(image is a mockup of a recommended AI coherence health meter currently not in use)
To build confidence, improve data, and protect users, the AI industry must confront its scaling paradox head-on.
Confidence in AI is extraordinarily low. And the numbers are stunning: According to a May 2025 KPMG survey, while 80+% of the population believe AI will offer enormous benefits, less than 50% of the public trusts AI to generate sought results. Even more surprisingly given this, “many rely on AI output without evaluating accuracy (66%) and are making mistakes in their work due to AI (56%).”
The emergence of Evans’ Law, the observed pattern showing that large language models (LLMs) become less accurate per token as their context windows expand, is upending assumptions about scale as a proxy for intelligence. It’s a revelation that lands squarely at the intersection of performance, trust, and economics in AI.
For platform providers, the implications are stark. If hallucination rates increase predictably with token length, then throwing more compute at the problem no longer guarantees better results. The industry’s scaling law has met its limit. In its place, Evans’ Law demands a reorientation toward precision, data integrity, and user transparency.
1. Confidence Comes from Measurable Reliability
The first mandate is simple but uncomfortable: quantify and disclose error rates.
OpenAI, Anthropic, xAI, and others must move beyond qualitative claims of “fewer hallucinations” and start reporting standardized accuracy metrics across context lengths. Confidence in AI systems will grow only when users can correlate model behavior with measurable, repeatable outcomes.
Enterprise adoption depends on this transparency. CIOs need to know when accuracy degrades—whether at 8K, 32K, or 128K tokens—and how retrieval-augmented generation (RAG) or guardrails mitigate those drops. Without it, model providers risk the same credibility trap that plagued early cloud services: powerful but opaque.
2. Data Integrity Is the New Arms Race
Evans’ Law implies that bigger is not necessarily better? *especially* if the underlying data remains inconsistent. To protect users from compounding errors, platforms must pivot from massive ingestion to curated integration.
Key strategies:
- High-fidelity data pipelines: Prioritize verified, current, and bias-controlled sources over scale for its own sake.
- Dynamic grounding: Continuously anchor outputs to trusted factual repositories during generation, not post-hoc correction.
- Context management: Intelligently truncate or summarize context windows to prevent the drift Evans’ Law predicts.
The new race should be about trustable tokens, not just terabytes.
3. Product-Level Fixes: Platform Alerts and “Coherence Health”
To translate Evans’ Law into user safety and product reliability, platforms can ship practical features now. We are calling this the “Coherence Health” layer, visible, measurable, and user-controllable.
a. Context Budget Meter
A live gauge that shows “coherence headroom” (not just token count) computed from model size and attention entropy.
Example UI copy: “You’re within 10% of the model’s coherence threshold; expect reliability to drop.”
b. Degradation Alerts
Soft warning at 70% of the coherence budget, hard alert at 90%, and auto-mitigation at 100%.
Provide one-click fixes: summarize, chunk, retrieve selectively, or switch to a higher-context model.
c. Autopilot Mitigation
Automatically slim prompts (deduplicate, compress citations, strip boilerplate).
Use progressive retrieval—send only the most salient slices, re-fetch on demand—and opportunistic tool calls (e.g., SQL, code, calc) instead of cramming data into context.
d. Degradation-Aware Sampling
Adjust decoding and temperature dynamically, allocating more compute to key-value caching and attention near coherence inflection points.
e. Session Hygiene
Trigger a “Start Fresh” banner when conversation history alone would push coherence load past 70%.
Pin 3–5 atomic memory facts; purge the rest. Keep sessions lean, not bloated.
By combining these, platforms could literally brand a new standard: “Coherence Health,” complete with a meter, alerts, and automated safety rails for enterprise use.
4. Protecting Users and Markets
As generative AI becomes infrastructural, hallucination risk is no longer cosmetic (was it ever?) It’s now a security vector.
Model providers must audit how scaling affects coherence drift, publish standardized benchmarks, and offer contractual guarantees about accuracy under load. This is both about protecting users from misinformation and about shielding enterprises from liability and reputational harm.
5. From “Smarter” to “Truer”
Evans’ Law reframes generative AI itself. The next phase of AI progress won’t come from larger parameter counts or billion-token contexts, but from systems that know their limits—and communicate them.
The platforms that thrive will be those that declare confidence scores, ground outputs continuously, and protect users from the physics of scaling itself. None of this is currently occurring, and as a result trust in public AI is very low, confusion is high, and confidence in output is nearly non-existent. This is relatively easily addressed, although the impact of mitigation tactics limiting sessions to degradation thresholds is still unknown. One thing is certain: as visible errors decrease, it will strengthen confidence in the platforms and the technology.
Trust in generative AI won’t be built on power. It isn’t even complicated: it will be built on real results with measurable impact, and fewer errors.
