Eric Weber @ericweberdata

LOVE that our friend @JLevering4 delivered the @Brewers version of the line Bob Uecker delivers as Harry Doyle when the Indians win the pennant in “Major League.” “The Brewers win the pennant! Oh my God, the Brewers win the pennant!” Pitch-perfect homage, Levo.

620 WTMJ @620wtmj

4 days ago

THE BREWERS WIN THE PENNANT The full call of the final out from @JLevering4, as heard on the Brewers Radio Network

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“Just use AI” is starting to sound as generic as “become data-driven.” ericdataproduct.substack.com/p/before-you-c… The enthusiasm is easy to agree with. Choosing what to build requires a clearer answer: what problem are we actually trying to solve?

BREAKING: Patrick Collison just told the Valley exactly where the next Stripe gets built. And it's not an AI lab. Asked what he'd build if starting from scratch, Collison pointed directly at legacy vertical software — decades-old systems still running healthcare, logistics, construction, finance. Entire industries. Still on 1990s infrastructure. Every ambitious founder is chasing foundation models right now. Collison is pointing at the opposite end of the stack. The most defensible businesses often come from the places everyone else decides are too boring to touch. Is the Valley sleeping on the biggest opportunity in software right now?

We are introducing Beam today, a 500b open model that excels in coding, agentic, and scientific workloads. Beam is very token-efficient, pre-trained from scratch, and scaled with the largest RL run documented openly we are aware of. Getting here was challenging, rewarding, and personal. I immigrated to the USA during childhood when my parents got jobs as scientists at PNNL, a national lab located in a rural town in Washington state. Growing up, I developed an interest in physics because it was grounded in simple principles, was foundational, and had long-term impact. Physics was the foundational science of the 20th century. The first general computer (ENIAC), the transistor, GPS, and much of modern technology had its roots in physics. In 2016, during my graduate studies, I noticed the birth of a new foundational science - Artificial Intelligence. AlphaGo, the superintelligent Go agent developed by DeepMind, had come out and made me feel what was about to come. A neural network, trained to imitate human players, and then improve itself through trial and error, mastered the most intellectually challenging board game in the world. What surprised me about AlphaGo is how it was conceptually simple, general, and scalable. The system could in principle improve itself forever, it was just a function of compute. This is when I became “AGI pilled” as they say. I switched from physics to AI because I thought this would become the root node science of the 21st century, much like physics was the root node science of the 20th century. I believe that working on foundational science is not enough on its own. Foundational science must also remain open and widely accessible. We remember Newton for his contributions to physics, but he was also an alchemist. Alchemy was done in secret, because the artificial production of gold was deemed lucrative but also dangerous with the potential do destroy entire economies. Newton even wrote his alchemy notebooks in cypher. On the other hand, Principia was published in the Philosophical Transactions of the Royal Society, the knowledge was made widely available, passed down for generations in what we now call Newton’s laws. The computer, transistor, GPS were possible to build because physics as a science was open. Openness creates an ecosystem of innovation. The notion of openness, which is a core tenet of scientific discovery, has also become a guiding principle for how foundational technology gets built and distributed. The internet was built on open protocols. The most widely used operating systems are open. The field of cybersecurity exists because strong encryption protocols were made open in the 1990s. Open also means safer. The best defence is a distributed one. Commonly used open source software is robust and battle-tested because it is inspected and patched by a community of developers. AI is different but also similar to software in many ways, though with a much larger surface area of vulnerabilities. It is impossible for a small group of safety researchers working in secrecy to remediate all of the potential issues regardless of their intentions. It is like entrusting a handful of white blood cells to protect your body. AI is both foundational as a science and a technology, and the safest way that also ensures the benefits of AI are distributed evenly is by making it open. I’m excited for what developers and scientists build and learn from this model, and for Beam to play a small part in ensuring that AI is made widely accessible, safe, and beneficial to all.

Reflection @reflection_ai

2 weeks ago

Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active. - Frontier reasoning efficiency - Advances the Western open frontier on coding & agentic tasks - Trained end-to-end from scratch Full weights release this month. Learn more about

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@athyuttamre I love it as a brainstorming partner. Voice with different modes like brainstorm would be really useful

@johnclevenger @thsottiaux Computer, phone, but perhaps it's just time to go cloud.

@johnclevenger @thsottiaux It doesn't feel easy!

@realpaulsmith Talent asymmetry is a huge issue and glad to see this.

I found this really helpful!

Gokul Rajaram @gokulr

3 weeks ago

Defensibility in AI Data: Lessons from Ads (Warning: LONG POST) Companies selling data / expertise / RL environments to AI labs are growing at extraordinary speed, with several reaching tens to hundreds of millions in annualized revenue within a year. But venture investors

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Lots of founders are crashing out A year ago AI felt like an incredible opportunity. Most of us were able to ship faster, remove admin, and get time back. Lots of engineering founders were even able to explore marketing for the first time: SEO, organic social, outbound. Building complex systems that felt extremely productive. But since then, a lot has happened. Here’s some of the themes I’m seeing: AI marketing isn’t working. At first it feels impressive and it looks good but for most it’s not driving results. This is made worse by the fact everyone has access to the same tools. Inboxes are flooded, buyers are fatigued, the economy is flat. Engineers are fried. Yes you can ship more but for many the flow state has gone. It’s a new way of working and it’s not for everyone. The speed leaves many of us exhausted before 11am. There’s a feeling of what’s the point? Is my product’s next feature going to be redundant in a year, or a month? Can’t AI do what my business does, better? SaaS valuations have collapsed. From 3-4x revenue to 1x. There’s a sinking realisation that a large number of SaaS companies will go to 0 in the next few years. Building to exit seems almost crazy right now. There’s far fewer buyers and far more sellers. These include vibe coders cloning products without the care or craft, contributing to the distribution challenges from people doing it the right way. Lots of people are trying to make money by selling shovels. This just adds to the frenzied energy. Build in public stopped being fun. A few makers realised that the new game is attention, and now everything feels insincere and stunt-driven. They are influencers not founders. Changes to the X timeline compounded the issues. There are exceptions and there are still moats remaining, but the challenges feel existential

@joedaroo Thank you for taking the time to write this. It made a meaningful difference for me in how I’m thinking about this.

@Miles_Brundage This at scale will produce the real backlash against AI from America. That, or people find out data centers run off Diet Coke.

Databricks CEO @alighodsi went off on @a16z pod about enterprise AI adoption: "They're just so far behind in the adoption curve of actually automating things and getting value out of this stuff." Ali says most companies are still just using chatbots. There's hardly any agentic transformation. Why is that? "The models are smart enough, but they just don't have the context that exists inside of any organization." "They have not been in every meeting. They don't know what's in everybody's heads. They don't know all the processes." "If you just fused that and gave that context into the AI models...there's so much productivity gains you could get for any organization on the planet." Context Creation is the biggest opportunity in AI right now.

100% agree. Great speech.

Scott @scottstts

3 weeks ago

My god this is such a good speech that every SWE needs to hear. You know what? Every person should hear it Keep the happy memories, eyes on the reality, be excited about the future. That’s the best that anyone can do

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This was the key for me. The only limits now are how much I can think about doing. It turns out that is its own learning experience.

sarah guo @saranormous

4 weeks ago

just got off the phone with a new engineer hire at a portco with a very wise insight “AI has been replacing my job since I got a CS degree, yet I’m busier than ever - we can all just be more ambitious” this is the same insight that the inimitable @JensenHuang has for us all

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People outside the AI labs should have a real say in how this technology develops, and a clear way to judge if it's happening safely. Standards should help prevent the concentration of power, including by making sure new companies and open-model companies can compete. They should also help countries and companies compare evidence and learn from failures. We think the US should lead this effort. Here is our proposal: openai.com/index/building…

"The average person has fewer tasks" is right. I think we also overestimate the degree to which people value "productivity". The sweet spot is in the magic of the experience. Does it solve a pain, and does it feel easy doing it?

Alex Kehr @alexkehr

4 weeks ago

I think Silicon Valley is making a fundamental mistake with personal agents… optimizing entirely for the outcome and forgetting that, for a lot of things, the process is a very core part of experience. Travel is the obvious example. People will almost always never want “book me

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Really good points.

Venky Ganesan @venkyganesan

4 weeks ago

A few thoughts on the current state of venture capital. When the Music Is Playing In July 2007, a few weeks before the credit markets seized up, Chuck Prince, then the CEO of Citigroup, gave an interview to the Financial Times. The line everyone remembers is this one: "As long

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@Jason I think it meets the moment for agentic money movement worldwide. Just a hypothesis, but early signs are there as a uniquely strong use case.