Has the artificial intelligence business model in Silicon Valley now collapsed?

Breaking free from the subscription trap (AI & cloud lock-in): Why companies are now relying on their own AI hardware instead of cloud APIs.

For a long time, an unwritten law prevailed in the technology sector: whoever owned the largest data centers and the most proprietary data controlled the artificial intelligence market. But this dogma, which cemented the dominance of hyperscalers like Google and OpenAI, is beginning to crumble. With the release of DeepSeek V3.2 and its impressive performance data, we are currently witnessing a tectonic shift in the global balance of power in technology. What might appear to be the mere release of a new model, upon closer inspection, reveals itself to be a targeted blow against the economic foundations of Silicon Valley.

The analysis shows that the seemingly insurmountable "moat" of the US tech giants is being eroded by a combination of open-source strategy, algorithmic efficiency, and radical pricing. We are moving away from a world where AI was an expensive software-as-a-service (SaaS) product to a future where high-performance models become commonplace. This opens up unprecedented opportunities for German and European companies: The dream of data sovereignty and independence from US APIs is becoming a tangible reality thanks to locally operated open-weights models. The following article explores why the investment model is shifting from operational spending to capital-based infrastructure, how software optimization is rendering hardware sanctions irrelevant, and why we are at the beginning of a massive deflation of the cost of digital labor.

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The erosion of proprietary competition and US dominance: A watershed moment in AI economics

The global technology landscape is currently experiencing a moment of rare strategic significance, one with the potential to fundamentally reshape the balance of power in artificial intelligence. The release of DeepSeek V3.2 and its associated performance data are challenging the established market dynamics. For a long time, it was assumed that the leading US hyperscalers had created an insurmountable moat through their massive advantage in computing power and proprietary data. This assumption was based on the premise that peak performance was inextricably linked to closed systems and excessive investment costs. The recent developments from Hangzhou not only refute this premise, but in some respects reverse it. We are witnessing a democratization of high-performance technology that is putting significant pressure on the economic value of pure API access and could permanently weaken the pricing power of players like OpenAI or Google. It is not an exaggeration to say that this indicates a tectonic shift from a rental-based Software-as-a-Service model to a capital-based infrastructure economy, in which the model itself becomes a commodity, i.e., a common good.

Structural market shift through open weights and data sovereignty

The decision to release a GPT-5-level performance model under the Apache 2.0 license is far more than an altruistic gesture from the open-source community; it is an aggressive strategic move aimed at cannibalizing the margins of Western competitors. For decision-makers in German and European companies, the calculations for integrating artificial intelligence are fundamentally changing. Previously, CIOs and CTOs faced the dilemma of either paying immense sums for cloud subscriptions and routing sensitive company data through American servers, or relying on less powerful local models. This dilemma is now being resolved. The availability of open weights allows corporations to run high-performance models on their own infrastructure or in sovereign European clouds.

From an economic perspective, this transforms operational expenses for API calls (OpEx) into capital investments in proprietary hardware (CapEx). In the long run, this is more attractive for many companies because the marginal cost per token generated with in-house infrastructure tends to approach the pure electricity costs over time, while external providers always have to add a margin. Furthermore, local operation eliminates the risk of industrial espionage or unintentional data leaks, which is invaluable, especially for the European automotive, pharmaceutical, and financial industries. When the performance curve of open models intersects with or even surpasses that of closed-source models, the licensing model of proprietary providers loses its primary justification. The market shifts from a seller's market, where access is rationed, to a buyer's market, where implementation efficiency is the deciding factor.

Algorithmic efficiency as a response to hardware limitations

The technical architecture of the new model reveals an interesting response to the geopolitical landscape, particularly the US semiconductor sanctions against China. Necessity is the mother of invention. Instead of simply increasing computing power, which is difficult due to export restrictions on high-end chips, DeepSeek is optimizing algorithmic efficiency. The introduction of DeepSeek Sparse Attention represents a paradigm shift in processing massive amounts of data. In the traditional Transformer architecture, the computational effort grows quadratically with the length of the input text, as each word is related to every other word. This leads to massive inefficiency with very long documents, such as those commonly found in legal review, medical research, or codebase analysis.

By implementing an indexing system that filters out irrelevant information early on and focuses only on the contextually necessary text elements, this effort is linearized. Economically, this means that the costs for processing information, the so-called inference, decrease drastically. For companies that want to operate RAG systems (Retrieval Augmented Generation) to make their internal knowledge databases usable, this is the decisive factor. A model that not only finds the needle in the haystack but also consumes only a fraction of the energy enables business models that would previously have failed due to high operating costs. It turns out that software optimization is capable not only of compensating for hardware deficiencies but of transforming them into a competitive advantage. Efficiency becomes the actual product feature.

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The realignment of the value chain in model training

Another indicator of the industry's maturation is the shift in budget allocation within the development process. While in the past the majority of capital flowed into so-called pre-training—simply feeding the model with enormous amounts of text from the internet—the focus is now shifting significantly to post-training. The increase in the budget share for this phase from one to over ten percent signals that the era of simple scaling is over. We are reaching the point of diminishing returns on sheer data volume. Quality and fine-tuning are becoming the new drivers of performance improvement.

The strategy of generating synthetic data through specialized teacher models is particularly noteworthy. It addresses the looming problem of a potential shortage of high-quality human-generated text on the internet. By using AI to train AI, a self-reinforcing cycle of quality improvement is created. This breaks the monopoly of companies with access to the largest proprietary user databases, such as Google with its search engine or Meta with its social networks. If synthetic environments and generated scenarios are sufficient to train mathematical and logical skills to world-class levels, the barrier to entry for new players decreases. It becomes clear that intelligent process design and curated datasets are becoming more important than simply having access to the entire internet. This is good news for specialized industries, which can now train their own highly specific models with reasonable effort.

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From conversation to autonomous value creation

Perhaps the most significant economic implication stems from the benchmark results in software development. When a model is capable of autonomously solving over 70 percent of real-world programming problems, we move beyond the realm of supportive chatbots and into the era of digital workers. The significant lead over GPT-5 in this specific segment suggests that specializing in capable agents is the next major growth driver. For the software industry, this translates into a massive deflation of manufacturing costs. Code is the foundation of the digital economy. If the costs of creating, maintaining, and debugging code by AI agents plummet, it will enable an explosion of new software products and services.

At the same time, this increases the pressure on IT service providers and outsourcing locations. The wage arbitrage model, in which simple programming tasks were outsourced to low-wage countries, is under pressure when local AI can perform these tasks faster, cheaper, and more securely. Companies won't eliminate their development departments, but their role will change: away from writing lines of code and toward orchestrating AI agents and designing systems. A model's ability to act as an autonomous agent—that is, to plan tasks, use tools, and verify results—is key to productivity. Here, DeepSeek appears to have found an architecture that transcends simply predicting the next word and simulates genuine problem-solving behavior.

The Economics of Thought and the Price of Precision

The introduction of the Speciale variant and the associated token consumption data shed light on a new cost dimension: the cost of thinking. The fact that a model can achieve gold-level performance at the Olympiad, but requires significantly more computing power, illustrates the principle of inference-time compute. We are moving away from a world where every answer costs the same, towards a model where the depth of reflection is priced. These new AI architectures behave similarly to the human brain, where intuitive action, the so-called System 1, consumes little energy, while deep logical thinking, System 2, is demanding.

For the market, this means segmentation. For everyday tasks like summaries or emails, the efficient basic model is the rational choice. But for problems where a single error can cost millions—such as analyzing contracts, diagnosing rare diseases, or optimizing financial portfolios—the high resource consumption of the Speciale variant is absolutely justified from an economic perspective. The cost of 77,000 tokens is negligible compared to the value of a correct solution in a high-risk scenario. This creates a market for premium inference where logical depth, not speed, is the selling point. DeepSeek's openness regarding these trade-offs demonstrates a maturity in product management that is no longer solely focused on simple marketing metrics, but rather on real-world use cases.

Strategic implications for global competition

The release cycle of DeepSeek V3.2 is far more than a technical update. It's a catalyst for market consolidation. The notion that US companies, due to their early entry and financial resources, maintain a lasting hegemony in the AI ​​sector must be revised. The combination of open-source licensing, extreme efficiency, and specialized agent capabilities is attacking the business model of closed platforms from multiple sides simultaneously. The pressure is mounting immensely on OpenAI and Google. They now have to prove that their proprietary models offer added value beyond what is freely available.

This is expected to lead to an acceleration of innovation, but also to a price war. For the German and European economies, this is a stroke of luck. Dependence on a few US tech giants is being broken by a viable, high-performing alternative. It is quite possible that in the near future we will see a hybrid landscape in which sensitive and complex core processes run on open models under local control, while cloud services are used only for generic tasks. The dominance of the hyperscalers is not broken, but it is being seriously challenged for the first time. The market is becoming more efficient, technology more accessible, and competition fiercer. The era of easy profits from simply providing intelligence is drawing to a close; the age of value-creating integration has begun.

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