Blog
Insights on market intelligence, AI-powered analysis, and the future of financial research.
AI Misconceptions and Factual Realities: An Overview
A comprehensive overview of common AI misconceptions and the factual realities that should guide engineering decisions.
The math on autonomous agents is brutal. Chain 10 steps at 95% accuracy each and your total success rate is 60%. The hype ignores exponential decay.
If your system breaks because you changed an adjective, the problem isn't your prompt; it's your architecture. The mature approach is System Engineering.
Fine-tuning is effective for teaching form but terrible for injecting facts. The 'domain-specific fine-tuned model' pitch is mostly marketing.
Million-token context windows sound impressive. The reality: models struggle to retrieve information buried in the middle of long prompts.
The 'blackbox' narrative is convenient but wrong. AI models are sequences of math operations. Control the arithmetic, control the output.
LLMs predict the next token. That's it. They've mastered linguistic form without possessing communicative intent.
If code can do it, code should do it. There's a concerning number of AI automations that shouldn't be AI automations.
The industry conflates 'bigger' with 'better.' This ignores basic math. Running a cheaper model multiple times beats expensive single passes.