A $200 Claude subscription can deliver almost $10,000 worth of usage, but Jonathan Archer thinks those economics will not last.

Jonathan Archer is co-founder and CEO of Open LLM, a gateway designed to put multiple AI models, subscriptions, and API keys behind one layer. Before Open LLM, he spent roughly six years advising crypto projects across business development, growth, and community. He met his co-founder at the Avalanche Summit in Buenos Aires before the pair began building side projects together.

Open LLM started with a simple frustration: hitting AI subscription rate limits and then wasting time maintaining open-source alternatives. Jonathan’s thesis is that the future will be multi-model, not dominated by one model or one interface. Different models already have different strengths, and users should be able to combine them without constantly moving between products.

That leads to his more provocative argument: AI companies should not trap users inside proprietary harnesses. “It’s their tokens,” Jonathan says. Open LLM is being built so users can bring subscriptions and API keys, maintain a fallback chain across providers, share inference between devices, and eventually treat multiple machines as part of one AI infrastructure layer.

The conversation also moves beyond routing. Jonathan describes a future where agents spin up machines and subagents, pull inference from another authenticated device, and use lower-cost models for smaller jobs rather than burning the most capable model on everything. Open LLM is also exploring unified memory, one-click VPS deployment, multi-device sessions, and enterprise routing.

In Today's Episode We Discuss:

00:01 Jonathan Archer and Open LLM

03:34 The Origin of Open LLM

05:30 AI Subscription Economics

07:28 Subscription Routing and Compliance

09:01 Unified AI Memory

11:04 Multi-Device AI Infrastructure

13:19 Self-Hosted Models and VPS Deployment

14:43 Open LLM Pricing and Free Tier

16:19 Cross-Model Workflows

17:03 The Technical Challenge of AI Routing

19:10 The Multi-Model AI Future

20:24 Free vs Paid Plans

21:37 Fallback Chains and Model Selection

23:38 AI Subagents and Token Efficiency

24:35 Open Source vs Closed Infrastructure

26:12 Multi-Device Sessions and Enterprise AI

28:15 Breaking AI Platform Lock-In

29:53 OpenRouter and the Gateway Model

32:06 AI Gateway Acquisition Value

34:09 Open LLM and Vercel

36:18 Dynamic Model Discovery

37:38 Beyond the AI Gateway

38:29 Rapid-Fire Questions

39:00 Crypto Adoption and Real-World Utility

40:56 Crypto, Payments, and Stablecoins

One of the most concrete techniques is Open LLM’s configurable fallback chain, which moves to another model when usage runs out or a provider goes down. Jonathan also explains why subagents should default to less capable models for smaller tasks, comparing the alternative to sending Steve Jobs to retrieve a file. And he describes dynamic discovery that continuously queries providers for newly available models.

The bigger idea is older than AI: durable infrastructure often wins by preserving choice rather than forcing everything into one closed system.

Pull Quotes

“It’s their tokens, they should be able to go.”

“We’re gonna live in a multi-model future.”

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Autonomize AI now powers four of the five largest U.S. health enterprises, targeting the administrative work consuming clinicians’ time.

Ganesh Padmanabhan is the founder and CEO of Autonomize AI. Before founding the company, he spent 11 years at Dell, where he worked across engineering, product management, and general management and helped build what he describes as Dell’s youngest billion-dollar business. He later worked on enterprise AI before starting Autonomize roughly four and a half years ago.

Ganesh argues that healthcare’s core technology problem is not a shortage of innovation. It is fragmentation. Decades of point solutions have optimized individual workflows without improving the system as a whole. Autonomize is taking the opposite approach by building infrastructure for AI agents that can work across healthcare administration, including utilization management, care management, claims, payment integrity, and revenue cycle management.

His more provocative argument is that deploying agents is only the beginning. Enterprises already have systems for managing human workers, but almost nothing comparable for governing, evaluating, routing, and improving AI workers. Ganesh believes that infrastructure will eventually resemble the ERP, HR, recruiting, and learning systems companies built around human organizations.

In Today's Episode We Discuss:

00:00 Ganesh Padmanabhan and Autonomize AI

00:27 From mechanical engineering to software

01:59 Taking apart and rebuilding his grandfather’s compressor

03:41 Playing cricket at the national level in India

06:35 What Dell taught Ganesh about operational discipline

11:15 Why COVID pushed him toward healthcare

14:17 Why healthcare’s point solutions fail to compound

17:47 Losing a friend to breast cancer and the clinical trial problem

21:51 Biotech innovation, regulation, and faster clinical trials

24:34 What Autonomize’s AI agents actually do

30:32 Genie AI and letting clinicians build workflows

33:10 RLM Opt and reducing token usage

36:48 Why Autonomize uses a library of roughly 55 models

38:13 Why enterprises need infrastructure to manage AI agents

40:21 Why today’s AI adoption resembles early cloud migration

42:32 Growing from roughly 40 to 140-150 employees

43:22 Building company culture when AI can give you easy answers

44:59 Building an AI-native health enterprise

47:23 Moving healthcare from sick care toward prevention and longevity

47:46 Where to find Ganesh and Autonomize AI

One of Autonomize’s earliest projects came directly from Ganesh’s experience watching clinical-trial teams manually search medical records that could run thousands of pages. He also explains how a nurse’s 30-minute chart review might become a two-minute review, why Genie AI puts workflow creation directly into clinicians’ hands, and why Autonomize converts repeatedly proven facts into deterministic code instead of forcing an LLM to reason from scratch every time.

The broader lesson is an old one applied to a new technology: durable systems are built by redesigning the work, not merely adding faster tools to the old process.

Pull Quotes

“Everybody has it, everybody's using it, but we haven't really figured out what it takes to make it work.”

“Healthcare is more about care than health.”

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After 23 years at Google, employee #319 Alana Karen says tech careers are getting harder as AI reshapes jobs and corporate priorities.

Alana Karen spent 23 years at Google across search, ads, fiber, and other teams, joining as employee #319. She is the author of the best-selling Adventures of Women in Tech, now in its second edition, which grew out of years of writing about her own performance feedback and interviewing 80 women about their careers in technology.

The first edition carried a relatively straightforward message: if you wanted a career in tech, the opportunity was worth pursuing despite the problems. Five years later, Karen is less certain. Layoffs, post-COVID scarcity, AI investment, and changing incentives have created what she describes as a much harder environment where even engineers no longer have the security they once assumed.

Her most counterintuitive finding is that many of the problems driving people away from tech are less dramatic than the headlines suggest. The women she interviewed often talked about bad management, shrinking opportunity, constant manager changes, and whether their work still mattered. She also challenges the belief that the best technology must come from five to ten people working around the clock. That model can produce results, but burnout, failure, and organizational damage are real costs.

In Today's Episode We Discuss:

00:00 Alana Karen's 23 years at Google

00:28 Joining Google as employee #319

02:11 Growing up with scarcity and choosing a career path

07:01 Why staying at Google for 20 years mattered

08:45 When is enough actually enough?

10:26 Google, AI, and making veterans reprove themselves

11:55 How post-COVID scarcity changed Google's culture

14:23 Publishing real Google performance feedback

16:26 How that feedback became Adventures of Women in Tech

19:12 What 80 women actually said about careers in tech

23:19 Bad managers and the hidden retention problem

25:14 Why the second edition needed to be different

28:26 Engineers, AI investment, and the hard tech era

31:32 The myth of the five-person team working 24/7

36:28 Principles, Facebook, capitalism, and business models

44:06 What AI fluency now means for your career

48:15 How fast AI is improving

51:16 Why tech may no longer be the obvious career choice

53:34 Trust, ambition, feedback, and career decisions

57:50 What Alana hopes people remember

59:00 Where to find Alana Karen

Karen also explains how publishing years of Google performance reviews became the seed for her book, why she declined to jump to Facebook despite knowing people there, and how Gemini marked up a photo of her car to show where to place Honda's capsicum-infused rat-deterrent tape. She describes sitting through Google leadership presentations that framed small-team startup mode as AI best practice, while questioning whether burning people out should be treated as the only path to breakthrough products.

The larger question is whether careers should be built around loyalty to an industry or around the ability to keep adapting as industries change.

Pull Quotes

“They are willing to fire the engineers to buy the machines to hopefully replace them.”

“Don't marry tech and never look around.”

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