[Student IDEAS] by Dan Xiao - Master in Management at ESSEC Business School
As Big Tech dominates foundation models, software is shifting to the application layer with Systems of Action (SOA)—autonomous software that targets global professional labor spend by executing end-to-end outcomes. Led by capital-efficient second movers, vertical AI startups are building defensible moats through a four-step execution stack: cost-optimized model routing, proprietary Retrieval-Augmented Generation (RAG), agentic workflows, and outcome-based pricing. By turning regulatory compliance like the EU AI Act into trust barriers and mastering deep domain edge cases, specialized application-layer platforms are redefining value capture beyond brute-force compute.
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As Big Tech bundles good enough AI into every existing seat, how can a software startup survive the gravity of the giants?
The answer is simple: if you are still using AI just to draft emails or fix typos, you are missing the forest for the trees. While the masses use AI for these simple tasks, a fundamental transition is occurring in the corporate world: the narrative of AI is shifting from what it says to what it does. We are witnessing a fundamental transition: the focus is moving from the Foundational Layer to the Application Layer. In this new era, AI is reshaping the very concept of SaaS (Software as a Service), shifting the value proposition from managing data to executing outcomes.
Let’s review how SaaS has evolved1.
As highlighted in NEA’s 2025 analysis2, while traditional SaaS captures a share of the $500 billion enterprise software sector, AI “Systems of Action” are now targeting the $11 trillion global professional services labor spend.
The disruption is both localized and surgical. A group of second-mover3 startups are quietly dominating entire industries with narrow, high-margin AI services, intentionally avoiding the massive capital demands of the model race. While this efficiency movement is global, European founders have emerged as its primary architects. They are turning the EU AI Act4 into a form of regulatory sovereignty, creating a home-field advantage in high-stakes sectors where trust is currency.
According to a recent report by Goldman Sachs5 US giants are bleeding capital, with Big Tech capex projected to exceed $400 billion. In contrast, while European founders lack the same raw financial firepower, they excel at capital conversion—matching Silicon Valley's ability to turn investments into $1B+ outcomes, but on a much leaner budget. European founders are not inferior to their Silicon Valley counterparts in their ability to convert “investment amounts” into “company value”. Along with the decrease of token fees, second-mover start-ups maintain strong 60% gross margins and healthier scaling trajectories6. They’ve realized one crucial wisdom: It’s time to trade hype for impact.
How do these second-mover startups win? Which investment hotspots are overheated, and which are untapped? This article will decode the profit logic and the cycles that will decide who actually captures value in the next decade.
The conventional wisdom that the European tech scene lags behind due to capital inefficiency is a persistent myth. In reality, the data tells a different story: when normalized for total capital raised, European startups share the exact same probability of reaching unicorn status as their American counterparts.
Another counter-intuitive reality is the role of the EU AI Act. While often criticized as a hurdle for innovation, it is increasingly viewed as a form of “regulatory sovereignty”. This framework provides Europe with a natural home-field advantage in high-stakes, highly regulated sectors like Healthcare and RegTech, where trust is the primary currency.
In these specialized sectors, Vertical AI has become a clear trend. Vertical AI refers to Artificial Intelligence applications that are purpose-built to solve problems within a specific industry (a “vertical”), such as Law, Healthcare, Construction, or Finance. Unlike Horizontal AI (like ChatGPT or Claude), which can do a little bit of everything, Vertical AI is a specialist. These applications are purpose-built for specific industries to combine the raw power of LLMs with industry-proprietary data, specialized workflows, and strict regulatory compliance.
Take a French healthcare startup, Nabla for example, it achieved $70 million in Series C funding last year in New York. Nabla directly embed AI into their care service by generating high-quality clinical notes in seconds through ambient documentation and real-time medical coding. Their advanced products save doctors hours of paperwork while maintaining the highest data privacy standards required by law.
To see how the Vertical AI trend is manifested on a global scale, let’s look at the regional divergence in strategy. Synthesizing the latest 2025 reports from Accel, Bessemer Venture Partners (BVP) and Atomico, the table below illustrates how different regions are positioning themselves for the next phase of the “model wars.”
| Region | Core Strategy | Primary Capital Focus | Target Industries | Strategic / Economic Moat |
| USA | Compute & Foundation Models | Infrastructure Layer (75%+) | Consumer Tech, Hyperscale Search, General Productivity | Capital Concentration: Massive CapEx scale ($400B+); “Winner-takes-all” platform dynamics. |
| Europe(EU) | Vertical Specialization & Digital Sovereignty | Application Layer (60%+) | Advanced Manufacturing, RegTech, Healthcare, Sovereign Cloud | Trust Moat: EU AI Act compliance; focus on data privacy, ethics, and B2B workflow depth. |
| China | Mass Implementation & Full-Stack Integration | Hardware & Industrial B2B Apps | E-commerce, Humanoid Robotics, Smart Cities, Supply Chain | Data Scale: Massive closed-loop industrial datasets; state-led infrastructure and 5G/6G integration. |
| Israel / UK | Deep Tech Niches & Security Resilience | Vertical SaaS & Agent | Cybersecurity, FinTech, Drug Discovery | Talent Density: Specialized R&D hubs; high concentration of “Exit-focused” elite engineering talent. |
This data further demonstrates why Europe is winning the race for capital efficiency: it has chosen the application layer as its battlefield, a space characterized by higher gross profit margins (70–80%) and greater industry depth, allowing it to avoid the computing-power quagmire dominated by American giants.
Does this focus on the application layer mean we can ignore the “model wars”? Certainly not. While common sense holds that building a frontier LLM model requires $10B+, as OpenAI did, France’s Mistral AI has shown that mathematical elegance can rival brute force. By pioneering optimized architectures like Mixture-of-Experts (MoE), a design that routes queries only to specialized experts rather than activating the entire massive model for every single task, Mistral released models (e.g., Mistral Large 2) that match the performance of GPT-4 while requiring significantly fewer parameters and lower inference costs. They proved that you don’t need a power plant to run world-class AI, you just need a smarter design.
On the other hand, for those operating on the Application Layer, the path can lie in Vertical Specialization. For example, which apps do you use more often: DeepL or Google Translate?
Most people use them in different contexts: Google Translate is often used for quick, casual queries, while DeepL is seen as a gold standard for professional and business communication. This is a striking example: in 2024, DeepL reached a $2 billion valuation7 and was reportedly exploring a $5 billion IPO (Initial Public Offering), with just $132 million in primary capital. DeepL is proving that “precision” can scale faster than “brute force.” Its specialized engine consistently achieves strong BLEU scores (Bilingual Evaluation Understudy), often outperforming general-purpose models like GPT-4 in high-stakes formal business contexts.
Apart from capital efficiency, DeepL also shows incredible advantages in real-time solutions among plenty of languages. It perfectly exemplifies the “European Second-Mover” logic: instead of racing to build general models, they mastered the domain-specialized workflow—locking in a critical vertical and building an unassailable moat of expertise.
But how is this moat actually engineered? Behind the striking valuation of companies like DeepL lies a fundamental shift in how AI products are built. In 2026, creating a Vertical AI leader is about orchestrating a four-step strategic stack that prioritizes precision over brute force.
To build a Vertical AI leader that can stand up to Silicon Valley giants, founders are moving away from General AI to adapt a specialized blueprint that prioritizes localized intelligence, proprietary context, and outcome-based economics. Here is the organized 2026 prospecting execution stack:
Instead of chasing the massive, generalized intelligence of a GPT-4, Vertical AI starts with Model Routing and Distillation.
In 2026, inference cost has replaced hosting as the primary variable expense for software firms. According to Accel 2025, inference costs have crashed by 97%. Specifically, GPT-4 pricing fell from $75 per million tokens in March 2023 to just $2 per million tokens for equivalent mini models by late 2025.
By running a smaller, fine-tuned model locally, you capture 70-80% gross margins that were previously lost as external API fees paid to model providers. Use a “Frontier Model” (like Claude 3.5) for the initial reasoning, but then distill that specific industry knowledge into a smaller, faster, and cheaper model (like Mistral 7B or Llama 3.1).
The second step in the vertical blueprint is the Context Layer. This is achieved through Retrieval-Augmented Generation (RAG). RAG makes it possible to feed AI with proprietary, high-density data such as a law firm’s past 5,000 litigation files, a manufacturer’s sensor logs, or a hospital’s clinical case histories.
First, for grounding in proprietary data assets, Vertical AI leaders build their moats by feeding models with data that Silicon Valley giants cannot easily access. In Europe, the EU AI Act creates a compliance moat. Research from team.blue suggests that 72% of European CIOs have concerns about “data sovereignty” when using public cloud LLMs based in the US.
RAG provides a technical solution to these regulatory hurdles. Because it retrieves information at the time of the prompt rather than embedding it in model weights, it enables superior data governance. For example, if a user exercises their “right to be forgotten” under the GDPR or the EU AI Act, a RAG system can simply delete the specific document from its index. In contrast, a model that has been fine-tuned on that data would theoretically require expensive retraining to ensure the data is fully removed. This makes RAG-based architectures the default choice for compliant Vertical AI in sectors such as finance, law, and healthcare. Below are examples of RAG-layer implementations for specific provisions of the EU AI Act.
| EU AI Act Article | Requirement (extracted from contexts8) | RAG Layer Implementation |
| Article 10 | High-quality, error-free training data | Automated data validation and bias scanning |
| Article 15 | Accuracy, robustness, and cybersecurity | LLMfFirewalls and real-time monitoring of RAG retrievals |
| Article 17 | Quality management system | Documented data lineage and decision logs for every AI action |
By 2026, 40% of AI-driven applications will feature task-specific AI agents to complete end-to-end tasks, moving away from simple “chat” prompts9. This means that after 2026, the vanguard of Vertical AI will be built entirely on agentic workflows.
To illustrate what “agentic” means, consider a traditional supply chain application: previously, a user had to manually type a prompt asking, 'Where is the delay?' In the 2026 agentic paradigm, however, the AI operates and observes entirely in the background. It monitors the data, identifies the delay, drafts a re-routing plan, and presents it for a one-click approval. This is the “Invisible AI” concept. There is no UI; the AI becomes a background process that orchestrates complex tasks through tools like LangChain or CrewAI. This shift is being accelerated by frameworks like Google’s Agent Development Kit, which standardizes how developers orchestrate sub-agents. Gartner10 also expects that by 2028 there will be multi-agent systems, working like an F1 car pit crew. However, the increased latency and compounding risk of errors inherent in multi-agent reasoning remain open questions.
It is already striking that BVP predicts the transition of SaaS to AI “Systems of Action” (not solely through Vertical AI) has the potential to grow from $500 billion to $11 trillion. It is an ambitious figure. How can we know if it is realistic? What if this is just a shift from one hype cycle to another?
First of all, this leap in valuation reflects a fundamental expansion of the total addressable market (TAM). The focus is shifting from the software budget to the much larger labor budget. Since the target market has moved from tools to labor, the profit model must adapt accordingly. To capture the labor-based value, a new charging standard—outcome-based pricing—is emerging. In this model, customers pay for a successful outcome such as a resolved support ticket, a booked meeting, or a successfully filed permit, rather than for access to the tool. This makes sense because if your AI can do the work of a junior accountant in 10 seconds, charging per seat becomes a strategic mistake.
BVP notes that outcome-based pricing is expected to grow 10x faster than traditional SaaS pricing11. We can already see early signs of this shift in companies like RELX (a London-based global leader in legal, medical, and scientific analytics) . While the broader market feared AI disruption, RELX’s legal AI platform saw revenue growth accelerate to 8% in H1 2025. This acceleration isn’t coming from selling more seats to the same lawyers, but from deep workflow integration, where AI creates high-value outcomes—such as real-time fraud detection and automated legal drafting—that customers are willing to pay a premium for.
In the short term, gross margins may be lower. This may be driven by two factors: the orchestration costs of agentic workflows, which demand a massive volume of tokens, and the initial engineering overhead required to ground models in proprietary industry data. However, Customer Acquisition Cost (CAC) is lower because the “pain point” (e.g., manual data entry) is so visceral that the product effectively sells itself.
However, in the long game, the battlefield shifts to data network effects. Every additional customer makes the model cheaper and the predictions sharper, until switching feels like leaving a social graph. In the European context, this advantage is further reinforced by regulation. Regulated European data pools and the EU AI Act are turning today’s utility feature into tomorrow’s licensed infrastructure. Achieving compliance for “high-risk AI” (as defined by the Act in sectors like HR, credit, or healthcare) is an expensive, demanding process. Once a vertical leader becomes the “certified infrastructure” within a regulated data pool, it is transformed into a licensed utility rather than just a software vendor. Obviously, a Silicon Valley giant cannot simply “A/B test” its way into a market that requires a two-year compliance audit and strict data residency requirements.
Still, we should not be overly optimistic. This brings us back to the question of survival: how do you resist the gravitational pull of bundling from Microsoft, Google, and Salesforce? Beyond the “model wars,” these players are not standing still. Their strategy is to add a good-enough AI layer to the tools enterprises already pay for. Even if a specialized AI is 20% better, a CFO may still choose Microsoft Copilot because it is “free” within an existing E5 license.
In a world of distilled models, the winner is not who has the most data, but who owns the edge cases. Does your software solve the 20% of problems that 80% general models fail at? It is time for second movers to think strategically and act decisively.
[1] https://www.bvp.com/atlas/roadmap-ai-systems-of-action
[3] Second-mover: Here means those who belong to application layers that entered the market after the initial LLM hype in the AI era.
[4] The EU AI Act: officially entering into force in mid-2024 with major compliance deadlines rolling out through 2025 and early 2026, is the world’s first comprehensive horizontal legal framework for AI. It classifies AI applications by risk levels and imposes strict transparency and governance obligations on high-risk sectors and General-Purpose AI (GPAI) models.
[6] https://www.bvp.com/atlas/the-state-of-ai-2025#AI-Shooting-Stars
[7] https://www.deepl.com/en/press-release
