New Englander living in the South. Love the Pats, Sox, Bruins and Celtics ... and Atlanta United.
Atlanta, GA
Joined May 2009
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I guess people would rather use relatively free “fake” images than expensive real images: Getty Images files for Chapter 11 bankruptcy after struggling with mounting debt & growing competition from AI-generated images.
The first AI win is often speed. The durable win is fewer handoffs, fewer rework loops, and clearer ownership.
A fast answer is not a finished task. The task is finished when the answer survives review, reaches the right place, and creates the next action.
AI adoption usually starts with a model. Real adoption starts when someone owns the workflow around it.
Great interview. Provides insight into some of the challenges and strategic decisions many startups face going forward.
Jean-Denis Greze was CTO of Plaid. His next startup, an AI tax product for small businesses, brought in nearly $1M in revenue its first year, and he projected $2.5 to $3M the next. He shut it down anyway.
The reason: in the age of AI, it was never going to be venture scale. Incumbents could use the same technology to automate tax prep for their existing customers faster than a startup could win market share. Tax is also sold once a year, so he had about a one-month window to pivot before the door closed.
The pivot became Town, an AI assistant that works from your email and calendar. It found product-market fit fast enough that Ben Horowitz messaged a16z's Alex Rampell to say it "changed my life."
In this conversation, Alex and Jean-Denis cover:
- How to know when to kill a business that's working on paper
- Why Town competes with OpenAI and Anthropic, the companies it buys its tokens from
- Why the "forward deployed engineer" model gets worse as AI gets better
- How a $1/month AI routine can save a restaurant thousands
- What agents mean for Amazon, advertising and the future of shopping
0:57 How Ben Horowitz discovered Town
2:20 Why Town started with AI tax prep
3:55 Why tax couldn't be venture scale
6:30 Shutting down a working business
10:24 The customer ask that became Town
13:35 Why Town built for users before IT
16:18 AI's iPhone moment at work
21:28 Jevons paradox for executive assistants
23:13 Town as a "token allocation machine"
28:00 How Town tailors itself to each job
34:03 Competing with your token suppliers
42:24 Staying off the AI labs' radar
44:42 Why AI isn't spreading evenly yet
52:32 How agents break the ad model
55:35 The friction humans actually enjoy
57:05 Why supply aggregators lose to agents
YouTube: youtu.be/o_5f4N6Nq80
@jgreze @TownAI @arampell
“The key is experimentation. You bring it to all your tasks, and you'll learn what it's good or bad at very quickly.” — Ethan Mollick
The hard part of AI adoption is not access to a model. It is redesigning the surrounding workflow so the model’s output can become accountable work.
Ian Stinson retweeted
Your workflows have changed with AI. How about your setup?
Physical controls are back. Take the new Logitech MX Keypad, now directly integrated with the GitHub Copilot app.
This experience allows you to run agentic workflows with control, map a repeated prompt, start or resume a session, and help you stay in flow.
github.blog/news-insights/co…
Ian Stinson retweeted
Gemini 4 Argon is now globally available.
Built to solve your hardest problems from start to finish, the model brings frontier capabilities across coding, enterprise workflows, and cyber defense.
The transition will feel uneven because AI changes tasks faster than institutions change training, pay, credentials, and career ladders.
Look at him cry! Ha.
Chinese bride gifts her partner a flagship GPU at wedding — he breaks down in tears
She handed him an ASUS ROG Astral RTX 5090 that costs around $12,000
