AI Revisited - part 2
Episode
28 min
Read time
2 min
Topics
Leadership, Design & UX, Artificial Intelligence
AI-Generated Summary
Key Takeaways
- ✓AI as language alignment tool: Feed AI a corpus of real customer language — such as ~1,000 testimonials — then ask it to audit your copy for terminology mismatches. Fried discovered customers say "organized" where he wrote "stay on top of things." This closes the gap between internal product vocabulary and how buyers actually describe value.
- ✓Rapid prototyping without developer dependency: Use Claude to build functional local prototypes before assigning engineer time. Fried built a working "recent contacts" sidebar feature in Basecamp himself to validate the concept first. This avoids pulling teammates off priorities, especially critical when leadership requests carry unintended organizational weight regardless of stated urgency.
- ✓Synthetic test data generation via prompt: Instead of manually logging in as multiple users to populate realistic UI states, prompt Claude to generate weeks of multi-user chat history — including file attachments, varied message lengths, and emoji reactions — directly into a local database. This produces usable design context in seconds rather than hours.
- ✓Agent-first product strategy over native AI features: Rather than building custom AI features that may become obsolete, 37signals is developing CLI access and cleaner data interfaces so third-party agents like OpenAI or Anthropic offerings can operate as standard users inside Basecamp. Fried cites OpenClaw as an early signal that always-on personal agents will soon be mainstream.
- ✓Human support as competitive advantage at small scale: With a 62-person company where roughly one-third are engineers and designers, 37signals treats long-tenured support staff — some with 15 years on the team — as a structural differentiator. Routing all support through AI bots before humans is explicitly rejected as a model, with direct email to humans kept as the primary channel.
What It Covers
Jason Fried, CEO of 37signals, shares how he uses AI tools Claude and ChatGPT daily for writing, prototyping, and generating test data, while explaining the company's deliberate strategy of waiting before embedding native AI features into Basecamp, Hey, and Fizzy products.
Key Questions Answered
- •AI as language alignment tool: Feed AI a corpus of real customer language — such as ~1,000 testimonials — then ask it to audit your copy for terminology mismatches. Fried discovered customers say "organized" where he wrote "stay on top of things." This closes the gap between internal product vocabulary and how buyers actually describe value.
- •Rapid prototyping without developer dependency: Use Claude to build functional local prototypes before assigning engineer time. Fried built a working "recent contacts" sidebar feature in Basecamp himself to validate the concept first. This avoids pulling teammates off priorities, especially critical when leadership requests carry unintended organizational weight regardless of stated urgency.
- •Synthetic test data generation via prompt: Instead of manually logging in as multiple users to populate realistic UI states, prompt Claude to generate weeks of multi-user chat history — including file attachments, varied message lengths, and emoji reactions — directly into a local database. This produces usable design context in seconds rather than hours.
- •Agent-first product strategy over native AI features: Rather than building custom AI features that may become obsolete, 37signals is developing CLI access and cleaner data interfaces so third-party agents like OpenAI or Anthropic offerings can operate as standard users inside Basecamp. Fried cites OpenClaw as an early signal that always-on personal agents will soon be mainstream.
- •Human support as competitive advantage at small scale: With a 62-person company where roughly one-third are engineers and designers, 37signals treats long-tenured support staff — some with 15 years on the team — as a structural differentiator. Routing all support through AI bots before humans is explicitly rejected as a model, with direct email to humans kept as the primary channel.
Notable Moment
Fried reveals that 37signals already has AI agents operating inside their own Basecamp account as regular users — without building any custom integration. This happened organically, suggesting the "bring your own agent" model is already arriving faster than most product roadmaps anticipate.
Episode Transcript
Welcome to Rework, a podcast by thirty seven Signals about the better way to work and run your business. I'm your host, Kimberly Rhodes, joined this week by Jason Fried, CEO of thirty seven Signals. Now we talked a couple weeks with David about AI and this new energy around it, specifically how our programmers are using it internally. This week, I thought we would talk with Jason about his thoughts on AI and from a product perspective, where our thoughts are. So, Jason, before we talk about our products, let's maybe talk about just AI in general. Are you using this on a day to day basis, and what kind of use are you getting out of it so far? Yeah. You know, I use it for all sorts of different things. On the personal side, plenty of stuff. Business side, what I've been using it most for lately is actually as sort of an editor. Mhmm. Same. So I do a lot of writing. So I'm currently writing, for example, a newbasecamp.com homepage. And I wrote this piece. It's sort of a letter form, which is kind of how I approach these things. And I wanted to make sure that I was speaking really plainly and clearly, and I I thought I was, but I also wanted to really match it up with a lot of the language that our customers use very specifically. So we have this page on our site, basecamp.com/customers, which has about I think it's close to a thousand customer testimonials. Wow. And I use both Claude and ChatGPT. I kinda use them almost in competition with each other to kinda hone in on something because they each have their own style, and I don't like their house styles necessarily, but they can kind of somehow help me get somewhere that I'm comfortable with in a way that I like. So what I did was I pointed them both at the customer testimonial page and just said, like, internalize the language our customers are using, how they describe things, what they call things. Because for me, I might call a feature to dos, and and most people might call it tasks. Right. Now I noticed to dos, the tool in Basecamp is called to dos, but maybe people are calling it tasks, and maybe we should call it tasks. But I wanna make sure that what I wrote will land with more people and just land in their own mental model of what they're thinking about and how they're thinking about the product. So I asked it to internalize all the language and then read my letter and then make some suggestions for ways to tweak the language, not to tweak the letter or the tone, but make sure that I'm more aligned with how our real world customers speak about these things and call these things and name these things. So for example, I was using things like stay on top of things, …
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Books, tools, and gear mentioned in this episode
SignalCast may earn commission on purchases via these links. As an Amazon Associate, SignalCast earns from qualifying purchases.
Tools
- ChatGPTRecommended
by OpenAI
“Jason Fried, CEO of 37signals, shares how he uses AI tools Claude and ChatGPT daily for writing, prototyping, and generating test data”
- ClaudeRecommended
by Anthropic
“Jason Fried, CEO of 37signals, shares how he uses AI tools Claude and ChatGPT daily for writing, prototyping, and generating test data”
Products
other
“Fried cites OpenClaw as an early signal that always-on personal agents will soon be mainstream”
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