The massive capital influx into AI means much of the discourse is marketing disguised as education. To find the signal, analyze the speaker's incentives. Are they trying to raise capital and justify valuations, or are they providing a grounded, factual perspective on the technology's actual capabilities?

AI diligence has replaced cybersecurity as the modern, high-stakes technical hurdle in M&A. Buyers now focus on a company's AI defensibility and roadmap. A lack of a clear AI strategy or a perceived vulnerability to AI disruption can be an existential risk that either kills the deal or severely impacts the valuation.

In AI M&A, recency is key. Companies pre-ChatGPT often had to rewrite their entire stack and relearn skills, making their experience less relevant. Acquiring a company with post-ChatGPT experience ensures their tech and knowledge are current, not already obsolete.

A company's career page is a crucial source of truth during due diligence. The technologies listed in job postings reveal the actual tech stack. This can expose a major disconnect between an investor's thesis (e.g., modern, AI-native) and the on-the-ground reality (e.g., hiring for legacy Delphi developers).

Tech due diligence is no longer a post-LOI, checkbox "IT audit." In the AI era, it has "shifted left" to become a critical, pre-LOI analysis of a company's strategic defensibility, AI maturity, and ability to innovate, often starting with outside-in signal gathering.

With nearly every public B2B company now featuring AI, the novelty has worn off. 'AI washing' by adding a simple co-pilot is no longer a differentiator. To succeed, companies must use AI to create genuinely disruptive products that solve problems in ways that were previously impossible.

While many firms are just now reacting to AI's impact, major credit investors like KKR have been actively underwriting AI-driven business model risk for nearly six years. This proactive, long-term approach to assessing technological disruption is a core part of their due diligence process, not a recent development.

Many "AI Product Manager" jobs are standard PM roles with "AI" sprinkled in. A simple test is to replace every instance of "AI" with a random noun like "marble." If the description still largely makes sense or becomes nonsensical, it reveals the role lacks true AI-specific responsibilities.

A developer reverse-engineered 200 AI startups and found that 146 were primarily wrappers for major APIs like OpenAI and Claude, despite marketing claims of "proprietary language models." This suggests a widespread disconnect between technical substance and marketing hype, a critical due diligence flag for investors and enterprise buyers in the AI space.