As model commoditization intensifies, where do large model companies find their competitive edge?

Within a mere two weeks, Yuezhi'anmian has made a series of strategic maneuvers. Initially, it was reported to be in talks with Microsoft, Amazon, and Google for revenue-sharing agreements, pioneering an overseas leasing model for open-source models. This was swiftly followed by the introduction of an enterprise partnership initiative leveraging the FDE (Forward Deployed Engineer) model. Under this program, partnered IT service providers and integrators will provide on-site support to help enterprises seamlessly integrate AI into their core operations. Last week, Yuezhi'anmian unveiled a comprehensive financial industry solution package, integrating tailored products and capabilities for the financial sector, along with institutional-grade data modeling and reporting functionalities.

Individually, these moves may not seem revolutionary: cloud-based large models have long been offered by OpenAI and Anthropic; on-site FDE delivery was pioneered by Palantir two decades ago; and industry-specific solutions are a staple among B2B companies. However, the rapid succession of these three initiatives within such a short timeframe signals a unified strategic pivot: Kimi is transitioning from a model-centric company to a delivery-focused enterprise.

This pivot is not exclusive to Kimi. Concurrently, OpenAI also launched a financial industry solution, while Anthropic has long been deeply entrenched in the B2B space. Leading players are unanimously directing resources to the enterprise frontlines, driven by a singular realization: benchmarks and rankings do not translate into revenue; only deployment capabilities that integrate into business operations and deliver tangible benefits can. If these moves are simply interpreted as 'Kimi going B2B,' the strategic depth is being overlooked. The pertinent question is: Why are large model companies collectively shifting towards delivery now?

01 The Collective Dilemma of Proof of Concept (PoC)

Over the past two years, domestic large model technology has witnessed exponential growth. Yet, many vendors persist with traditional PoC delivery models. Enterprises often receive polished demo versions based on carefully selected samples, which perform exceptionally well in controlled test environments. However, when confronted with internal chaotic historical documents, fragmented work orders, and cross-system access constraints, model accuracy rapidly declines, and hallucination issues surge. Many projects stall after concept validation budgets are exhausted, falling into the 'PoC death trap.'

PoC can only demonstrate a model's potential performance, not its effective utilization within an enterprise.

The root of the problem lies not in the models themselves but in a misaligned division of labor. The primary flaw in traditional PoC models is the disconnect between business, data, and intelligent systems. Most vendors merely provide API interfaces, focusing on token sales, while leaving business adaptation, data processing, and system integration to the clients. However, enterprise AI transformation necessitates a blend of business acumen, large model fine-tuning, and heterogeneous system integration capabilities—a composite skill set that most government and enterprise clients struggle to assemble. This has created an industry paradox: the market is inundated with large model products, yet truly impactful deployments embedded in core businesses with measurable benefits remain scarce.

This reveals a truth obscured by benchmarking competitions: in the second half of the large model race, the bottleneck is not intelligence but delivery.

02 A Transparent Strategy: Standardizing Delivery as a Business

Kimi employs the PDE (Process, Data, Engine) methodology in enterprise projects, utilizing Process breakdown, Data engineering for enterprise private data, and Engine orchestration for intelligent agents as prerequisites to circumvent PoC pitfalls from the outset. Unlike some vendors who prioritize demo creation before business adaptation, the PDE framework prioritizes business processes: early-stage teams collaborate with clients to map out complete real-world business workflows, clarify AI-human responsibility boundaries, and integrate with existing systems like ERP, OA, and CRM to form closed-loop business operations—rather than producing mere copy-paste demos. Next comes enterprise private data engineering: instead of relying on curated samples for demonstrations, raw, unprocessed business data is directly utilized, with data anonymization, permission isolation, and knowledge base/evaluation set construction to suppress model hallucinations from the source. On the engine side, built on the Kimi Hosted Agents platform, it handles prompt version management, tool invocation orchestration, safety guardrails, shadow testing, and full-chain observability—architected for production environments, not simple API calls. The base model is interchangeable, with business logic precipitated at the engine layer, supporting shadow operations and gradual rollouts to minimize business disruption risks.

The base model serves as the entry ticket; the PDE methodology is the converter that transforms technology into business value. However, methodology alone cannot execute on-site implementations—FDEs (Forward Deployed Engineers) are needed for frontline execution. The FDE concept originated with Palantir in the U.S., later adopted by OpenAI and Anthropic through self-built FDE teams. However, the overseas self-built FDE model has inherent flaws: Palantir relies on elite engineers for deep delivery but faces heavy asset burdens—FDE composite talent is scarce, revenue growth is tightly bound to headcount expansion, and it struggles to achieve software-like scalability. Capital markets value it more like a consulting firm than a software company.

Recognizing these limitations, Kimi did not replicate the heavy-asset self-built team model. Instead, it partnered with leading IT service providers and system integrators like Chinasoft International, Kingsoft Cloud, and Teamsun to co-build an FDE frontline deployment engineer force. The engineers belong to the ecosystem partners, while the methodology and platform remain Kimi's proprietary assets. This approach leverages China's mature IT service ecosystem to solve Palantir's unsolved leverage problem: heavy delivery tasks are outsourced to existing integrators, while Kimi retains control over the model base, standardized processes, and component accumulation. All FDE projects must adhere to unified PDE standards, with project components fed back into the platform. FDE engineers also act as 'business translators,' driving base model iterations to continuously reduce marginal delivery costs.

This is a strategic move, but the biggest vulnerability in the co-building model is quality control: varying capabilities among partner engineers risk reducing projects to ordinary outsourcing. Kimi's countermeasure is standardization—a correct direction, but its effectiveness hinges on project volume and renewal rates, which will determine how far this model can scale and impact the company's Annual Recurring Revenue (ARR) growth.

03 Finance as a Litmus Test, Not a Final Destination

The financial industry solution package is actually a phased outcome of the FDE model's implementation in the financial sector, representing standardized industry capabilities exported after on-site project accumulation and standardization. The choice of finance as a litmus test is deliberate: securities firms, banks, and funds have the strictest data compliance and accuracy requirements across industries. Success here grants credibility in other sectors. From morning holdings reports and earnings reviews to project screening, in-depth research, and portfolio reviews, this solution links data acquisition, professional analysis, and outcome delivery, shifting the value of large models in finance from frontend efficiency gains to deep integration into core business closed loops.

Disclosed data shows that the 'ad-hoc mandate report generation' agent co-built by CSC Securities and Kimi reduced the planned 2-month integration to just 3 working days, cutting single-report manual production time from 30 to 10 minutes and reducing labor input by 67%. However, it must be acknowledged: a 67% efficiency gain is a 'productivity' narrative, not yet a 'revenue generation' one. Whether financial institutions will sustain payments for this solution—and whether payment scales can cover on-site delivery costs—remains the true litmus test for commercial viability. The gap between 'flagship projects' and 'scalable businesses' is a chasm most enterprise service companies fail to cross.

04 The True Implications of the Three Arrows

Viewed collectively, Kimi's intentions become clear: negotiating revenue-sharing with overseas cloud providers for the model base represents a light-asset globalization route; enterprise deployment leverages ecosystem partners for FDE teams, following a leverage-based approach; and the financial solution package demonstrates that this strategy can yield standardized products. By tapping into China's mature IT service ecosystem to complement technical capabilities, industry expertise, and client channels, Kimi's three-pronged approach essentially answers the same question: as models become commoditized, where lies the competitive moat for large model companies?

Industry trends corroborate this shift. Gartner predicts that by 2026, over 85% of tech service providers will adopt FDE projects as a core means of AI deployment to shorten implementation cycles. When on-site delivery becomes industry standard, competition will shift upstream: winners will be determined by stronger model bases, more standardized methodologies, and greater ecological leverage.

As the hype of large model benchmarking competitions fades, commercialization has become the ultimate test of real capability. The main battleground for domestic large model competition has shifted to the real-world business environments of countless enterprises. For Kimi, the flurry of half-month activities demonstrates strategic clarity, but clarity alone does not guarantee victory. The ultimate question for this 'delivery business'—like all enterprise service companies—remains refreshingly simple: after bridging the last mile of digital transformation, will clients renew next year?

From demos to delivery, from efficiency gains to revenue generation, this is a hurdle all large model companies must clear sooner rather than later. The large model narrative has evolved from 'witnessing miracles' to 'grinding through daily operations.' This is not a retreat of imagination but the moment technology truly enters reality. As the spotlight shifts from benchmarking lists to enterprise business sites, the road is long, but with the right direction, distance matters less.