If I asked you, “When someone turns in a work assignment, how accurate is it? 80%, 90%, 95% or perhaps 100%?” We don’t think this way about coworkers’ spreadsheets. But we will probably think this way about AI & this will very likely change the way product managers on-board users. When was the last time you signed up for a SaaS & wondered : Would the data be accurate? Would the database corrupt my data? Would the report be correct? But today, with every AI software now tucking a disclaimer at the bottom of the page, we will be wondering. “Gemini may display inaccurate info, including about people, so double-check its responses” & “ChatGPT/Claude can make mistakes. Check important info” are two examples. In the early days of this epoch, mistakes will be common. Over time, less so, as accuracies improve. The more important the work, the greater peoples’ need to be confident the AI is correct. We will demand much better than human error rates. Self-driving cars provide an extreme example of this trust fall. Waymo & Cruise have published data arguing self-driving cars are 65-94% safer. Yet, 2/3 of Americans surveyed by the AAA fear them. We suffer from a cognitive bias : work performed by a human is likely more trustworthy because we understand the biases & the limitations. AIs are a Schrodinger’s cat stuffed in a black box. We don’t comprehend how the box works (yet), nor can we believe our eyes if the feline is dead or alive when we see it. New product on-boarding will need to mitigate this bias. One path may be starting with low-value tasks where the software-maker has tested exhaustively the potential inputs & outputs. Another tactic may be to provide a human-in-the-loop to check the AI’s work. Citations, references, & other forms of fact-checking will be a core part of the product experience. Independent testing might be another path. As with any new colleague, the first impressions & a series of small wins will determine the person’s trust. Severe errors in the future will erode confidence, that must be rebuilt - likely with the help of human support teams who will explain, develop tests for the future, & assure users. I recently asked a financial LLM to analyze NVIDIA’s annual report. A question about the company’s increase in dividend amount vaporized its credibility, raising the question : is it less work to do the analysis myself than to check the AI’s work? That will be the trust fall for AI. Will the software catch us if we trust it?

40 days in the desert ...

Professor @emollick has some of the most practical content on the applications—and implications—of AI in business. For anyone who isn’t clear yet how disruptive this technology is going to be in the very near term, check out Mollick’s latest post. “To see why these tools can be so disruptive, I want to revisit my old experiment of giving myself 30 minutes to launch a product. I last did this 10 months ago — forever in AI time — so it seemed unfair to give myself a half hour again. Let’s try doing the work in under a minute.” “Five reasonably high-quality drafts were done in under a minute. I had renderings of a few trendy kitchens, a three-page syllabus (that wasn’t half bad), a thirteen-page slide deck with speaker notes, an almost 1,000-word market research summary, and a product launch strategy (with draft emails) for one of Wharton Interactive’s teaching games that was really solid. A few more interactions with the AI, and a bit more time, and they might have been excellent.” “To paraphrase Kratzenberg’s First Law: “AI at work is neither good nor bad; nor is it neutral.” AI does not automatically improve the experience of work, nor does it automatically rob us of meaning or replace workers. How leaders and employees use the technology will determine whether it is good or bad. But AI also isn’t neutral. The use of AI will inevitably lead to deep and profound changes.” If you haven’t already subscribed to his One Useful Thing Substack, do it today. oneusefulthing.org/p/what-ca…

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People who live on the West side of each time zone go to bed later (dark blue), get less sleep, make less money, and suffer more diseases on average, as a map from this study shows! dailymail.co.uk/health/artic…

This is a reminder that Boston is best equipped to survive a zombie apocalypse. Considering the healthcare system, cobblestones, unreliable public transportation, and confusing roads, it makes sense.