Coy Byron

Building DoMoreWorld — AI for purpose, not just productivity. Father. Builder. Poet.

[Coy Byron @coybyron]

Productivity with purpose is the path to abundant prosperity.

[Coy | TheDigitalCoy @TheDigitalCoy]

https://t.co/CouWbTdJP9

[Coy Byron @coybyron]

#NewProfilePic

[Coy | TheDigitalCoy @TheDigitalCoy]

https://t.co/CouWbTdJP9

[Coy Byron @coybyron]

@minchoi This is hilarious but not outside the realm of possibility.

This potential for so many futures is why I'm building what I'm building.

[Min Choi @minchoi]

This AI ad is wild 😂
https://t.co/Oy2Sencny8

[Coy Byron @coybyron]

@MagicalPep I completely agree with this. Fermin has the potential to reach Frank Lapard level mode - I really hope he doesn’t leave but have a bad feeling he will and Barca will have big time regrets 🫠

[MP𖤐 @MagicalPep]

🚨Pep Guardiola

🗣️: “Fermín López has something special — he sees space you can’t teach. Players like him are rare, Barca would be smart never to let him go; he can define their next generation.”

[Coy Byron @coybyron]

The World = A Tale of Two Cities

[Coy Byron @coybyron]

@0xsachi or just AI instead

[Coy Byron @coybyron]

@0xsachi get in early - most of our best ideas form before the age of 21 and with AI tools the way they are, anyone can now execute on them, so this makes sense to me

[Coy Byron @coybyron]

@AlgoSnafu @sama lolz

[Coy Byron @coybyron]

@avniyayin @AlgoSnafu @sama gov't revenue is from taxes so ... but if the gov't becomes more efficient, then as a tax payer, we benefit so there's that.

[Coy Byron @coybyron]

@0xsachi cycles

[Coy Byron @coybyron]

@0xsachi This is such a weird thing - don't quite understand it.

[Coy | TheDigitalCoy @TheDigitalCoy]

Gn

[Paul Graham @paulg]

I just talked to a startup whose market cap, if they succeed, will be so large that we didn't even try to estimate it. It would just sound like fiction to investors.

[Coy Byron @coybyron]

@pmarca This is one is maybe even worse 🫣

Top-10 wealthiest ~10% growth annually
Rest of world ~ 4% growth annually

[Marc Andreessen 🇺🇸 @pmarca]

S&P 10 vs S&P 490 🫣

[Coy Byron @coybyron]

Sad to see what’s going on w/ Barca and Ter Stegan 😞

[Andrew Ng @AndrewYNg]

The invention of modern writing instruments like the typewriter made writing easier, but they also led to the rise of writer’s block, where deciding what to write became the bottleneck. Similarly, the invention of agentic coding assistants has led to a new builder’s block, where the holdup is deciding what to build. I call this the Product Management Bottleneck.

Product management is the art and science of deciding what to build. Because highly agentic coding accelerates the writing of software to a given product specification, deciding what to build is the new bottleneck, especially in early-stage projects. As the teams I work with take advantage of agentic coders, I increasingly value product managers (PMs) who have very high user empathy and can make product decisions quickly, so the speed of product decision-making matches the speed of coding.

PMs with high user empathy can make decisions by gut and get them right a lot of the time. As new information comes in, they can keep refining their mental models of what users like or do not like — and thereby refine their gut — and keep making fast decisions of increasing quality.

Many tactics are available to get user feedback and other forms of data that shape our beliefs about users. They include conversations with a handful of users, focus groups, surveys, and A/B tests on scaled products. But to drive progress at GenAI speed, I find that synthesizing all these sources of data in a PM's gut helps us move faster.

Let me illustrate with an example. Recently, my team debated which of 4 features users would prefer. I had my instincts, but none of us were sure, so we surveyed about 1,000 users. The results contradicted my initial beliefs — I was wrong! So what was the right thing to do at this point?
- Option 1: Go by the survey and build what users told us clearly they prefer.
- Option 2: Examine the survey data in detail to see how it changes my beliefs about what users want. That is, refine my mental model of users. Then use my revised mental model to decide what to do.

Even though some would consider Option 1 the “data-driven” way to make decisions, I consider this an inferior approach for most projects. Surveys may be flawed. Further, taking time to run a survey before making a decision results in slow decision-making.

In contrast, using Option 2, the survey results give much more generalizable information that can help me shape not just this decision, but many others as well. And it lets me process this one piece of data alongside all the user conversations, surveys, market reports, and observations of user behavior when they’re engaging with our product to form a much fuller view on how to serve users. Ultimately, that mental model drives my product decisions.

Of course, this technique does not always scale. For example, with programmatic online advertising in which AI might try to optimize the number of clicks on ads shown, an automated system conducts far more experiments in parallel and gathers data on what users do and do not click on, to filter through a PM's mental model of users. When a system needs to make a huge number of decisions, such as what ads to show (or products to recommend) on a huge number of pages, PM review and human intuition do not scale.

But in products where a team is making a small number of critical decisions such as what key features to prioritize, I find that data — used to help build a good mental model of the user, which is then applied to make decisions very quickly — is still the best way to drive rapid progress and relieve the Product Management Bottleneck.

[Original text: https://t.co/1tulDs3k7U ]