leverage advancements in large language models (LLMs) to provide a bridge between human expertise and machine capabilities. This democratization of AI tools allows users without technical backgrounds to solve problems and deploy solutions, reducing dependence on programmers or data engineers.

These systems will not only empower users but also shift workflows in industries such as data engineering, customer service, and creative work. The ability to interact with machines in natural language removes a significant barrier to adoption, allowing businesses and individuals to incorporate AI into their processes seamlessly.

This year, we will also pass the point where people can tell whether or not they are speaking to an AI or a human. Many processes will likely be a blend of the two, especially customer service. AI can handle most processes, but humans can step in when expertise is needed.

6. Industry-Specific Models

AI is becoming more specialized, with models built for specific industries rather than just general use. In 2025, with large models being more and more commoditised this trend will accelerate, shaping fields like healthcare and biotechnology, where precision and efficiency are key.

In healthcare, AI-assisted screening is set to become even more common in 2025. Vara, a company backed by Merantix, has developed an AI tool that helps radiologists detect breast cancer more accurately. A study in Germany involving nearly 462,000 women found that AI-assisted screening detected 6.7% of cancers, compared to 5.7% with traditional methods—equating to one extra cancer found per 1,000 screenings. Crucially, AI didn’t increase false positives, meaning fewer unnecessary tests and less anxiety for patients.

In biotechnology, AI is reshaping how new materials and medicines are designed. Cambrium is using AI to create proteins that aren’t just effective but also easy to produce at scale. Instead of searching for naturally occurring proteins, they design new ones from scratch, optimizing them for human needs. One example is vegan human collagen, which Cambrium has developed for skincare products. As AI-driven protein design expands in 2025, it could transform industries like medicine, food production, and sustainable materials, making lab-created proteins a mainstream solution.

Let’s start building

AI in 2025 won’t just assist—it will act. It’s solving billion-year problems in science, reshaping weather forecasting, and optimizing industries with digital twins and automation. We’ll see governments around the world scramble to develop distributed model training to tap into much needed sources of growth and agentic AI will have an enormous effect on everything from work to daily life.

We’re excited to see what this year brings in AI. What are we missing? Are you building something that helps solve the world’s most pressing issues with AI? Let us know!