Blind to Disruption – The CEOs Who Missed the Future

Analogy: Carriages vs Cars vs AI/SaaS

• Many liked the historical carriage–auto story but found the AI analogy strained. Car makers were directly in transportation; most firms are not “in AI” in the same sense.
• Some argued the better reading is: your real business is the outcome (transport, content, intelligence), not the artifact (carriage, print, humans), so ignoring AI may be like misidentifying your industry.
• Others stressed survivorship bias: 1 out of ~4000 carriage makers pivoting doesn’t mean the others “failed” by not betting their businesses on a risky new tech.

CEO Incentives, Labor, and Accountability

• Several comments focused on CEOs treating workers like cost items (akin to horses), making AI-driven replacement attractive.
• Debate over whether CEOs are truly “unaccountable”: some cited high firing rates; others noted that severance packages and lack of legal consequences make termination a weak societal sanction.
• One thread argued viewing employees as replaceable “cogs” caps a firm at small, local scales; high‑leverage businesses need irreplaceable talent layers.

AI Hype, Adoption, and Real Value

• Mixed views on the article’s “jump on AI” ending; some likened it to prior fads (blockchain, metaverse, web3).
• Others countered that this time is different: current systems already meaningfully assist with code, writing, search, logistics, and more, even if productization is immature.
• There’s pushback that “everyone is on the AI train”: many SMBs reportedly have no concrete AI strategy and may or may not be at risk, depending on whether there’s a real moat.

LLMs: Revolution, Tool, or Dead End?

• Optimists see LLMs as near-future disruptors of education, mental health, creative work, and knowledge tasks, with huge untapped potential.
• Skeptics emphasize unreliability, hallucinations, lack of clear reasoning and memory, and heavy costs; some expect current LLMs to be a “dead end” on the path to more capable architectures.
• Long subthreads debate whether LLMs truly “reason” or merely predict tokens, and whether that distinction matters if behavior is useful.

Disruption Stories and Strategy

• Many criticized disruption literature as hindsight-heavy and biased toward winners; for every “Studebaker,” there are countless “wrong bets” (EVs too early, 3D TV, Segway, metaverse, etc.).
• Several commenters argued it can be rational not to pivot: milk the existing business, accept eventual decline, and avoid speculative bets.
• Others note that disruption here is different: AI targets an entire category of labor (knowledge work), not a single vertical, which may make this wave less comparable to past examples.