Alien: Decentralized Human Identity and Agent Trust for the Age of AI

Alien: Decentralized Human Identity and Agent Trust for the Age of AI

The internet was built without identity at the protocol level. Now AI makes it nearly impossible to know who or what you're interacting with online. Alien is a decentralized unique identity platform built on the principle of one human, one account — no government required, no central authority, just a cryptographically verifiable way to prove you're human and link that identity to the AI agents acting on your behalf. Mainnet is live now. In this episode of High Bit, Brett Gibson talks with Kirill Avery, founder and CEO of Alien, about why identity has always been the missing layer of the internet, how Alien combines biometrics, social graphs, and verifiable credentials into a single trust framework, and why the company built its own blockchain rather than deploy on Ethereum or Solana. Kirill also explains why 50 countries already have CBDCs and why building an alternative is the only real answer, what it looks like when one agent manages a thousand agents each managing a thousand more, and why proving you're human online will always be probabilistic — never absolute. Chapters: (00:00) Are you human? Why the answer is always probabilistic (00:19) What Alien is and why unique identity matters (01:08) Kirill's background: VK, bots, and the identity problem (03:11) Why the internet was built without an identity layer (05:01) How AI made the identity problem urgent (06:24) Why Alien needed its own blockchain (07:22) CBDCs, centralized identity, and why an alternative is necessary (10:30) Consensus mechanisms as voting machines and Bitcoin's honesty problem (12:15) How Alien ensures one person, one account (16:04) Biometrics, birthdates, and social graphs as layered verification (19:54) The Alien coin and how value accrues to participants (22:39) Why early adopter concentration is a design flaw (27:42) Agent hierarchies and why human-linked agent identity matters (30:06) How Alien uses AI internally to build with a smaller team (35:15) What's next: Mainnet phases, Alien coin, and Solana integration Follow Kirill and Alien for more: X:@kirillzzy@alienorg LinkedIn: linkedin.com/in/kirillzzy [http://linkedin.com/in/kirillzzy] linkedin.com/company/alienorg [http://linkedin.com/company/alienorg] Follow Brett and Initialized: X: @brettdg / @Initialized LinkedIn:linkedin.com/in/brettdgibson [http://linkedin.com/in/brettdgibson] linkedin.com/company/initialized-capital [http://linkedin.com/company/initialized-capital]

Azura: Building an Onchain Brokerage

Azura built a unified application for trading across multiple blockchains from one interface. You can trade tokens across Solana, Ethereum, Base, Arbitrum, Blast, and more without jumping between wallets, bridges, and apps. Try it: app.azura.xyz In this episode of High Bit, Initialized managing partner Brett Gibson talks with Jackson Denka, founder and CEO of Azura, about building an onchain trading platform that aims to feel more like a traditional brokerage while staying self-custodial. They dig into why crypto trading is still fragmented — and what Azura is doing about it. Jackson explains why DeFi is powerful but still hard to use, what it takes to unify DEXs and bridges into a single trading experience, and why cross-chain execution is such a difficult technical problem. They also discuss why he believes value in crypto will accrue at the application layer, why Azura built more of its stack in-house, and what it would take for crypto to disappear into the backend. Chapters: (00:00) One venue for every asset (00:46) What Azura builds (01:07) Jackson’s origin story (03:13) Why DeFi is hard to use (05:39) Why the app layer wins (09:23) Why onchain is next (13:05) One app across chains (15:45) Why this is so hard (17:12) Cross-chain execution (21:12) Hiring in DeFi (22:36) App revenue and DEX growth (25:44) Removing gas and networks (27:01) Self-custody, simpler UX (30:07) Building the stack in-house (33:13) How Azura uses LLMs (39:52) What’s next for Azura Follow Jackson and Azura: X @jacksondenka @AzuraTrade LinkedIn https://www.linkedin.com/in/jacksondenka/ [https://www.linkedin.com/in/jacksondenka/] https://www.linkedin.com/company/azuraxyz/ [https://www.linkedin.com/company/azuraxyz/]

Variant: What It Takes to Get AI-Generated Design Right

Code can be generated faster than ever. But getting that code to actually look good is a different problem entirely. In this episode of High Bit, Initialized managing partner Brett Gibson sits down with Daniel Bulhosa Solórzano, cofounder and CTO of Variant, about what it takes to build AI that gets design right, not just code that runs. Daniel started thinking about this problem in 2017 at Weebly, when the models weren't close to ready. After a stint in self-driving, he came back to it. Variant generates UI that's visually designed, not just technically correct, and shows you multiple options at once so you can find what you actually want instead of having to describe it upfront. Topics include: - Why AI struggles with visual quality even when the code works - The difference between precision and recall in design generation - Why showing people options beats asking them to describe what they want - Why people will always want to stay in the loop on design - How the role of designers changes as AI handles more of the execution (00:00) What design AI could eventually do (00:44) What Variant builds (01:25) How Daniel got here: Weebly, self-driving, and an unsolved problem (03:20) Why visual code generation is hard (04:10) Precision vs. recall and why design is different (06:55) What good design actually means (09:04) Landing page vs. dashboard: how context shapes design choices (11:31) What can be described vs. what has to be labeled (13:10) Casting a wide net for what good design looks like (15:25) Building the product around an imperfect model (17:04) Why people react to designs faster than they can describe them (19:29) Why people won't give up design to AI (23:40) What AI does to the design learning curve (25:06) Designers as design managers for agents (37:42) The 50%/80% horizon and what it means for engineering teams Follow Daniel and Variant: Daniel Bulhosa Solórzano X: https://x.com/bulhosa LinkedIn: https://www.linkedin.com/in/dbulhosa/ Variant Website: https://variant.com/ X: https://x.com/variantui LinkedIn: https://www.linkedin.com/company/variantui/posts/?feedView=all High Bit Watch more episodes: https://www.youtube.com/@InitializedCapital Follow on Spotify: https://open.spotify.com/show/36gTYrH1wlYzTZwLQywbIf

Trunk: Fixing CI at Scale (Merge Queues, Flaky Tests, and Shipping Code)

Code can be written faster than ever. But getting that code safely into production is where many engineering teams lose time. As organizations grow, CI failures, flaky tests, and conflicting pull requests start to compound. In this episode of High Bit, Initialized managing partner Brett Gibson sits down with Eli Schleifer, founder and CEO of Trunk, to talk about the systems that keep CI green and allow engineering teams to land code reliably as organizations grow. Before starting Trunk, Eli built developer infrastructure at Microsoft, started a company that was acquired by Google, and later worked with hundreds of engineers at Uber ATG. At Google he saw how powerful internal developer tooling could be. At Uber he saw engineers spend days trying to land code because those systems did not exist. That gap led him to start Trunk. Eli explains why engineering productivity slows once dozens or hundreds of engineers share the same repository, how flaky tests quietly waste engineering time, and how merge queues prevent broken builds and conflicting pull requests. Topics include: * Why CI becomes the bottleneck as engineering teams grow * How merge queues keep builds reliable * Why flaky tests waste engineering time * The build vs buy decision for developer tooling * How coding tools are increasing pull request volume * How engineering workflows are changing (00:00) AI fixing flaky tests (00:40) What Trunk builds (02:13) When CI becomes the bottleneck (02:33) Eli’s background: Microsoft, Google, Uber (04:29) Why dev tools must solve real pain (05:09) The merge queue problem (09:25) Build vs buy for developer tooling (14:22) How merge queues handle large codebases (18:58) How coding tools increase PR volume (21:53) Flaky tests and engineering productivity (25:24) Using CI and test history to debug failures (27:37) How engineers prune the search space (33:23) Engineers as conductors of automated systems (37:49) What’s next for Trunk Follow Eli and Trunk: Eli Schleifer X: https://x.com/elischleifer LinkedIn: https://www.linkedin.com/in/elischleifer/ Trunk Website: [https://trunk.io](https://trunk.io/) X: https://x.com/trunkio LinkedIn: https://www.linkedin.com/company/trunk-io/ High Bit Hosted by Brett Gibson, managing partner, Initialized https://open.spotify.com/show/36gTYrH1wlYzTZwLQywbIf

ZeroEntropy: The Hidden Bottleneck in AI. Retrieval, Not Models

AI models keep getting better, but most AI systems still fail in production. Why? In this episode of High Bit, Brett Gibson sits down with Ghita Houir Alami, cofounder and CEO of ZeroEntropy, to break down the real bottleneck holding AI agents back: retrieval. Ghita explains why embeddings alone can’t reliably surface the right information, why tools like Slack search feel so frustrating, and how rerankers add a critical second pass that dramatically improves accuracy. She walks through ZeroEntropy’s approach to training rerankers using pairwise comparisons and Elo-style scoring, and why this method generalizes across domains like code, finance, and biology. The conversation goes deep into: * Why AI agents fail even when the data exists. * How reranking fixes poor ordering from vector search. * Why “accuracy” now includes helpful context, not just correct answers. * What actually changes when retrieval becomes trustworthy enough to remove humans from the loop. If you’re building AI agents, search systems, customer support bots, or internal knowledge tools, this episode explains what’s breaking today, and what has to change for AI to work reliably at scale. (00:00) What changes when retrieval works (00:39) What ZeroEntropy builds (01:42) Why retrieval became the real problem (03:12) Why search fails (Slack included) (05:11) Why embeddings fall short (07:11) Rerankers: the missing layer (10:11) Why rerankers matter most (12:44) Pairwise ranking vs scoring (13:52) Elo scoring for documents (16:33) Fast rerankers via distillation (18:07) Why old training methods break (21:29) Retrieval for AI agents (24:20) Recency, memory, personalization (32:06) What reliable retrieval unlocks (33:42) What’s next for ZeroEntropy Follow Ghita and ZeroEntropy for more:X@ghita__ha@ZeroEntropy_AILinkedInhttps://www.linkedin.com/in/ghita-houir-alami/https://www.linkedin.com/company/zeroentropy-inc