Welcome to High Bit, a podcast hosted by Initialized Capital managing partner Brett Gibson about the art of technical problem-solving.
A high bit is the most significant part of the binary representation of a number. In programming language, it is commonly referred to as the most important thing you need to understand about a problem.
Brett speaks to guests about just that. In each episode, they’ll break down a gnarly engineering problem and you'll hear how the builder’s ingenuity and inventiveness led to a successful outcome.
Guide Labs: Why AI Interpretability Has to Start at Training Time
Much of AI interpretability work begins after a model has already been trained. Guide Labs is taking the opposite approach: building interpretability into the model from the start.
In this episode of High Bit, Brett Gibson talks with Julius Adebayo, cofounder and CEO of Guide Labs, about why post-doc tools struggle to explain models with billions of parameters, and how changing the training process can make AI systems easier to understand, audit, and control without the performance trade-off the field has long assumed.
Julius explains how his research challenged the long-held assumption that interpretability and model performance are fundamentally at odds. He also gets into Guide Labs’ move from next-token prediction to diffusion language models, why that architecture may offer greater control, and what breaks when you scale interpretability from hundreds of concepts to tens of thousands.
The conversation covers the unexpected engineering challenges behind training interpretable models, from preventing interpretability loss functions from destabilizing training to dealing with duplicated training data, and Guide Labs’ goal of making every output traceable to its context, the concepts influencing it, and the training data behind it.
Chapters:
(00:00) Change the way you train your model
(00:59) What Guide Labs builds
(01:56) Why interpretability has been hard
(05:33) Julius's background: loans, X-rays, and a PhD
(07:04) The trade-off the field assumed was real
(08:01) Why he stopped trusting post-doc tools
(13:51) The three things that made them commit
(18:01) YC, the first models, and a team of nine
(20:19) Why language models were the hardest test
(23:10) Leaving next token prediction for diffusion
(26:41) Why GPT-3 wasn't just scaling it up
(28:00) 200 concepts by hand, 50,000 you can't
(31:25) Training lore and "divine benevolence"
(32:33) Setting a minimum bar to keep shipping
(34:33) How open to be about your own techniques
(35:51) The standard dataset full of duplicates
(39:31) Where AI coding tools stop helping
(41:43) What's next: the model they're training now
Follow Julius and Guide Labs:
X: @juliusadml (https://x.com/juliusadml) / @guidelabsai (https://x.com/guidelabsai)
LinkedIn:
Julius https://www.linkedin.com/in/juliusadebayo/ (https://www.linkedin.com/in/juliusadebayo/)
Guide Labs https://www.linkedin.com/company/guide-labs/ (https://www.linkedin.com/company/guide-labs/)
Follow Brett and Initialized:
X: @brettdg (https://www.youtube.com/redirect?event=video_description&redir_token=QUM4Zm9rVFJpeTNseDE3cmZOUG5pRjhuekdOWHxBR3JiS2FtOGR1SVlOT3Qzb1Z3RXRyZHBOaDllVkhteDRNSFhfU29XQ0FQRTVMTFVMR2pTNXl1eHBRZlFWSWxJZWdGX3dRNjV3LUU4d3psZUs5b245eVBrLS1UbGVFb3VPVV82&q=https%3A%2F%2Fx.com%2Fbrettdg&v=aaxjOK_FCHo) / @Initialized (https://www.youtube.com/redirect?event=video_description&redir_token=QUM4Zm9rUjNOY3lOR09lVzZ0Tl96dm9GUFRWWHxBR3JiS2FuU0VpNGtoRm5OelJXck1saVdBSUc4cUkydHdFY2pIMlBHU0Fzdm44NmNKdFczTEM1UW1KM3JRcnZvMENEVWV3X0loR2xfQUh0aHlWVDhZMEFYUHoyRzE3TXhESy1y&q=https%3A%2F%2Fx.com%2FInitialized&v=aaxjOK_FCHo)
LinkedIn:
https://www.linkedin.com/in/brettdgibson/ (https://www.linkedin.com/in/brettdgibson/)
https://www.linkedin.com/company/initialized-capital/ (https://www.linkedin.com/company/initialized-capital/)
6 Jul 2026
Greptile & Trunk.io: Rethinking Engineering in the Age of AI
For the first-ever live recording of High Bit, Brett Gibson (https://x.com/brettdg) sits down with two founders building at the center of the AI engineering shift: Daksh Gupta (https://x.com/dakshgup?lang=en), co-founder and CEO of Greptile (https://www.greptile.com/), and Matt Matheson (https://www.linkedin.com/in/matthewmatheson/), co-founder and CTO of Trunk.io (http://trunk.io/).
AI can write code. That part is no longer interesting. What matters now is what happens when engineering teams suddenly ship much faster, when review and testing become the bottleneck, and when the old operating model for software teams starts to break.
In this episode, Daksh and Matt get into how their teams are structured, why smaller teams can now do much more, what happens to project management when writing a ticket can take as long as shipping the feature, and why the best engineers in this era may look different than before.
They also discuss the tooling layer beyond code generation, from AI code review and CI to agents, testing, validation, and the “outer loop” required to get code safely into production.
The conversation gets into what engineering leaders should rethink now: team size, autonomy, hiring, process, token spend, and how to build teams that can move at AI speed without turning the product into a mess.
Chapters:
(00:00) Advice for Engineering Leaders
(01:07) Welcome to High Bit Live
(02:20) Matt's Path Through Microsoft, Uber, and Trunk
(03:08) Daksh's First Job Is Running Greptile
(04:00) Why Three-Person Teams Beat Everything Else
(07:55) Why AI-Native New Grads Changed Their Minds on Junior Hires
(11:41) When Tickets Take Longer Than Shipping
(16:05) Review and Testing Became the New Bottleneck
(19:40) Greptile's Three Questions for Code Review
(22:34) The Outer Loop Is the Next Bottleneck to Fix
(25:33) The Rise of the God Agent
(29:53) One Tool Led to 40 Internal Workflows
(36:05) The 10,000x Engineer and the War for Talent
(39:10) Two Hiring Archetypes That Beat Generalists
(40:16) Why You Can't Make a Heavy Company Light
(44:40) Why They're Not Maxing Out Token Spend
(49:07) Why Small Inefficiencies Now Cost 10X More
Follow Daksh and Greptile:
@dakshgup
@greptile
Follow Matt and Trunk.io:
linkedin.com/in/matthewmatheson
@trunkio
Follow Brett and Initialized:
X: @brettdg / @Initialized
LinkedIn:
linkedin.com/in/brettdgibson
linkedin.com/company/initialized-capital
29 May 2026
Picogrid: Building the Infrastructure Layer for Modern Defense
Integration is a dirty word in defense. The consultancy model that solved it for decades no longer works when you're dealing with hundreds of systems from dozens of vendors across land, sea, and airspace, all needing to work together within milliseconds.
In this episode of High Bit, Brett Gibson talks with Zane Mountcastle, cofounder and CEO of Picogrid, about how they built the hardware and software infrastructure layer that makes hundreds of defense systems work together.
Zane gets into what it actually looks like to deploy in the field, from detecting a quadcopter 50 feet off the ground, operating in GPS-denied environments with decades-old hardware and adversaries trying to trick your sensors.
Speed became their biggest differentiator. Their average integration time is measured in hours, and what used to take six to twelve months gets done over a weekend.
He also explains why trust is the biggest moat in defense, and how AI is now core to how they build, including training a model on every system they've ever connected so a first pass integration happens in seconds.
Chapters:
(00:00) "Integration is a dirty word"
(00:19) What Picogrid builds
(01:08) How Zane ended up working with the Pentagon
(03:11) The Last Supper: how defense consolidated
(05:01) Why hundreds of new defense vendors made integration worse
(07:22) Why the consultancy model no longer works
(09:41) Why speed is their biggest differentiator
(11:53) Each system has a language
(15:49) Four types of location data to find and track a drone
(19:54) Why Picogrid will never build a drone or sensor
(22:16) The military loves to buy widgets
(30:38) Trust is the biggest moat in defense
(34:20) Calibrating in the field: the camera that finds the sun
(37:36) First team in, last team out
(45:37) How AI is now part of everything they build
(47:00) $45M from Bessemer and what comes next
Follow Zane and Picogrid:
X: @zanemountcastle / @Picogrid
LinkedIn:
linkedin.com/in/zanemountcastle
linkedin.com/company/picogrid
Follow Brett and Initialized:
X: @brettdg / @Initialized
LinkedIn:
linkedin.com/in/brettdgibson
linkedin.com/company/initialized-capital
Reach and audience
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YouTube views
123,897
Score snapshot 15 Sept 2025
Apple Podcasts (US)
5.0 / 5
9 ratings
Spotify
5.0 / 5
6 ratings
Podcast Authority Score: 65 / 100
A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.
Quality
63
Social presence
0
YouTube
86
Engagement
82
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