James Cham

Sometimes, all it takes is to find kindred souls to feel like the world is an amazing place.

@nickgraynews you can totally create your version right now!

@jamescham I want this future! Love this idea

We are very close to the point when someone is going to release audio books that you can talk back to in order to save highlights and notes. But for now it is very easy to build your own version with the existing components.

Rather than presenting a list of best practices that probably don’t apply to your organization, you should keep a list of worst practices that just consistently don’t work anywhere.

@scottbelsky This is a good way of putting it!

anger expansion loops: in the age of attention-seeking rage bait algos optimized for polarization: when people are angry, they become MORE angry and LESS discriminate about who and what they are angry at. facts matter less, soundbites more. community notes must scale to address

I think people get to decide how they want to live and sometimes it might turn out that different solutions work for different personalities as we all desperately try to deal with the inevitable heat death of the universe.

Want to get thoroughly infected by a Dan Davies-style cybernetic view of the world? Read Tim Harford column on accountability sinks and get outraged. Then read the Davies essay on problem factories and you’ll painfully see how we ended up in this mess. (With a few ideas about what to do about it!)

Realized that iMessage is a SQLite DB on your desktop and basically open (not encrypted vs WhatsApp etc). Vibed a PRM (personal relationship mgr) for myself atop it, feel free to fork, if helpful: github.com/mobilerajs/iMe…

@pwang :)

@jamescham I got the reference!! I’m old 😞

“Go through the games of Ethan Mollick and create an IGN style review website of all of the games that he’s created. Call it AIGN.”

@oanaolt I love this!

@andrewparker @nchirls I agree that there is so much value with this pattern in general.

@nchirls - awareness (had no idea I was exposed to some names I knew) - double and triple dips in some names - GP sentiment now centralized in one place. Again, not rocket science, but so tedious. Thinking next extension is asking for light research on each name.

I'm an LP in roughly 25 VC funds. Using Claude to do a lookthrough analysis at exposure to underlying startups is so interesting. None of this is rocket science but it's such tedious work. I bet LPs, especially FoF, are having so much fun with this stuff.

@kevinakwok @andrewparker Not just “fun”. “So much fun”!

@andrewparker "fun"

Besides being probably among the best angel investors out there, Scott and Cyan really are just the nicest people.

@TheAndresRosa Me too! But I think we might be different from most people.

@jamescham N=1: love people; don't like solving social puzzles; love non-social puzzles.

You are optimized for enjoying and solving social puzzles rather than logic puzzles, and we should design systems accordingly.

@ttunguz This was a terrific summation.

Three years ago, we launched Theory Ventures with a simple premise : AI would reshape how software is built, sold, deployed, & operated. Within that world, we would build a concentrated, thesis-driven firm. The market moved faster than even the most bullish expectations after the ChatGPT moment. Frontier models leapt from delicate demos to production systems. Open source models have become substitutes for enterprise workloads. Inference emerged as the dominant market in AI. Underpinning all of this, AI compresses time. New models are released every 41 days. Companies reach $100m in revenue in record time. We all achieve more faster. In celebration of our anniversary, we wanted to trace that mechanism through the market shifts of the last three years. The first casualty of compressed time is the old language of venture capital. Seed, Series A, Series B categories still exist, but they describe the financial product companies seek rather than rather than company maturity. Venture firms have left the idea of offering a standard financial product to bespoke offerings : seeds range from $1m to $500m in size. Can we really call it all the same thing, anymore? Three years ago, a seed company was often a small team with a product concept & early signs of product-market fit. Today, some seed rounds are larger than IPOs, fueled by great ambition, a supportive VC ecosystem, & the promise of generational scale businesses to be built. Part of this is inflation in private markets. But more of it is time compression : the best companies mature much earlier than software companies did in prior generations. We’ve learned as an ecosystem how to build software companies & AI accelerates product development. Compressed time also redraws the map of where great opportunity lies. When we first launched Theory, most AI conversations centered on models. Remember the debate of whether model companies would be the airlines of the era? Today, inference is becoming the dominant market. The market is segmenting because the workloads & buyer preferences have evolved - very few companies can afford state-of-the-art AI for everyone - & each specialized constraint creates a new infrastructure category. Companies like @sailresearchco are building the systems that operationalize intelligence : serving it cheaply, routing it intelligently, & specializing it around use cases like video, batch, local, agentic, & real-time workloads. Databases followed this path a decade ago. They fragmented into OLTP, OLAP, vector databases, & streaming systems. Those markets have evolved with AI, a pattern we’ve backed through @motherduck & @lancedb , with @omni in the AI analytics layer above them. Inference infrastructure is now specializing the same way. The expense of inference reinvigorates a sedate market that has been controlled by behemoths for a decade : advertising. Every major interface shift, TV, web, mobile, streaming, found its answer to monetizing a massive audience in ads, & AI is no different. AI advertising is emerging as the subsidy for inference costs, letting applications grow usage & revenue together rather than against each other. We wrote about this dynamic when we led @koahlabs ' Series A : native ad formats inside AI conversations are producing click-through rates 4-5x the display baseline, & an agentic app builder can provide inference offset by ads. The same compression closed the gap between closed & open models, cloud models & local models. The conventional narrative holds that frontier closed-source models lead & open source follows. We’ve reached the iPhone 15 moment of AI. Many models are good enough for most work. Running a model locally reduces cost, improves latency, increases control, & minimizes data governance concerns. Enterprises are adopting local & open-source models for sensitive workloads, & frontier capabilities compress toward consumer hardware within a few years. What once required a hyperscaler cluster runs on a laptop just a few quarters later, a shift @ollama brings to millions of developers. The promise of AI is that software will ultimately be more secure : machines that read every line of code, patch faster than attackers move, & never tire. In the meantime, the attack surface is exploding. MCP servers, skills, plug-ins, & coding agents each introduce new entry points, & enterprises are deploying them faster than security teams can review them. Attackers are massively parallel & shrinking necessary response times from months to minutes. Defenses must respond. It’s why we backed @DropzoneAI , whose AI analysts investigate the alert flood no human SOC can keep up with, @Maze_Security , which applies agents to cloud vulnerability triage, & @artemis , securing the new agentic surface itself. The same agentic wave is rewriting operations. ERP & back-office systems have resisted change for decades because the work is unglamorous, the data is messy, & the switching costs are enormous. One CFO we interviewed, when asked about a startup said, “that company has only been around 15 years; they are too immature.” Agents invert that math. Systems that read documents, reconcile records, & execute workflows can attack operations from the inside rather than demanding a rip-&-replace. It’s the thesis behind Doss, rebuilding ERP for teams that move at modern speed, & Backops, applying agents to the back-office work no one wants to do by hand. AI has impacted crypto, another market fueled by data. Prediction markets, stablecoins, micropayments all have an AI infusion to them. Today, crypto companies need to generate revenue & use AI to provide better experiences, which led to our investment @AlliumLabs , the data layer underneath that institutional wave. Recognizing shifts early requires fingers on keyboards, wrestling AI agents into compliance rather than observing it. We built Theory as a technical organization, experimenting with AI across research, sourcing, diligence, portfolio support, & internal operations. Working inside these systems sharpens our understanding of where the stack is breaking & where new workflows are emerging, while deepening our empathy for founders deploying real AI systems inside enterprises. It’s harder than social media says. AI also changes the economics of an investment firm. Over the last decade, venture firms scaled by adding people. AI-native companies are demonstrating that much smaller teams can operate at 10x+ the leverage of prior software generations, & the same dynamic applies to us : since launch, we’ve analyzed 2x the investment opportunities with a team of just 3 investors working alongside a nine-person intelligence organization. None of this works without the team behind it. Theory started three years ago as a handful of people & a thesis. Today we are thirteen strong. We believe this is the structure of a modern venture capital firm : engineers & researchers who build the systems we use every day : agents that map markets, pipelines that surface companies months before they raise, & research infrastructure that lets a small team cover the ground of a firm several times our size. Everyone at @Theoryvc works with the technology we invest in, & that shared fluency shapes every decision we make. The firm we’ve built over three years is itself a product of the thesis : a small team, deeply technical, operating with the leverage AI makes possible. But the real story of these three years is the founders. They compressed decades of company-building into quarters & shipped products that rewrote what enterprises expect from software. The next three years will make these look slow. The most ambitious builders we meet are just getting started, & we can’t wait to see what they do.

At humans&, we train models from the long-term impacts of their interactions with people. This requires prioritizing long-horizon multi-agent RL. We've developed and are excited to share an open-source, hardware-native 4-bit RL recipe, significantly accelerating training

This is a great idea!

@NaithanJones Thank you Nait!

Been reading a ton of stories of bad VC behavior on here. I have plenty of those, we all do, but here's a story time about the opposite One thing I learned from working with founders over a decade: people make all of the difference and relationships are not singular transactions. They last decades and multiple contexts. After an 8 year tenure at a16z to jump back into building. I go on sabbatical then start working on a new idea around AI and IP rights in the age of generative media. At the time AI wrappers and frontier labs were sucking the oxygen out of the room (still are). An IP marketplace wasn't so easy to raise for, to say the least lol. Eventually we got traction and my friend @semil invests and intros to @roybahat of bloomberg beta. The call goes decently, but you never can really tell. One day I get a call and Roy says “I’m coming to Austin I want to sit down with you”. Very different vibe from most. We meet and never discuss the business that much. Ended up being a one hour meeting that goes three hours. Mainly we discussed life and out story etc. End of meeting "I'm in, send the docs" - ok off to the races we go Fast forward a little over year later. My once strong mom, my rock, suffers a stroke. A widow, she had the foresight to make me her decision maker in such a case. I become her primary care taker. I end up turning a corner of her hospital room into my office as we were launching to initial customers that month. Roy was one of first people to call me. We spoke about life again. Never the business. He immediately surrounded the company with extra resources from their network and even offered me support for the stress, including access to wellness retreat, expenses paid by the fund. Fast forward a year later. My mom passes away a week after we made the choice to shut down the company. One of the first calls again? Roy. Nothing about business "How can we support you during this time of grief" and "What can we do to help with your career transition". Their team jumped into action. I did not know Roy personally before I started the company. I have always believed in karma. I spent years trying to be as helpful as I could to founders doing a hard job while life happens simultaneously, and then it was me. Roy and Bloomberg Beta team as a whole are much more than a seed fund, they are truly people who see the human side of working with founders and teams. Reflected consistently in the dialog in the Bloomberg Beta founder Slack channel. in the meantime I'll be taking the learnings to spin up a fun side project with a few friends - a new fund vehicle to invest in creators and media rights (more on that in a few days) exploring new options (I'm open to new opportunities) The team at Bloomberg Beta really exemplifies what it is to work with true believers. So I wanted to publicly thank them

Thanks to Nait for such a kind note. Excited to see what he does next!