Brett Bohannon

421 teams connected to the Data Dive MCP in six weeks. Agencies and enterprise brands are building their own agents on top of Data Dive data, inside their own AI tools. Your data, your processes, your agents. What have you built with AI that saves real time?

Dessa=full-duplex speech model vs Human=me, block.xyz/dessa

What if society were just a little bit more optimistic about AI? During the dot-com bubble, lawyers were leaving $1M jobs to move to Silicon Valley and start companies. You had companies getting huge multiples with almost no revenue, sometimes with no business model. In some ways, that period was crazier than the AI boom today. Why? Most people weren't even using the internet. There wasn't even a mass market to go after yet. It truly was pure hype. In 1998-2000, sure people had AOL, but there wasn't that much business happening online. A lot of the excitement was based on what people hoped the internet would eventually become. And eventually it did become that. With AI, the usage is real, the revenue is real, the growth is real. What's interesting to me is how much less optimistic people are about technology today. In the 90s, people thought computers and the internet were going to be amazing for the world. Then we got social media, teenagers (and adults!) addicted to their feeds, governments using disinformation against each other, and people living in information bubbles that reinforce what they already believe. I think all of that has made people much less optimistic about technology. It's hard to compare the two periods. They're both pretty bubbly, but with a very different emotional backdrop. I'm not sure the optimism of the late 90s was any better than the pessimism we have today. I wish we'd had a little more pragmatism then, and I think we could use a little bit more irrational optimism today.

One of the worst parts of getting sick is a mental game. I am an always on kind of guy, lately I have been going really hard on physical training. Running, lifting, been head down training for a hyrox. This week has completely derailed me. I know one week will be ok. I have asked many of my personal ai assistants if I’m ok with missing weeks. lol

This did not age well for Amazon. Rough napkin math Muse downloads : 10,000,000 Let’s say 5,000,000 are actual users ( prob too low ) 5,000,000 x $200 = 1,000,000,000 ( 1 trillion ) Let’s say 30% of that could have been on Amazon : $300,000,000 in sales

JUST IN: Zuck reveals shopping has become the biggest use case for Meta's Muse AI, with over 25% of users shopping through the agent & average purchase exceeding $200.

all this productivity is messing with my flow

Foursquare was acquired last month. The outcome was disappointing. It's taken me a while to write this, but I think I'm finally done grieving it. Here are some thoughts on our final years, an effort to honor the team who helped build & maintain FSQ's legacy, and my attempt to finally get some closure. teendrama.substack.com/p/som…

Another takeaway after three years of building a company is that the returns to just "being a good person", and not acting transactionally, are higher than I expected. There are so many transactional, boring, and at times immoral people in the tech industry that if you act with sustained warmth, authenticity, humor, and integrity, great gifts will be laid before you. White pill.

bruh

I am really pushing the multiplayer features to their natural limit this morning. Also, I am a great leader.

Delta, Chase and Amazon are blocking AI agents. The war on agents has begun, and here’s how it probably plays out: Stage 1: Companies block agents because agents skip the ads and upsells. Stage 2: Agents learn to act like humans, and it becomes an arms race. Stage 3: The market splits. Companies that make money on sales let agents in, companies that make money on data charge for access, and the giants build their own agents. Stage 4: A trusted middle layer for agent identity and payments becomes the standard way in. Stage 5: Companies that welcome agents start winning customers from the ones that block them. Stage 6: The agent blockers come back and let agents in on their own terms. Stage 7: Everyone starts competing for agents with better prices, faster checkout and SEO built for agents. Stage 8: The ads come back, and companies pay to be your agent’s first recommendation. Stage 9: The agent war ends, and selling to agents becomes as normal as having a website.

Here's the challenge with modern SEO: you're expected to understand everything from technical performance to user experience. And the list keeps growing.

after an exciting week of launches (muse wonder [full] & muse spark open), i want to write a note of appreciation to the amazing research teams at meta superintelligence labs on reaching such a special milestone ♥️ we started MSL and TBD a little more than a year ago and set a goal of producing a frontier model within a year or so. given where llama4 landed, that was an incredibly audacious goal, and we knew it wouldn't be easy. many people told us it would be impossible. we knew it was unlikely, but still worth doing. a remarkable group of researchers and engineers joined the effort from inside and outside meta. every single person came in, picked up a shovel, and got to work with earnestness and urgency. nobody asked whose job it was. we assembled a team of the most brilliant people i have ever had the opportunity to work with (and remember, i was an HRT intern 🙃). i am surrounded by ambitious, visionary, technically excellent, hard-working, pragmatic, thoughtful scientists, engineers, and product folks—people who will chase a weird loss spike past midnight just because they need to know why. these models are the product of their passion, hard work, and brilliance. somewhat paradoxically, when we started TBD, we set a principle of no deadlines. we knew we had an astronomical amount of work ahead of us—across pretraining, post-training, RL, multimodal, infrastructure, safety, data, AND frontier research. there was no use setting arbitrary timelines—we just had to get to work, be thoughtful, and build the foundations of a frontier research effort in record time. and run after run, this team did exactly that. our researchers are magicians. i can only describe building these models with this team as a wondrous journey of scientific discovery. i am constantly amazed by what we can accomplish with scaling. i will remember this past year for the rest of my life—the late nights, the broken runs, the evals that came back better than any of us dared to hope. watching this team create wonder after wonder has been a true and deep gift. more than just the models we released this week, we have built a machine that builds new models. it often sounds like a cop-out to say labs can just turn the crank to keep scaling, but now i have seen it first-hand. sure, it's a lot of work and a careful process, but we have built a scalable system to keep advancing at the frontier, and i am really proud of how fast this team keeps making these models better. lastly, i get to work with researchers who care deeply about how their work benefits humanity. it's easy to forget when everything is changing so fast, but the whole damn point is to make people's lives better. time and time again, i have seen our researchers, engineers, and product folks build our technology with care so it can truly be good for the world—whether that's building the best models for muse, thoughtfully open-sourcing strong models, or making sure the alignment and security of our systems are rock solid. this is a team of people who never forget there's a real person on the other side of every prompt. thank you to everyone at MSL for being part of this crazy journey over the past year. i'm so grateful for every one of you, and our work is not done!