Phenomenon and Business Essence

Google's latest open-source model Gemma 4 (26B parameters) performs impressively on general question-answering benchmarks, but once entering real business scenarios—requiring external tool calls, adherence to system instructions, and executing multi-step automation workflows—it essentially fails completely. Developer tests reveal: the model explicitly receives instructions to "must call tools," yet still answers using internal knowledge; the longer the context, the worse the compliance. In other words: benchmark performance is one thing, doing your job is another. This is not a problem unique to Gemma 4, but a systemic flaw in the current open-source model ecosystem.

Dimension Analogy: "Exhibition Machines" in the Industrial Revolution

When steam engines were first introduced in the 19th century, many factory owners saw them operating smoothly at exhibitions and immediately signed orders to purchase them. Only after installation in factories did they discover: exhibitions used finely calibrated coal and constant loads; real workshops had variable materials and complex transmission requirements that caused frequent breakdowns. Those earliest adopters lost a generation's worth of accumulated wealth.

Today's AI model evaluations (benchmarks) are that exhibition. Benchmarks measure "what the model knows," but enterprises need "what the model can execute." The gap between these is pushing a wave of SMBs eager to implement AI automation into the same trap: procurement costs have been incurred, but business returns are nowhere in sight.

Industry Consolidation and Endgame Projection

Using Grove's "strategic inflection point" framework, this flaw is creating a divergence in the fates of two types of players:

  • Death Zone (within 18 months): AI system integrators (SI) and RPA replacement solution providers that made heavy commitments based on "model demo performance." Once customers go live and discover insufficient instruction compliance, contract disputes will follow.
  • Harvest Zone: Vertical industry service providers focusing on "model fine-tuning + tool call reliability." When general models fail, companies that can offer industry-specific fine-tune solutions gain pricing power—order values can jump from 200K annual to 800K+.
  • Wait-and-See Dividend Zone: Traditional manufacturing and chain retail enterprises that haven't yet made large-scale AI automation purchases. Waiting 6-12 months, model instruction compliance issues will partially resolve, allowing procurement at that time for more stable ROI.

Endgame assessment: 2025 is the "reliability culling year" for AI agents—models that can stably execute instructions in real workflows will have 3-5x the commercial value of benchmark champions.

Two Paths for Business Leaders

Path One: Defer Major Investment, Conduct Stress Testing First

Before signing any AI automation contract, require vendors to run a 30-day pilot on your real business processes, with only one assessment metric: instruction completion rate ≥95%. Keep pilot costs under 50K to avoid million-level erroneous purchases.

Path Two: Invest in "Model Taming" Capabilities

Internally develop or outsource an "AI Process Engineer" (market salary 250K-400K/year), dedicated to system prompt engineering and toolchain integration testing. This is the critical role that transforms general models into reliable business tools, and also the most difficult-to-replace new human asset in the next two years.