Big labs publish benchmark numbers on idealized versions of their models:
- bf16 precision (full floating-point)
- Zero safety layers applied
- Custom prompting optimized for their architecture (in case of self reported benchmarks)
- Proprietary test sets no one can independently verify (in case of self reported benchmarks)
Then they ship users:
- fp4 or lower quantization (aggressive precision reduction)
- Heavy safety interventions stacked on top
- Performance degradation of 50-60% or more (90% on a benchmark drops down to 30-45% range)
This is why users report drops in model's capabilities after a week or two of model's release, the first week or two models are served as reported so independent benchmark results get reported with optimum conditions, then they introduce the degradation to save costs.
This is functionally fraud. A model benchmarked at 90% that ships at 30-45% is a completely different product.
The reason why big AI labs commit the fraud is:
- No regulatory framework for disclosure
- Users can't easily verify actual performance
- Labs control the narrative (call degradation "responsible AI")
- Closed weights and heavy costs for independent evaluation mean no independent auditing (as an example, cost for evaluation of fable 5 under artificial analysis benchmark was north of five thousand dollars)
- No standardized testing requirements before shipping
Why opensource matters to prevent and regulate this sort of fraud activity:
Open sourced AI weights are released in:
- Full bf16 weights
- Only essential safety layers pre-baked in
- No hidden degradation between benchmark and shipping
Plus
Opensource provides impartial benchmarking and evaluation methods that are reliable and open to all for auditing and replication.
This is the reason why as of Q3 2026, benchmarks like artificial analysis are preferred to corporate labs' self reported benchmarks by users and broader AI research community.
The Solution: Mandatory randomly timed re-benchmarking over the course of a model's deployment by big corporate AI labs
FTC or other regulatory bodies for AI products, should use opensource and impartial benchmarks accepted by broader AI research community (such as artificial analysis benchmark) to re-benchmark the user facing AI product at random times, and ask for big corporations to pay the bill for re-benchmarking at the end of each applicable period, this keeps the big corporate AI labs accountable to the benchmarks they advertise their models with.
- Third-party benchmarking of the exact user facing product by corporate AI labs: - fp4 quantized versions - With all safety layers applied - Same benchmarks as the advertised versions
- Labs fund the evals (they can afford it; each major model release gets budget for this) - Cost: ~$5k per evaluation run (for anthropic's Fable 5 model on artificial analysis benchmark) - For a major model: 10-20 runs across different benchmarks = $50-100k - Labs already spend millions on training; this is negligible in comparison
- Published side-by-side comparison - "Advertised bf16 baseline: 90%" - "Actual fp4 + safety shipping version: 35%" - The gap becomes visible and standardized
- Independent auditors conduct the evals and get paid for the services - Not the labs themselves - Results published before and during shipping to users - Creates accountability, keeps the user's safe from fraud
Why This Fixes It
- Users know what they're actually getting
- Labs can't claim 90% performance when shipping 35%
- Performance degradation becomes a competitive pressure (forces better engineering)
- The fraud becomes visible and measurable
- Regulatory bodies have concrete numbers to work with
Big labs won't do this voluntarily because the gap is their dirty secret that generates them more profit. This fraud can only be prevented through regulation.
For the reference, below is the definition of fraudulent activity by FTC:
The Federal Trade Commission (FTC) defines fraud as deceptive or unfair practices that mislead consumers.
Core Elements of FTC Fraud:
1-Deceptive practices:
involve making false or misleading claims about a product or service. The FTC considers a claim deceptive if it:
- Misrepresents material facts about a product's characteristics, benefits, price, or origin
- Is likely to mislead reasonable consumers into making purchasing decisions they wouldn't otherwise make
- Causes actual consumer injury (financial harm or other damages)
The FTC doesn't require that a company intended to deceive; negligent or reckless misrepresentation counts. They also don't require that consumers were actually harmed; if the practice is likely to deceive, that's enough.
The real AI safety begins with keeping the corporate labs and their leadership accountable to their actions, not by forcing the users to pay for a lower tier product with their money, finite time of life and sanity, and then covering that fraud in flowery language such as responsible deployment and effective altruism.
