August leaves behind a signal that is hard to ignore. Artificial intelligence is no longer appearing only as an emerging technology or as a standalone investment category. It is becoming embedded in core processes across complex industries, creating new layers of health data and forcing us to rethink the physical infrastructure needed to sustain its growth.

In insurance, virtually all insurtech funding is concentrating in AI-focused companies. In healthcare, some of the most relevant startups are capturing data, simulating clinical decisions and embedding those capabilities into professionals’ day-to-day work. In energy and infrastructure, the expansion of computing is driving growing demand for data centres, grid capacity and the assets needed to support them.

The question is no longer whether AI will have an impact on the real world. The question is which specific problems it can solve, within which processes, and with what level of reliability.

AI is concentrating insurtech investment and is moving into critical processes

Gallagher Re’s report on the second quarter of 2026 shows just how concentrated capital has become. Insurtech companies raised $2.44 billion during the quarter, the highest level since Q2 2022. Of that funding, 99.1% went to companies focused on artificial intelligence.

The figure reflects the appeal of the technology, but also a more selective market. The number of early-stage deals fell by 51.8%, while large rounds accounted for a significant share of total investment. Capital is not disappearing. It is shifting towards companies with commercial potential, access to data and the ability to integrate into real insurance operations.

Having an advanced model is not enough. The technology has to be embedded in processes with demanding requirements around security, traceability and oversight. adesso’s acquisition of omni:us confirms this shift from the corporate side. The German company has brought the Berlin-based startup, which specialises in AI-powered claims management, into its group with the aim of progressively integrating its technology into the in|sure Ecosphere platform.

The transaction represents more than a technology acquisition. AI is becoming part of the architecture of insurers’ core systems and is being applied to a critical process such as claims handling. It can automate routine tasks, classify documentation and support claims professionals in complex cases. It can also improve the consistency and traceability of decisions.

In regulated sectors, the advantage does not come simply from having access to a powerful model. It comes from embedding that model in workflows where reliability and accountability matter just as much as efficiency. AI is therefore moving from being a category within insurtech to becoming a technology layer integrated into critical insurance processes.

Healthtech is beginning to generate the data medicine needs

Healthtech is evolving in a similar direction, but with one important difference. The challenge is not only to apply artificial intelligence to existing data. It is also to generate new sources of health information.

In the early years of digital health, much of the innovation focused on moving existing processes onto a platform: booking an appointment, holding a consultation or managing a clinic. The next stage is more ambitious. Companies are beginning to capture physiological data continuously, simulate clinical decisions and embed intelligent assistants into professionals’ everyday work.

Onalabs is a good example of this transition. The Spanish company has raised €9.3 million to accelerate the industrialisation and commercialisation of its wearable devices for non-invasive sweat analysis. Its technology makes it possible to monitor certain biomarkers and physiological parameters without relying on invasive procedures.

The value proposition lies in turning the body and everyday activity into a continuous source of data. That information can help identify gradual changes, anticipate deterioration and enable more personalised monitoring.

QuantHealth is approaching the same shift from a different angle. The company has closed a $45 million Series B round led by Qumra Capital, with participation from Sanofi Ventures, Pitango HealthTech, Bertelsmann Healthcare Investments and other investors. Its platform uses artificial intelligence to simulate clinical trials before patient recruitment begins.

Simulation makes it possible to optimise study design, assess different patient populations and anticipate potential outcomes before committing clinical and financial resources. AI is not only being used to analyse data once a trial is underway. It is also helping improve the decisions that determine how the trial is designed from the outset.

Eholo offers an example that is closer to everyday clinical practice. The Spanish management platform for mental health practices and centres has welcomed CRB Health Tech and GCO Ventures into its shareholder base. It has more than 21,000 professional users in Spain and managed over one million psychology sessions in 2025.

The new funding will help accelerate the development of MIA, its artificial intelligence assistant. MIA transcribes sessions, generates clinical documentation and allows professionals to query a patient’s history using natural language.

These cases point in the same direction. Healthtech is moving towards a richer data layer: capturing data in new ways, using it to anticipate outcomes and integrating it into clinical practice. In a regulated environment, competitive advantage does not lie in technology alone. It lies in the combination of product, evidence, compliance and real adoption by professionals and patients.

AI is becoming physical, and the next constraint may be the power grid

The expansion of artificial intelligence is creating new demand for physical infrastructure. Every model, application or agent requires computing capacity, data centres, energy, cooling and connectivity.

As AI adoption grows, part of the opportunity is shifting from software to the assets that make it possible. The question is not only who develops the most advanced models. It is also who solves the bottlenecks that limit their deployment.

In August 2026, Emerald AI raised $150 million in a Series A round at a valuation of $1.05 billion. The company develops software designed to make data centres more flexible and adapt their energy demand to grid conditions.

The problem is becoming increasingly relevant. The expansion of AI is driving up electricity consumption in data centres, while building new generation capacity and expanding power grids requires years of investment and planning. The answer is not simply to produce more energy. It is also to use existing capacity more effectively.

Emerald AI’s technology aims to help data centres adjust their consumption and coordinate more effectively with the power grid. These facilities are no longer managed only as technology assets; they are increasingly being managed as energy assets as well.

MassMutual Ventures’ investment thesis points in the same direction. The firm has launched a second $150 million climate technology fund focused on companies applying artificial intelligence and climate technology to energy infrastructure, real estate, natural resources and digital systems.

The signal is clear. Artificial intelligence will not only create software companies. It will also drive solutions for managing energy, grid capacity, cooling, connectivity and resilience. Value is shifting towards companies that can address the physical constraints of a computing-based economy.

The next stage will be measured by AI’s ability to transform complex sectors

August leaves three converging signals. In insurtech, capital is concentrating in artificial intelligence companies and backing their integration into insurers’ core systems. In healthtech, innovation is moving towards the generation of richer data and its use in clinical decision-making. In infrastructure, the expansion of AI is creating opportunities around energy, data centres and physical assets.

AI is no longer being assessed only on its ability to generate answers or automate tasks. It is increasingly being assessed on its ability to integrate into complex processes, improve critical decisions and deliver measurable outcomes.

For GCO Ventures, this evolution reinforces a clear thesis. The most relevant opportunities are not necessarily found in the most visible applications. They also emerge in the technology layers that solve structural problems across insurance, healthcare and infrastructure.

Artificial intelligence is no longer simply arriving in the real world. It is becoming part of it – and, in doing so, it is becoming subject to its constraints.

If you found this analysis useful, come back to the GCO Ventures blog next month. We will continue exploring how technology, investment and strategic sectors are evolving together.