From experimentation to economic viability: Deeptech 2026 as a decisive turning point

The end of chatbots? Will autonomous AI agents take over the global economy from 2026 onwards?

While the years 2023 to 2025 were characterized by a global hype surrounding generative AI, chatbots, and theoretical possibilities, 2026 marks a fundamental shift: DeepTech leaves the realm of scientific curiosity and transforms into a hard economic infrastructure. The era of "proof-of-concept" is over; now begins the phase of industrial scaling, in which technology is no longer judged by its novelty but ruthlessly by its economic viability.

This transformation is driven by a quiet but radical revolution: the transition from assistive intelligence to autonomous agents. AI systems are no longer merely tools awaiting human input, but are becoming independent market players that make decisions, negotiate resources, and optimize processes—often more efficiently than any human. This new autonomy, however, is changing the rules of the game for the entire industry. It shifts the focus from pure computing power to energy efficiency, makes electricity the most valuable resource, and elevates "trust" from a soft factor to a technically verifiable necessity.

For Europe as a business location, and especially for German SMEs, this scenario presents a volatile mix of risk and opportunity. Squeezed between progressive regulations like the AI ​​Act and a lack of sovereign hardware infrastructure, companies must now decide how to compete in a world where data sovereignty and energy availability determine market leadership. The following text analyzes in depth how these dynamics will unfold in 2026 and why DeepTech is the crucial lever for future competitiveness.

From the lab to the balance sheet: Why DeepTech will force a radical shift towards profitability in 2026

DeepTech, or “profound technology,” refers to a class of companies and innovations based on fundamental scientific breakthroughs and groundbreaking engineering innovations. Unlike digital business models, which often optimize existing processes (such as a new delivery app), DeepTech aims to create fundamentally new technological capabilities. These innovations, often characterized by long development cycles, high capital requirements, and a strong focus on intellectual property such as patents, have the potential to revolutionize entire industries and address major societal challenges in areas such as health, climate, and energy.

A prime example of the dynamism and importance of DeepTech is Artificial Intelligence (AI). However, a clear distinction is crucial here: DeepTech in the AI ​​context means advancing the core technology itself – be it through the development of new algorithms, the training of fundamental base models (such as GPT), or the creation of specialized hardware. This contrasts with the mere application of AI, where existing models are used to create a specific product, such as a customer service chatbot. While both are valuable, the essence of DeepTech lies in creating the underlying, groundbreaking technology that pushes the boundaries of what is possible.

The last frontier before mass production: Autonomous systems as genuine business players

The coming year, 2026, marks the transition of an industry from the phase of theoretical possibilities to the phase of operational necessity. After years of pilot implementations and fragmented trials, artificial intelligence, highly specialized computer architectures, and decentralized infrastructure systems are now converging to create a new level of production capacity. The era of laboratory experiments and proof-of-concepts is ending – the era of scaling is beginning.

The central turning point lies in the fundamental transformation of AI systems: they cease to be assistants and become autonomous decision-makers. These systems no longer negotiate according to predefined rules, but make decisions based on contextual information, conduct complex negotiations, and orchestrate processes entirely independently. Experts refer to this as the transition from reactive intelligence to proactive agentics. This transformation rests on three pillars: reliable mechanisms for data verification, newly created trust architectures, and extreme hardware efficiency.

The economic potential of this transformation is exceptionally vast. Analysts at the market research firm Gartner predict that by 2028, nine out of ten business transactions between companies will be initiated and executed by autonomous AI systems – a cumulative business volume of over $15 trillion, entirely administered by machines. The resulting reduction in transaction costs and friction losses could generate savings of at least 50 percent in service-oriented business models by 2027. This is a critical signal for German industry and the European economic area: companies that fail to develop this autonomous capability will be competitively squeezed out.

Several parallel economic shifts are driving this autonomy revolution. The first is a reassessment of what “economic efficiency” means. The age of large, general-purpose models is over—not because they are obsolete, but because they are uneconomical. The economic metric that matters is “cost per operational unit” or “cost per inference,” not “model size.” Inference costs for language models at the performance level of GPT-3.5 fell more than 280-fold between November 2022 and October 2024. This dramatic cost drop was not the result of a single breakthrough moment, but rather a combination of hardware efficiency gains of 30 percent per year and energy efficiency improvements of 40 percent per year.

The second is the dismantling of the “cloud-centralized paradigm.” Artificial intelligence infrastructure is becoming distributed. Instead of performing all computations in enormous mega-data centers, specialized hardware architectures are emerging, enabling computation close to the data source. The market for edge AI (intelligence at the edges of networks) is growing at an average annual rate of 21.84 percent and is projected to increase from its current value of just under $9 billion to over $66 billion by 2035. This is far more than a hardware trend—it is a fundamental restructuring of how the global economy handles data.

The third shift is a redistribution of power within the infrastructure itself. The decades-old model of the hyper-centralized cloud, dominated by a handful of mega-corporations like Amazon Web Services, Google Cloud, and Microsoft Azure, will be complemented and partially replaced by decentralized, regional, and national models starting in 2026. Organizations are now investing heavily in geographically distributed data centers, colocation solutions within their own regions, and locally operated AI infrastructure. This is neither purely technical nor purely economic in motivation—it is a geopolitical statement. This transformation is materializing in legal frameworks such as the EU AI Act and the upcoming Cloud and AI Development Act, which demand sovereignty over data and infrastructure.

The trust layer: A new market for old problems

While previous phases of the AI ​​industry focused on scaling model parameters and accelerating computing processes, 2026 deals with a different existential question: How can you trust a system that even its creator cannot fully understand?

This isn't a philosophical question—it's an immediate business necessity. An autonomous system that makes wrong decisions or can be manipulated is a risk, not an advantage. That's why entirely new layers of infrastructure are emerging, which technically anchor trust. This trust infrastructure includes systems for the automated verification of AI-generated content, protocols for the cryptographic authentication of device identities, and mathematical proofs of the integrity of data flows. The business reality is that this layer of trust is becoming the new economic foundation.

Companies are now investing heavily in public key infrastructures (PKI), decentralized identity management systems, and blockchain-based authentication mechanisms. This isn't exotic—it's an immediate operational necessity. Security firms point out that traditional password-based authentication mechanisms are perfectly adequate for autonomous AI systems operating at machine speed. An AI capable of detecting systematic weaknesses in authentication can perform lateral movements across networks at exponentially higher speeds.

European regulation has driven this development – ​​not unintentionally. The EU AI Act mandates full compliance for high-risk systems from August 2026 onwards, with a long list of requirements: technical robustness, top-level cybersecurity, proven accuracy, and continuous human oversight. For general-purpose systems – i.e., large language models – specific transparency requirements and reporting obligations will apply from August 2025 as soon as systemic risks are identified. This regulation doesn't merely create compliance burdens – it creates new markets. Companies that offer trust infrastructure – certificate management, data authentication, and model integrity verification systems – are becoming critical suppliers.

At the same time, alternative financing models for AI are emerging, based on decentralized systems and blockchain technologies. Platforms like SingularityNET and others enable the trading of AI models, computing resources, and datasets on open, decentralized markets, coordinated by smart contracts and rewarded with crypto tokens. These systems are not yet mainstream and have significant technical weaknesses, but they address a growing market demand: access to specialized AI without dependence on US or Chinese platforms.

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The infrastructure itself is becoming an economic bottleneck

A counterintuitive but crucial phenomenon is shaping the near future: While semiconductor chips are plentiful, electricity is becoming the most critical resource. The next generation of AI models requires exponential increases in computing power. Training a single large language model already consumes several megawatts of electricity per day. Real-time inference for millions of users demands a stable, continuous, and massive power supply.

This is already creating a geographical realignment of global infrastructure. Companies are relocating their AI clusters to regions with reliable, affordable electricity. Tech firms are entering into direct contracts with nuclear power plants or purchasing energy capacity from wind farms. This development has not only technical but also macroeconomic consequences. The profitability of AI operations is directly linked to electricity costs. Countries or regions with abundant, inexpensive electricity are becoming global AI superpowers, while others are marginalized.

The technical answer is heterogeneous computing. Instead of homogeneous GPU clusters—where all computation runs on identical graphics processors—companies combine specialized hardware: CPUs for traditional computing, GPUs for parallel processing, TPUs for specialized tasks, and specialized accelerators for individual model types. This maximizes efficiency and minimizes power consumption per operation. But it requires entirely new orchestration systems, new programming models, and newly developed expertise. The market for AI infrastructure software—tools for orchestrating heterogeneous resources—has exploded and has itself become a critical bottleneck.

One particular case deserves mention: AI inference. Once general language models are trained, they need to be used millions of times a day. Traditionally, this is done on GPUs—the same processors used for training. But for pure inference, GPUs are inefficient. They consume far too much power for the actual computational work. Analysts show that CPUs—conventional processors—often deliver 19 percent better throughput for AI inference while using only 36 percent of the power of a GPU-based system. This may sound like a technical detail, but it represents a fundamental reshaping of infrastructure economics. Inference, not training, accounts for 85 percent of all AI workloads. A shift to CPU-based inference would have global energy implications.

Sovereignty, regulation and decentralized economy

The European and German regulatory landscape has transformed in the last 18 months. Data protection laws originally intended for user data—the GDPR, NIS-2, and the upcoming Cloud and AI Development Act—are now becoming infrastructure regulations. Essentially, these laws state: You cannot store your AI infrastructure in black boxes that control you. You must know where your data is, how it is processed, and who has access to it.

This is leading to a restructuring of what "cloud computing" means. Pure public cloud solutions—delegating everything to AWS or Google Cloud—are becoming regulatoryly impossible for many companies. Instead, hybrid cloud models are emerging: Sensitive data remains on-premises or in European-hosted infrastructure; less sensitive workloads can be outsourced to the global cloud. Companies are now investing in internal AI capabilities, building small data centers, and partnering with European cloud providers.

This leads to the profitability of domain-specific language models. A general-purpose, broad language model is highly inefficient and expensive for specialized applications—finance, medicine, law. A model specifically trained on medical data is more accurate, cheaper, easier to monitor, and simpler to classify for regulatory purposes. Gartner expects that by 2028, more than 50 percent of all generative AI models used by companies will be domain-specific. This represents a shift from centralized, general-purpose innovation to decentralized, specialized value creation.

The reality of autonomy in industry and trade

For years, factories and warehouse management have been the testing grounds for autonomous systems. By 2026, pilot projects will become standard operation. Driverless transport systems – Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) – are already deployed in the millions in warehouses and factories. Industrial robots with AI-controlled vision systems perform complex assembly tasks. The cumulative investments in robotic process automation and collaborative robotics are now delivering measurable economic results.

But the more substantial transformation is more subtle: the autonomous optimization of production processes themselves is becoming operational. Intelligent Manufacturing Execution Systems (MES) analyze real-time data from machines, warehouses, and supply chains and dynamically adjust production plans. Machine learning on production data enables predictive maintenance (maintenance is performed before breakdowns occur), optimal capacity utilization, and a massive reduction in scrap rates. Companies are already reporting efficiency gains of between 10 and 15 percent and reductions in unplanned machine downtime of between 20 and 30 percent.

The retail sector is undergoing similar transformations. Intelligent inventory management systems no longer rely on historical sales data, but rather on real-time signals—local events, weather patterns, demand velocity—to optimize stock levels. Large retail chains already have AI-driven distribution systems in place that calculate personalized inventory levels for each individual store. Retailers report significantly lower warehousing costs, fewer shortages (lack of stock), and reduced obsolescence losses on inventory.

The economic model itself is shifting. Traditional automation requires massive capital expenditures – factories must be rebuilt for robots, warehouse logistics must be redesigned. This limits access to automation to large companies. But new models – Robotics-as-a-Service (RaaS) – transform capital expenditures into operating costs. A medium-sized company can now rent robots instead of buying them, and can test automation without long-term commitments. This democratizes automation – and opens up market segments that were previously inaccessible.

The geopolitical and energy context

One of the overlooked economic realities: Future competitiveness is not limited by GPU capacity—there are enough chips. It is limited by electricity. This is not theoretical—it is already operational reality. Cloud providers report that they have thousands of opportunities to buy new GPU clusters but no space to connect them because local power grids are overloaded.

This leads to a new geographical logic. Data centers are located where a secure and economical power supply is available. Iceland, with its abundant geothermal energy, and Norway and Sweden, with their hydropower, are becoming global AI hubs. Countries with unstable or expensive power grids are being squeezed out of the global AI infrastructure competition. This has profound geopolitical implications: the energy sector is now AI infrastructure.

The US is investing heavily in energy infrastructure and regional data center clusters. China is doing the same. Europe is fragmented. Germany and continental Europe have conceptual advantages—high regulatory standards, technical expertise, an existing industrial base—but a major structural disadvantage: fragmented energy infrastructure, high electricity costs, and a lack of centralized planning for AI computing needs. This is not a problem that technology companies can solve—it requires national and European strategy.

The European-German position: Regulation without power

Germany and Europe find themselves in a paradoxical strategic situation. The European Union has enacted the world's first comprehensive regulatory framework for AI – the AI ​​Act. This framework sets high standards for security, transparency, and accountability. This regulation creates potential competitive advantages – European companies that can meet these standards will become "trust leaders" in global markets. Businesses and consumers seeking trust in AI systems may prefer European solutions.

But without the appropriate infrastructure, this advantage is limited and unstable. Europe lacks comparable AI infrastructure providers like AWS, Google Cloud, Alibaba Cloud, or the new Chinese alternatives. European companies rely on external infrastructure—mostly American or Chinese cloud providers. This means that European companies lack the physical control to guarantee compliance with the standards demanded by European regulations. This creates a genuine paradox of trust.

The strategic answer: European AI factories and sovereign AI infrastructure. Initiatives exist—the EU's AI computing program, the announcement of European chip factories, German and French investments in national data centers—aimed at closing this gap. But time is of the essence. 2026 will be crucial. If 2026 passes without substantial European AI infrastructure capacity going online, Europe will fall further behind, both technologically and strategically.

An important opportunity is opening for German SMEs. The majority of medium-sized companies cannot invest in independent, global AI infrastructure. However, they can deploy AI agents on their own hardware or in a European, regulatory-compliant cloud infrastructure. This requires entirely new service categories – enabling AI capabilities for small teams, consulting on data sovereignty, and custom training of models on proprietary data – which do not yet exist in this form.

The position of change: Quo Vadis Deeptech in 2026

In summary: 2026 is the year in which deep tech transitions from laboratories and pilot projects to mass production and market scale. Technologies experimented with between 2023 and 2025 are now being implemented on a massive scale. Economic benchmarks are dropping dramatically. Efficiency gains from autonomous systems are translating from theoretical to operational, measurable economic improvements.

At the same time, the critical bottlenecks are becoming apparent. It's not hardware—chips are plentiful. It's not software—AI models are increasingly accessible. The bottlenecks are: electricity (where will the next infrastructure be located), trust infrastructure (how will AI reliability be guaranteed), and data sovereignty (how do I maintain control). These questions are changing how infrastructure is planned, how regulation is designed, and how companies make their strategic AI investments.

2026 will be the year in which autonomy becomes the norm. This is no longer speculation or science fiction – it will be the new operational and economic basis of the global economy.

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