The technical reality your AI tools won't mention

Every AI deployment in production has the same four failure modes. Most organisations have no idea their AI is silently making decisions inside them. These are not edge cases - they are the baseline reality. The question is not whether your AI tools have these problems. The question is whether your decision-making process accounts for them.

Your AI gets worse the more you tell it

Every AI has a context window — working memory where it holds your conversation and documents. Chroma's 2025 research tested 18 frontier models and found every one degrades as input grows. Not at the limit — at 25–50% capacity. Stanford's research shows a 30%+ accuracy drop when critical information sits in the middle of the context. The model sounds just as confident when it's lost the plot. There's no built-in warning.

18/18 frontier models degrade with context length

AI is confidently wrong more often than you think

When a model lacks information, it doesn't say “I'm unsure” — it generates something that sounds right. Over 120 cases of AI-driven legal hallucinations have been documented since 2023, with 58 in 2025 alone. No model achieves zero. The danger isn't that people trust AI blindly — it's that AI outputs are hard to verify at scale. When the model generates a 10-page analysis, which facts are real and which are fabricated?

120+ documented AI legal hallucination cases

Every session, your AI starts from zero

LLMs are stateless by design. The moment a session ends, everything is gone — the context, the reasoning, the decisions. For one-off questions, this doesn't matter. For enterprise decision-making — where decisions build on prior decisions, where institutional knowledge matters — it's catastrophic. The same question on Monday and Thursday gets different answers because the context is different each time.

Zero persistent memory between sessions

The cost nobody's tracking

Every word an AI reads and writes costs money. Inference now represents 85% of enterprise AI budgets. Output tokens cost 4–8x more than input tokens. The difference between cached and uncached context is 10x in price. Yet almost no organisation tracks cost per decision. When a £50,000 strategic decision was supported by AI analysis, what did that analysis actually cost? Nobody knows.

85% of AI budgets are inference, not training

These aren't edge cases. They're the baseline reality of every AI deployment. The question isn't whether your AI tools have these problems - they do. The question is whether your decision-making process accounts for them. That's what we built Rubicon Probity to solve.

See how Rubicon Probity solves this →