After the Model Wars: Where Is the Second Half of AI Competition?

The Model Arms Race Is Winding Down

Over the past two years, the global AI industry has experienced an unprecedented 'model arms race.' From OpenAI's GPT-4 to Anthropic's Claude 3.5, from Google's Gemini to Meta's Llama 3, major players have fiercely competed on parameter scale, benchmark scores, and multimodal capabilities. Yet as the gap between leading models continues to narrow, a critical question has surfaced — what exactly will the next phase of AI competition hinge on?

At a recent Stanford University 'Talk to The World' series event, several guests from industry and academia engaged in an in-depth conversation around this topic. An increasingly clear consensus is forming: The second half of AI competition lies not in the models themselves, but in the 'last mile' built on top of them.

The Chasm Between 'Lab Toy' and 'Commercial Product'

A thought-provoking phenomenon stands out: despite the impressive capabilities of large models, the number of AI products that have achieved large-scale commercial deployment remains limited. Where does the problem lie?

The answer is that between a powerful foundation model and a usable product, there remains an enormous engineering chasm.

This chasm spans multiple dimensions:

• Persona Design: When users interact with AI, they expect not a cold text generator but a 'conversational partner' with a consistent personality, tone, and set of values. How to set an appropriate persona for AI so it exhibits a stable and natural communication style across different scenarios is itself a complex design discipline.

• Behavioral Alignment: Model outputs must not only be 'correct' but also 'appropriate.' They cannot be too casual in customer service scenarios, too rigid in creative writing, or too ambiguous in medical consultations. Behavioral alignment requires that model performance precisely matches the expectations of specific business contexts.

• System Prompts: These serve as the bridge connecting general-purpose models to specific applications. A carefully designed set of system prompts can mold the same foundation model into entirely different product forms — from a legal assistant to a coding coach, from a psychological counselor to a data analyst. The quality of system prompt design directly determines a product's upper bound.

• Safety Guardrails: In open environments, AI must be able to handle malicious inputs, sensitive topics, and edge cases. Being overly conservative reduces product usability, while being overly permissive can create compliance and reputational risks. Finding the optimal balance between safety and utility is a core challenge every AI product team faces.

Why the 'Last Mile' Is So Critical

From the perspective of technology history, this shift is unsurprising. In the internet era, once the core algorithms of search engines converged, competition shifted to user experience and ecosystem building. In the mobile internet era, once smartphone hardware became homogenized, operating systems and app ecosystems became the decisive factors.

The AI industry is undergoing a similar 'value migration.' When foundational model capabilities become utility-like infrastructure, the real differentiating value is created by productization capabilities built on top of models.

At the Stanford event, one guest offered a vivid analogy: a foundation model is like premium flour, but what users need is bread, cake, or noodles. The 'processing craft' from flour to finished product is the key link that determines the final product's value.

This assessment is corroborated by market data. According to recent reports from multiple research institutions, since 2024, the investment focus in AI has begun shifting from foundation model companies toward the application and middleware layers. Investors are increasingly asking not 'how big is your model' but rather 'what specific problem does your product solve' and 'what are your user retention and paid conversion rates.'

Four Major Trends in the New Competitive Landscape

Based on the Stanford event discussions and industry observations, the second half of AI competition is exhibiting four major trends:

Trend 1: Deep Cultivation of Vertical Scenarios Replaces General Capability Competition

The 'versatility' of general-purpose large models has ironically become a weakness in specific scenarios. Future winners will be products that deliver exceptional experiences in vertical domains such as healthcare, law, finance, and education. This requires not only deep integration of domain knowledge but also a precise understanding of user workflows.

Trend 2: AI Product Managers Become a Scarce Resource

As technical barriers lower, product design capability becomes a core competitive advantage. How to define AI interaction paradigms, how to design optimal human-machine collaboration workflows, how to balance powerful functionality with simplicity of use — these questions require hybrid talent with both technical understanding and user insight. AI product managers are becoming the most sought-after roles in the industry.

Trend 3: Evaluation Systems Shift from Benchmark Scores to User Value

Traditional model evaluation emphasizes standardized benchmarks, but these scores often disconnect from real user experience. The industry is establishing new evaluation frameworks that focus more on task completion rates, user satisfaction, error recovery capabilities, and other 'product-level' metrics. A model that scores high on benchmarks is not necessarily a good product, but a good product is always built on a deep understanding of user needs.

Trend 4: Safety and Trust Become Prerequisites for Commercialization

As AI applications enter highly sensitive fields such as finance, healthcare, and government services, safety and trustworthiness are no longer 'nice-to-haves' but 'entry requirements.' Enterprise clients are imposing increasingly strict requirements on data privacy, output controllability, and audit traceability when selecting AI solutions. The quality of safety guardrails directly impacts the signing of commercial contracts.

Implications for China's AI Ecosystem

This trend holds particular significance for China's AI industry. At the foundation model level, Chinese companies have demonstrated strong momentum in catching up, with products like Baidu's ERNIE, Alibaba's Tongyi, and ByteDance's Doubao approaching top international standards across multiple capabilities.

However, in 'last mile' productization capabilities, the industry still has considerable room for improvement. Specific shortcomings include: the methodology for system prompt engineering remains immature, evaluation standards for behavioral alignment lack consensus, and safety guardrail design often leans toward a 'one-size-fits-all' approach rather than fine-grained tiered management.

Whoever can fill these gaps first stands to gain a first-mover advantage in the AI commercialization race.

Outlook: The Endgame of Competition Is 'Experience'

Looking back at every paradigm shift in the tech industry, the ultimate winners have never been the players with the strongest technology, but rather the players who best understand their users. From Apple in the PC era to WeChat in the mobile era, this rule has held without exception.

The second half of AI competition is, in essence, a 'battle of experience.' The model is the engine, but the product is what users actually touch. From persona design to behavioral alignment, from system prompts to safety guardrails, these seemingly granular 'last mile' optimizations are becoming the dividing line between excellence and mediocrity.

As one guest summarized at the Stanford event: 'The ultimate competitive advantage in the large model era is not making AI smarter, but making AI more usable.'

This quiet revolution has already begun.