This week, we’re focused on the signal vs. noise, especially with AI. It is easy to get general direction from AI, but it lacks the nuance or edge cases that we really need to understand. And while companies continue to push the narrative of AI transformation, very few have changed. And even when the AI agents follow every step, they can still miss the mark.
Let’s dive into the latest news, resources, and use cases from the world of product, UX, AI and technology:
📊 Product-Market Fit
😵 Dead Startups
🥸 Synthetic Users
🔃 Company Transformations
🤖 AI Agents
🌇 Sunsetting Products
🍫 Halloween Candy
Podcast
Finding Product-Market Fit with Ken Gavranovic
Discover how seasoned technology executive Ken Gavranovic shares insights on building innovative products, leading digital transformation, and harnessing AI to drive business growth. Whether you’re an aspiring entrepreneur or a product leader, learn practical strategies to navigate and excel in the tech landscape.
News and Useful Reads
AI labs are buying data from dead startups to train their models
The most valuable part of a failed startup may no longer be the office furniture, but the data.
Demand for defunct companies’ data emerged suddenly last year, as AI training labs hit the limits of public and synthetic datasets. Surge AI, for example, buys data to build “reinforcement-learning gyms” where clients’ AI agents practice tasks—think debugging a complex IT architecture or drafting appellate briefs.
Synthetic Users vs. Human Respondents: Same Conclusion, Less Variation
I find this article and outcome really interesting: getting feedback from AI synthetic users can point us in the right direction, similar to human feedback. But AI has less variation and captures fewer of the edge cases. Not surprising, but interesting.
Data from AI-generated respondents pointed to the same conclusion as a survey of 604 humans but contained far fewer distinct answers.
‘Close to zero’: AI chiefs reckon with ‘politics projects’ and the level of true change in response to AI
We discuss this idea frequently here, but very few companies are truly changing because of AI so far. The type of change that everyone imagines takes more time and significantly more concerted effort.
Few large organizations have rebuilt their core operations around artificial intelligence, according to the technology chief of one of the world’s largest consulting firms. He also said companies shouldn’t yet spend much energy on assigning blame when AI projects go wrong.
Your AI agents can execute every tool call correctly and still break every enterprise rule
The prevailing market narrative rests on a simple formula. Agents, plus tools, plus data are enough. While no vendor explicitly states it, nearly every product pitch assumes it. The idea is that you drop your corporate data into a cloud data lake, wire up an off-the-shelf large language model (LLM), hand it a toolbox of enterprise actions, and agility follows.
It won’t. It does the opposite.
Managers Need to Get Better at Killing Digital Products
Sunsetting products is a difficult thing to do for most companies and product managers. Having killed off several products in my time, I can tell you it often takes more work to stop work on something than it does to get started on something.
companies need to retire products earlier and with more deliberation. Those that don’t will accumulate a graveyard of half-supported tools, funded out of the very budget that should back what comes next, along with a customer base frustrated at the brand and less willing to engage next time.
Other Interesting Finds
How Long Do You Need to Trick or Treat to Burn Off That Candy Bar?
I love Halloween and Halloween candy. So this article intrigued and also disappointed me. And now I’m sharing it with you so you can also think about it when you’re stealing some Halloween candy from your kids’ bags. In summary, your favorite candies will cost you:
Reese’s Pumpkin: 42 minutes
Candy corn (19 pieces): 35 minutes
Kit Kat (fun size): 18 minutes
3 Musketeers (fun size): 16 minutes