Following the unveiling of GPT-5, the capital market's response was remarkably serene. The anticipated uproar failed to materialize, and tech media coverage remained restrained and cautious. AI concept stocks, previously aflame with GPT-4 enthusiasm, chose collective silence this time around.

What lies beneath this peculiar "silence" in terms of market sentiment and industrial shifts? Is it an indication that the marginal benefits of technological advancements are waning, or that market expectations have already peaked? Where does the genuine investment rationale reside when the hustle and bustle subside?

To cut through the fog and gain a clear understanding of the technological wave's true trajectory, Silicon Rabbit convened a closed-door roundtable discussion last week.

We gathered four elite experts from Silicon Valley, representing the world's foremost AI model research institutions, the computational powerhouse shaping AI's future, the tech leader driving AI commercialization, and a top investor with a keen eye for capital trends.

From their unique and invaluable perspectives, they painted a picture of the industry starkly different from public reports.

This summary presents their core "non-consensus" judgments. We believe these insights adequately explain Wall Street's current tranquility and provide vital signposts for investment decisions in the secondary market's next phase.

【Expert Lineup】

Expert A: Former lead researcher at OpenAI's large models, deeply involved in the evolution of model architecture from GPT-3 to GPT-4.

Expert B: Senior deep learning scientist at NVIDIA, responsible for optimizing the underlying computational efficiency of large model training and inference for an extended period.

Expert C: Technical lead of Google AI flagship projects, directly serving hundreds of large enterprises in deploying AI applications, deeply familiar with the technology-commerce gap.

Expert D: Partner in a top US dollar fund specializing in AI, leading multiple AI project investments worth billions of dollars.

Expert A (Former OpenAI Researcher):

"We must acknowledge that GPT-5 is an engineering marvel, the pinnacle of the 'brutal aesthetics' of existing Transformer models. However, it represents more of an engineering triumph than a scientific breakthrough. Internally, we anticipated a new path addressing the model's fundamental flaws (like logical breaks and factual hallucinations), but GPT-5's answer is 'continuing down the old path with more parameters and data.' This 'disappointment within expectations' is a common sentiment among insiders."

Expert B (NVIDIA Scientist):

"This 'brutal aesthetics' is hitting two walls simultaneously: the 'data wall' and the 'inference ceiling.'

On one hand, high-quality public training data on the internet is nearly exhausted, and we're now feeding models 'synthetic data,' which introduces new risks.

On the other hand, the model is essentially a 'master of imitation' based on statistics; it lacks genuine logical reasoning and a model of the world. Relying solely on scale expansion may not bring us closer to true 'intelligence.'

Even more brutal is the 'cost curse' in economics. While training costs are a one-time substantial investment, inference costs are a continuous drain that determines whether technology can be commercialized on a large scale. GPT-5's inference costs are still too high for most business models to profit."

The views of these two core experts collectively reveal a profound industrial shift: the 'scale expansion' paradigm driving AI development is simultaneously hitting its scientific and economic limits.

The scientific limit lies in the potential fundamental theoretical bottleneck of the existing Transformer architecture. It excels at correlation and imitation but has inherent deficiencies in strict logical reasoning, causal judgment, and world model construction.

When high-quality real data is "exhausted," simply adding more parameters and computational power results in a sharp marginal decline in performance gains. AI development urgently needs breakthroughs in fundamental scientific theories, not just larger-scale engineering.

The economic limit is even more tangible. High inference costs mean many seemingly promising application scenarios (like real-time AI teaching assistants, ultra-high-definition AI video generation) are commercially unsustainable.

Every user interaction is 'burning money,' placing AI service providers in a dilemma of 'the larger the scale, the greater the losses.' The industry's core contradiction has shifted from 'can the technology be realized' to 'can the business afford it.'

This double ceiling will trigger a revaluation of the entire technology stack:

Architecture Innovation > Scale Expansion: The popularity of new architectures like MoE (Mixture of Experts) and renewed exploration of 'retro' routes like RNN and state-space models reflect the industry's attempt to improve model efficiency through smarter, not more brute-force, methods.

"Counterattack of Small Models": The popularity of open-source small models like Llama 3 8B confirms the market's pragmatic shift. They perform adequately on specific tasks and have significantly lower deployment costs than giant models, especially in privatized deployment scenarios where data privacy needs to be protected.

Efficiency Optimization Becomes Core: Techniques like model compression, quantization, pruning, and hardware specifically designed for accelerating inference will transform from 'icing on the cake' to 'core technologies' determining applications' survival.

Expert D (US Dollar Fund Partner):

"I can confidently state that the 'easy money' era of AI investment is over. Last year, any project related to 'large models' could secure high valuations; it was a frenzied, narrative-driven phase. But GPT-5's marginal performance improvement is a clear signal of diminishing returns on the simple investment logic of 'the bigger the model, the better.'

We believe the 'S-curve' of AI development is transitioning from a steep climb to a plateau period of decelerating growth, necessitating a complete overhaul of the valuation logic for foundational model companies."

Expert C (Google Technical Leader):

"We deeply feel the changes in the capital market on the front lines. The tech community often asks 'has AGI been achieved?' But our enterprise customers care about: Is the model stable enough? Are the costs controllable? Can it seamlessly integrate into my existing IT architecture and workflow?

In the second half of AI, the key to competition has shifted from 'how smart the model is' to 'how usable the model is.' Whoever can first solve the muddy and intricate problem of the 'last mile' in enterprise applications will win the real commercial war. Tremendous opportunities will emerge in the application layer deeply integrating AI with industry scenarios and in infrastructure companies providing services for AI implementation."

Capital and the market, the two most potent forces, are jointly driving the 'value regression' of the AI industry. Frenzy is subsiding, and pragmatism is taking center stage. The core of this shift lies in the market's eventual recognition of the huge 'application gap' between technological potential and commercial value.

The model itself is merely a powerful 'general brain,' but it inherently doesn't understand any company's 'assets' and 'rules.' For enterprises to effectively leverage AI, they must first complete their own modernization, including:

Data Assetization: Transforming dispersed and chaotic internal data into 'knowledge assets' that AI can understand and utilize through governance, cleaning, and the use of tools like vector databases.

Process Orchestration: Digitizing and modularizing complex business processes so they can be invoked and driven by AI Agents.

Organizational Collaboration: Breaking down departmental silos and establishing a mechanism that allows business, IT, and data teams to collaborate closely to jointly drive the implementation of AI projects.

These tasks are arduous, time-consuming, and lack 'sexy' narratives, but they are the watershed for whether AI can create genuine commercial value. The market is realizing that 5% of AI success lies in the model itself, while 95% lies in successful implementation.

GPT-5's 'calm' is not a harbinger of an AI winter but a sign of an industry maturing. It signals the end of an era driven by technological frenzy and AGI narratives;

Simultaneously, it ushers in a new phase – a specialized era bidding farewell to hustle and bustle, returning to business essentials, and focusing on 'economic benefits,' 'application implementation,' and 'real moats.'