This Week in Startups – Episode E2197 Detailed Summary

Podcast: This Week in Startups
Date: October 22, 2025
Host: Jason Calacanis
Guests: Elad Gil (entrepreneur/investor) & Alex Wilhelm (co-host)
Theme: Amazon’s “Age of Efficiency,” LLM Distribution, AI Wearable Worries, and More

Episode Overview

In this episode, Jason Calacanis, Alex Wilhelm, and guest Elad Gil dive deep into the current phase of efficiency in technology and startups, driven by AI and automation. They discuss the implications for enterprises and workers, examine developments in LLM (Large Language Model) infrastructure and distribution, address concerns about AI wearables, and debate regulatory approaches to emerging tech. The discussion is full of real-world examples, practical insight into startup strategy, and forward-looking speculation on how AI is transforming business and society.

Key Discussion Points and Insights

1. The "Age of Efficiency" and AI's Role in Startups (00:07–02:41, 15:34–19:27)

• AI Driving Margins: Companies are leveraging AI-powered solutions (e.g., chatbots) to automate processes, driving up gross margins significantly.
  • Quote (16:11, Alex): "Our ability to control customer support costs over time, even as volume has gone up, has contributed to an increase in gross margin from 60% in fiscal 24 to 68 in fiscal 25... essentially they're automating things."
• Startups as Early Adopters: Resource-constrained startups are the first movers in adopting AI for efficiency.
  • Quote (16:38, Jason): "The age of efficiency is upon us. Every unit in every company, particularly in startups... they are the ones to first use these tools."
• Accelerating Revenue Growth: Several AI-driven companies are scaling revenue rapidly, reaching hundreds of millions within a few years.
  • Quote (17:20, Elad): "Zero to a few hundred million dollars in revenue in two, three years is something I haven't seen in a very long time."
• Risks of Mispricing: Some companies may be underpricing their AI products, trading profits for market share, but most demonstrate solid margins.
  • Quote (18:45, Elad): "Some of them are burning a lot of money along the way... the pricing versus the value differential are leading to odd economics."

2. AI-Invested Roll-Ups and Change Management (20:39–22:40)

• AI-Driven Rollups: Elad describes a thesis of investing in roll-ups—acquiring service businesses and rapidly improving margins with AI.
  • Quote (21:15, Elad): "The one that is non stealthy is called Long Lake... you fund the purchase of services, businesses roll them up and then increase margin dramatically using AI."
• Operational Challenges: Adopting AI is often more about change management and team incentives than technology itself.

3. Enterprise AI Adoption and Book Translation Project (04:10–07:20)

• Enterprise Consulting: Elad is involved in helping enterprises identify and implement AI adoption strategies.
• Alexandria Project: An ambitious project using AI to translate a thousand of the most important public domain books into every language, with superior results to human translation.
  • Quote (04:54, Elad): "We're calling it Alexandria... translating them... working with OpenAI, Anthropic and 11 Labs... people prefer the machine translated works by far."
• Scalability: The infrastructure exists to let anyone translate any book, building a modern "Library of Alexandria."

4. Distributed Computing for LLMs and Data/Inference Location (07:20–14:01)

• Decentralization vs. Centralization: While concepts like Bittensor pursue decentralized AI compute, the economics tend to drive centralization, especially where capital and energy costs matter.
  • Quote (08:49, Elad): "Anything that has strong economic viability, compute tends to collapse into centralization."
• Energy as Critical Factor: Data center locations for AI training are shifting toward regions with cheap energy (e.g., the Gulf states, US), while Europe is at risk of being left behind due to high costs.
  • Quote (12:11, Elad): "Given how poorly Europe has governed itself relative to energy... all the training centers should end up in the Gulf and in the US..."

5. AI and Job Displacement: The Amazon Automation Story (23:36–31:40)

• Amazon’s Automation Push: Internal docs reveal Amazon expects to hire 160,000 fewer workers by 2027 due to automation, aiming for 75% warehouse automation by 2033.
  • Quote (23:56, Alex): "All the work... to build out robots... will save them about 600,000 hires through 2033."
• Societal Implications: Potential elimination of all human involvement in Amazon's fulfillment, with resulting mass job loss for warehouse and delivery workers.
  • Quote (27:49, Jason): "Before 2030 you're going to see Amazon... replace all factory workers and all drivers."
• Retraining and Safety Nets: The need for social solutions and retraining as automation upends employment, especially in areas like customer support.
  • Quote (30:50, Elad): "Any technology wave definitely has job displacement... what do you do from a retraining perspective? How do you think about social safety nets?"

6. Wearables, AI Assistants, and Privacy (38:34–56:34)

• Wearable AI Devices: Companies like Sesame (founded by Oculus and AR veterans) are developing voice-first AI wearables, reflecting a resurgence of the Google Glass vision.
  • Quote (52:06, Alex): "Sesame wants to give you some hardware that lets you chat with your personal AI agent... on the go."
• Conceptual Fears and Parallels: The group discusses science fiction scenarios—externalizing memory, always-on personal AIs, and privacy.
  • Quote (54:26, Elad): "If so much of your memory and all these agents and all these things you do is actually loaded in your device and you lose your device... how much of your memories or your abilities is it absorbing as you sort of augment as a human?"
• Public's Privacy Tradeoff: History shows many users accept privacy-sacrificing tech if the utility is high (Gmail, Facebook, etc.), raising the prospect that mass surveillance AI wearables could be widely adopted.

7. LLM Infrastructure, Regulation, and Industry Structure (41:14–45:09, 68:49–78:49)

• Big Tech and LLM Partnerships: Apple compared to Google—should they build their own LLMs or continue distributing those from others? Risks are not the same as with search, given the centrality of AI agents.
• Acquisition Chilling Effects: Regulatory scrutiny (FTC, EU, UK) is muting large acquisitions of promising AI startups, impacting industry consolidation.
  • Quote (44:32, Elad): "The FTC has continued to be reasonably anti large tech acquisitions... it's not just the US government, it's also the EU, it's the UK."
• State vs. Federal AI Regulation: Debated whether California (or any state) should effectively set AI policy for the country/world, with Elad skeptical of state-level patchwork regulation.
  • Quote (72:15, Elad): "Early on in industries, it's a bit of a disaster to let each incremental state chime in... there's a handful of states that effectively by fiat govern what the rest of the country does..."

8. Prediction Markets, Stablecoins, and Financial Innovation (46:00–68:41)

• Prediction Markets on the Rise: Platforms like Polymarket and Kalshi are democratizing speculation on everything from company earnings to politics, simplifying complex options trading into binary bets.
  • Quote (49:45, Elad): "The real innovation here is you've collapsed these really complex speculative things... and you've turned them into a Yes, no."
• The Growing Power of Stablecoins: Tether and Circle now among the world’s largest holders of US Treasuries, hinting at a deeper integration between crypto and traditional finance.
  • Quote (63:10, Elad): "One of the biggest buyers of US government debt is the crypto industry... if crypto gets 10 times bigger... what does that mean in terms of the US government's relationship to crypto?"

9. The Challenge of Being Early and Reinvention (59:11–61:26)

• Innovator’s Dilemma: Many ideas from Google (robotics, satellite internet, fiber), micropayments, AI wearables—failed at first, but the market may now be ready. Founders should not be afraid to revisit old ideas whose time may have come.

Notable Quotes and Memorable Moments

• "The age of efficiency is upon us." – Jason Calacanis (16:38)
• "Zero to a few hundred million dollars in revenue in two, three years is something I haven't seen in a very long time." – Elad Gil (17:20)
• "If you want to charge me 20 bucks for something that saves me $2,000 a month. Okay. And it's just wild to see how mispriced some of these products are for the value they're creating." – Jason Calacanis (17:57)
• "If you look at search as an analog... Google ended up paying Apple tens of billions of dollars a year to use its search instead of developing its own... It seems like that's the path that's been taken so far on LLMs." – Elad Gil (41:14)
• "Before 2030 you're going to see Amazon... replace all factory workers and all drivers... every Amazon worker. All those jobs, ups, gone, FedEx, gone." – Jason Calacanis (27:49)
• "When a CEO that sophisticated doesn't write that memo without a reason. He was telegraphing... hey, you know, this is going to result in less jobs." – Jason Calacanis (26:44)
• "Every time they can automate a task across 10,000 assistants... it's really an AI company." – Jason Calacanis (22:40)
• "It may have been the right thing to do. The question is, should California have been able to do it?... I think those are two separate things." – Elad Gil (73:43)
• "One of the most under hyped players in crypto right now perhaps is Stripe who I think have been doing amazing things in terms of stablecoins and Bridges and all the rest of it." – Elad Gil (64:55)

Timestamps of Key Segments

• 00:07–02:41 – The "age of efficiency," AI-driven organizational change, and startup/enterprise adoption of AI.
• 04:10–07:20 – Elad’s main projects: Enterprise AI, Alexandria book translation initiative.
• 07:20–14:01 – Distributed AI compute, centralization, and energy/geopolitics.
• 15:34–19:27 – AI margin expansion, underpricing, and startup profitability.
• 20:39–22:40 – AI-driven rollups, change management, and operational challenges.
• 23:36–31:40 – Amazon automation, job displacement, retraining, and policy implications.
• 38:34–41:14 – Future of AI interfaces: wearables, devices, and privacy.
• 41:14–45:09 – LLM distribution, Apple/Google comparison, and platform bets.
• 46:00–49:45 – The rise of prediction markets for finance, sports, and politics.
• 59:11–61:26 – Startups being too early, the value of revisiting failed concepts.
• 63:08–68:41 – Stablecoins' macro impact, crypto as a US Treasury buyer.
• 68:49–78:49 – Anthropic/Sacks spat, regulatory capture, and the state vs. federal AI regulation debate.

Tone and Style

• Direct, opinionated, and often humorous. The hosts challenge each other and share anecdotes from their experience, with an openness to disagreement and speculation.
• Insight-rich, practical, and forward-looking. The conversation is peppered with real company stories, investment theses, and “what if” scenarios.
• Occasional self-referencing and playful banter, especially in the regulatory and prediction market discussion.

For Listeners Who Didn’t Catch the Episode

This episode is a must for anyone interested in the intersection of AI, automation, the future of work, and financial/technical infrastructure. It doesn’t shy away from tricky topics—like job displacement or regulatory capture—nor does it settle for easy answers. If you want first-person investor perspective on the state of AI startups, the practical impact of automation, and what the next decade holds for big tech, regulation, and society, don’t miss this lively and substantial episode.