Let’s dive into the latest news, resources, and use cases from the world of product, UX, AI and technology:
⚒️ Building for Yourself
🙏 Too Much Faith in AI
😈 Sycophantic AI
➰ Human-in-the-Loop
💵 Pricing Strategy
❌ Product vs. Project
🤖 Harnesses
🔥 Burning Elon’s Money
Podcast
Build It for Yourself First: Todd Katcher on Internal Tools, AI Reality Checks, and Why the Best Product Doesn’t Win
In this episode, Kyle and Todd talk about how one conversation can change a company’s direction, and how Todd’s team builds its own tools when off-the-shelf software covers only 80% of what they need. That approach produced Zoney, Convert It, PriceAlerts, and an e-signature platform. Todd explains how he decides what to build, what to keep internal, and what to take to market, including why building their own CRM didn’t make the cut. They also take an honest look at AI: where it saves real time, where it still needs a critical human eye, and why managing expectations matters. The conversation closes on Todd’s biggest lesson: making the product is rarely the hard part. Getting people to buy it is.
News and Useful Reads
We’re putting too much faith in AI’s ability to say no
AI that goes beyond its guardrails or is generally disobedient is the stuff of science fiction. But we may be quickly approaching these scenarios regularly.
Curiously enough, disobedience doesn’t come naturally to the machine. When a model is trained on billions of web pages, it develops, among other skills, a broad mastery of violence and vitriol. What it doesn’t learn is how to keep those powers to itself.
What Sycophantic AI Can Cost Your Organization
When we outsource all our thinking and judgement to AI tools, the more likely we are to have unchallenged leaders and unchallenged ideas.
As organizations integrate AI into everyday work, they need to understand how its design shapes employee performance, human judgment, creativity, and organizational culture. Sycophancy—the tendency of AI systems to affirm users rather than challenge their thinking—is one potential risk, alongside reduced diversity of ideas and overreliance on AI-generated recommendations.
The “Human-in-the-Loop” Fallacy
Speaking of too much outsourcing, the idea that employees should just be checking the work of AI has it backwards.
As AI takes on more tasks, many companies are assigning employees to review its outputs and handle the exceptions it cannot manage. But this approach creates a shrinking role for human workers: As AI improves, the need for human validation declines, while reviewers risk losing the skills and attention needed to catch errors.
How Pricing Strategy Should Shape Your Product Roadmap
A roadmap isn’t just a list of what a team will build. It is a set of decisions about which customers to serve, what value to deliver, and how the business will sustain that value. Pricing is part of those decisions.
The Product–Project Paradox
I’ve written frequently about product thinking vs. project thinking. And it is one of the hardest balances to strike.
The more a product organization scales, the more delivery discipline it needs. But without clear accountability, that same delivery machinery can gradually crowd out the product thinking that created value in the first place.
Agent Harness: Definition
An agent harness (also called an agentic harness, or simply a harness) is the software around an AI model that runs its execution loop: it carries out the model’s requests to use tools, returns the results, and controls what information the model receives and what it is allowed to do.
Other Interesting Finds
Voters Set $400 Million of Elon Musk’s Money on Fire
There was a lot to be happy about this week in the US with elections. But Elon wasting almost half a billion dollars is icing on the cake.
Elon Musk almost single-handedly funded the Republican midterms push. But of every traceable dollar Musk put into races that have been decided, more than 99 cents went to a race he lost.