Why I'm Breaking My LinkedIn Silence: The AI Problems Nobody Else Is Solving
I don't post on LinkedIn often. Actually, I barely post at all. Social media and I have an understanding - we mostly ignore each other.
But sometimes you solve problems that feel too important to keep quiet about.
Over the past year, my team at 4Square Capital has been on a journey that started with a simple question in a Dubai meeting room in January 2025: "Why is everyone accepting that AI is fundamentally unreliable?"
You know the problems I'm talking about:
The Hallucination Problem
LLMs confidently generate complete fiction 10-15% of the time. Ask ChatGPT for references, get citations that don't exist. Ask for financial data, get numbers pulled from thin air.
The Privacy Nightmare
Every query you send to OpenAI, Google, or Anthropic goes through their servers. Your proprietary data, your customer information, your trade secrets - all travelling through someone else's infrastructure.
The Counting Catastrophe
Ask an LLM to add two numbers, and you're playing Russian roulette. The models that can write poetry and code can't reliably tell you what 7,849 × 3,267 equals.
These aren't edge cases. They're fundamental limitations that make AI unusable for anything that matters.
Here's what nobody talks about: these problems aren't separate. They're symptoms of the same architectural flaw - we're asking probabilistic language models to be deterministic computers.
The Journey
In January 2025, we designed a different approach. By February, we had a working prototype. By April, we were in production with real clients in pharmaceuticals, security, manufacturing, logistics, and travel.
These aren't startups experimenting with AI. These are enterprises where hallucinations can kill patients, privacy breaches can destroy companies, and wrong calculations can ground fleets.
What We Built
We didn't just patch the problems. We solved them at the architectural level:
- Genius2 eliminates hallucinations through multi-LLM consensus. Not reduces - eliminates. From 15% hallucination rates to under 2%.
- Private deployment architecture keeps data on your infrastructure. No external API calls to big AI providers unless you explicitly want them.
- Code generation for arithmetic stops asking LLMs to count and instead generates executable code that does the maths correctly.
Why This Matters
Over the next few weeks, I'll break down each of these solutions. Not marketing fluff - actual technical architecture, real deployment challenges, and honest lessons learned.
Because here's the thing: we're not geniuses. We're just a team that refused to accept "that's just how AI works" as an answer.
If you're a CTO wrestling with AI reliability, a business leader hesitant to deploy AI because of privacy concerns, or a technical leader who knows AI should be better than this - this series is for you.
Try AskDiana for free: https://askdiana.ai
Next up: The hallucination problem, why it happens, and how we made it virtually extinct.