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HOUSEKEEPING 📨

I surprised myself this week. While being tired, sore, sunburned, and hungry, I managed to squat an (equal, I think) all-time front squat best of 135kg (around 300 lbs). My slow and steady trudge towards my lifetime lifting goals of a 120kg snatch and 150kg clean and jerk are well and truly still alive.

In other news, I find holidays kinda hard, especially ones like the one I am currently on, which involve sitting around, relaxing, and spending time with your thoughts. I guess it’s healthy? But, as someone whose mind runs at a million miles an hour, it’s been a challenge to slow down. What about you?

As I have gotten older, I’ve begun to think of myself as an athlete, just one that is competing in the sport of business and tech. And because of this, more of my time and energy (and money) is going towards training, recovery, health, and wellness. Maybe, I need to spend a bit more time in this low-stress mode to really allow myself to get comfortable, and give it time to sink in. Enjoy today’s piece!

INTERVIEW 🎙️

Bryan McCann, Co-Founder at You.com

Bryan McCann is the Co-Founder at You.com, the AI-powered search and research platform built to give enterprises reliable, real-time access to information across the open web. Before founding You.com, Bryan was a Lead Research Scientist at Salesforce Research, where he authored the first paper and holds the patent on contextualized word vectors; work that eventually led to the transfer learning revolution in NLP with BERT and other transformer-based architectures.

Bryan’s work stems from a deep philosophical interest in meaning and the desire to use AI to complement human creativity, inspire new thoughts, and develop tools for more fulfilling lives and a more complete understanding of the world. He holds degrees in Computer Science and Philosophy from Stanford, has self-studied Latin, and was more than once moved to tears by a mathematical concept. He is, in other words, exactly the kind of person you'd want thinking about what the internet should look like when the agents take over.

What’s the problem you're trying to solve with You.com? And why this?

As agents become more prevalent and there is a growing effort to have them do higher-stakes work and make higher-stakes decisions, the need for them to be trustworthy and reliable is increasing proportionally. The problem is that large language models, at the core of AI agents, are instantaneously out of date as soon as they stop training. So, until someone figures out continual training and continual learning, deployed models still need to call out to search and data APIs of various kinds, not only to provide information in a summarized, search-oriented way, but also to support actual knowledge work and agentic use cases. You wouldn't have expected a human, even ten years ago, to make high-stakes decisions for your organization without having access to something like a Google search to do some research and figure out what's going on in the world. You'd want them to have the freshest information possible.

So what we primarily do is crawl and index the public web, and provide an API for basic search. That basic search can then be augmented with the full contents of a page or key snippets from a page.

Google never really did, though many people used third-party SERP APIs that would take a query, run it against Google, crawl the results page, and send those results back. But in that case, you're sending all your queries to Google, and some companies don't want to do that, especially in enterprise settings. If you're an AI lab that is perhaps competing with Google on foundation models, you really don't want to send them all your data, or you may simply need better reliability or the ability to handle more variable traffic. Large consumer-scale applications will have fairly continuous traffic, but if something goes viral, you need to be able to handle that kind of spiky behavior. Similarly, if you're an AI lab training a model, that creates spiky behavior too. So there are all these new use cases, and at the foundation of all of them is the idea that if more agents are doing more work than humans ever did, then they will be searching for information more than ever before. AI in its current form has not made search less useful or less important; it has actually amplified the importance of search, data, and all the other tools we build to support these agents.

Can you tell me about a real-world use case with a customer?

One great example is that we take a slice of our general web index, scoped specifically to the news domain, and serve all of the US news traffic for DuckDuckGo, a privacy-focused search engine that has been around for quite some time, and they rely on us for all of their news in the US. We are also doing a global expansion with them as well. Beyond that, many customers have adopted use cases around product research and competitive research, helping them understand where their own products sit in relation to the market, and what their competitors are doing in similar spaces. Some of these use cases fall into consulting or strategy-style research. Media and news companies also frequently use our APIs to do background research.

Hospitality is another interesting area. Players in that space need to establish some sort of independence within the open ecosystem, or risk facing the ongoing trend of Google (with Google Flights) and now ChatGPT, both presenting what could be an impending disruption for the independent travel industry, at least when it comes to bookings. The physical experiences underneath, of course, remain their own.

How would you describe You.com’s operating culture?

The team and the culture here revolve around a builder's mindset: a desire to take on significant ownership and responsibility. That means my org is filled with people who want to do more and take on more, which has been a real asset in keeping things fairly flat. Most people want to stay as close as possible to working with me or on the latest initiative, until they get their own big project where they can be the lead, make it their own, and grow and scale it. That has worked quite well for us over the years, and it has also allowed us to adapt fairly dynamically. We started out as a consumer-facing company and are now very much not just a B2B company, but an API company. The org has evolved with each shift in what we do.

I often use the metaphor of building my organization the way you would build a neural network. You want good information flow up, down, and across layers. You might want skip connections, or what we'd call ‘residual connections’ in a neural network. It's really important to set the objective clearly so that the loss function—the error signal—propagates down through the network and drives change toward that objective, while the more fundamental layers remain more stable.

I think that approach has helped us adapt fairly easily. One thing that has been particularly helpful more recently, and this may sound basic, is regularly revisiting the right grouping of people for each team, and clarifying which tools and metrics they own. As we've matured, that has become much steadier. What used to change every month, then every quarter, now perhaps changes every six months. We're trying to hold onto that nimbleness, double down on what's been working, and give ourselves more clarity into how the work should look going forward.

How does a company like You.com think about working with agentic coworkers?

The first thing is to have the mindset that you should use agents for just about everything. Many of our engineers have told me that they barely write new code anymore, that they're mostly interacting with Claude Code, or Cursor with Claude running, or something to that effect. Their time is already spent much more on thinking about what they should be doing to have impact, rather than on implementing and writing the code themselves. That has been a huge shift. A year and a half ago, I would have said we hardly used any coding tools, and people mostly thought they were great for boilerplate at best. Now, some people are not writing code at all. That has been very interesting to watch.

It has also been interesting to see how this extends beyond engineering. As a founder, I have been actively encouraging our sales and marketing teams to use these tools as well.

The mindset I try to instill is similar to something I carried over from my time in research: the goal was always to keep the GPUs running, because the more they're working, the more they're working for you, and the more you can learn and adjust your direction. It's the same idea here: always have something working in the background, and instill that mindset in people across every department, regardless of which specific agent they use. In terms of structure, it still primarily manifests as one-to-one: individual employees working with an agent to accelerate their own work. We don't yet have many multi-agent systems interacting across multiple employees, or anything more complex than that. I'm curious to see where that goes. It seems like multiple agents should eventually just live inside Slack, or whatever interface you use, but we haven't gotten there by any means yet.

Where do you think we'll be in five to ten years working with agents?

My time in research really embedded in me the idea that **data is king. My time at You.com, and watching AI have its moment, has only reinforced that, but I think I've extended the analogy a little bit to say that everything about a chessboard matters. Data is king, and AI is the queen on the board now. It moves around a lot and gets a lot done, but I think a company's strategy, or an individual's strategy, still revolves around the king—around data.

If you take that idea and apply it to what we're seeing with agents, knowledge work, and everything else, I divide people into roughly two groups. There will be people doing some form of work that generates data to train AI. And then there will be people evaluating that AI, either through actual benchmarks, evaluations, and harnesses, or in a fuzzier way, simply assessing whether it is good enough or strategically useful to them. Everything else will mostly get abstracted away. So you'll either be creating data for AI through your knowledge work—and most knowledge work will become some form of that—or you'll be on the evaluation side.

Engineers, for example, will remain valuable as long as they can produce code that is genuinely better than what the AI produces without them, in which case that becomes training data for the next iteration, until it no longer is. Or they'll be evaluating whether that's true and setting the direction in which AI should continue to learn. Even if you think of yourself as an AI manager or as a CEO of a swarm of agents, I would broadly categorize that as evaluating whether the AI is doing a good enough job and setting its direction.

How do philosophy, maths, and Latin shape artificial and human intelligence?

Mathematics probably has the biggest influence. Most of how I think about people stems from principles and abstractions I draw from some form of mathematics. I was at a philosophy group here in New York just a few weeks ago, and we were trying to discuss growth: how does growth differ from change, and various derivative questions that stem from that. My natural framing was to think about change in terms of metrics you can define and measure, and growth as some combination of a metric with a notion of accumulation. We talked about the example of a person who works really hard on themselves and seems to grow in this abstract human way until, say, a traumatic brain injury causes them to lose their memory. They grow after that too, but they're almost like a different person. Would you say the second version of them grew out of the first? It seemed like, perhaps not; that if their memory has a break in it, there's a discontinuity, and we might say they grew up until a certain point, that growth was continuous until the discontinuity, and then there is growth after.

We ultimately worked our way around to another framing entirely, but even the idea of continuity, and of topological spaces, whether we can separate two points in space in various ways, helps me understand things like quantum entanglement, or how people relate to each other and negotiate their relationships in business, work, and all aspects of how we socialize. So mathematics has been deeply influential.

It also grounded me in an important problem in natural language processing: English is by far the dominant language in most training datasets, meaning many other languages—what we call low-resource languages—are underrepresented. Learning Latin and then reading texts like the Aeneid in that language made them hit differently. It genuinely convinced me that it is not as simple as just teaching language models a language like English. Even English itself is lossy compared to our full human experience, meaning it's lossy in how much you can truly understand the world through it. I had heard arguments that it would be wonderfully efficient if the world converged on a single language; no need for translations, no friction. But this experience convinced me otherwise. There is so much of another kind of meaning that we would lose in that process, and I'm really glad I came to understand that firsthand.

What book or idea has been most present in your thinking lately, and how’s it influencing You.com?

I really love some of the books by an author called Italo Calvino. He takes some sort of fantastical premise and then writes the story that follows as if it is completely normal and logical. Take The Baron in the Trees, for example: a young boy, for reasons stemming from his sister's unkindness, runs up a tree, hides, and never comes down. That's the premise. But the rest of the book is essentially: okay, what does that actually look like? Nothing else fantastical happens; he just never comes down. It turns out he inherits the title of Baron, acquires land, connects all the forests across that land so he can get around everywhere, and ends up meeting Napoleon Bonaparte along the way.

I love things like that because these seeds of fantastical ideas can turn out not to be that fantastical at all. Where we are today, compared to where I started in research, would have seemed pretty crazy back then. I wrote essays about it at the time because I thought I could see it coming, but it still felt so abstract. I think that's a useful lens for thinking about where we're going. I would also say that the concepts in the mathematical branch of topology have been increasingly important to me over the last several years. And perhaps most motivating of all is simply the reminder that we still know very little. That feeling of how much remains to be discovered, I find that particularly energizing.

How do you get the best out of yourself personally and professionally?

I'm going to emphasize the basics. When I was in research, the lifecycle revolved around deadlines, and you might sprint really hard around one of them. But when I found myself leading teams more, I realized it was more important to show up every day as the best version of myself, rather than sprinting toward a deadline and then being completely spent for a week afterward just recovering. So focusing on sustainability is key. That's not to say there aren't times when you push extra hard, but it's almost like you want to be constantly building up your tolerance and getting better, so that even those harder stretches don't seem as daunting. Sleep, eating well, taking care of your body and your mind, those things are extremely important.

Even if it means putting it in your calendar, scheduling a call with someone, or going to do that thing you know you used to love, just keep those things alive. That's how you run the marathon. And finally, making sure that you remain very human in a very AI-centric world is something I find helpful, too. I think that goes a long way in bringing back a sense of grounding when things get tough.

Extra reading / learning

• You.com’s CTO: An AI Visionary a Decade Ahead - June, 2025

• Bryan McCann on Productivity, Proactivity, and the AI-Powered Workforce - February, 2026

• Will AI Ever Create Its Own Meaning? - October, 2025

And that’s it! You can follow Bryan on LinkedIn or check out You.com on their website to keep up with what they’re building.

BRAIN FOOD 🧠 

TWEETS OF THE WEEK 🐣 

TOOLS WE RECOMMEND 🛠️

Every week, we highlight tools we like and those we actually use inside our business and give them an honest review. Today, we are highlighting Vanta*—the security and compliance platform companies rely on to stay audit-ready without losing their sanity.

See the full set of tools we use inside of Athyna & Open Source CEO here.

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