Economist Podcasts — Intelligence
Episode Title: Power ranges: AI faces supply crunch
Date: April 29, 2026
Hosts: Rosie Blore, Jason Palmer
Guests: Shailesh Chitnis (Global Business Writer), Sue Lin Wong (Host, Scam Inc.), Shira Aviona (Business Writer)
Episode Overview
This episode explores the rapid expansion of artificial intelligence (AI) in Silicon Valley and the resulting supply crunch affecting technological infrastructure—especially data center capacity and cutting-edge chips. The episode also examines the latest evolution in sophisticated online scams powered by AI, and ends with a look at the striking pivot of the iconic shoe company Allbirds, which is abandoning footwear in favor of AI infrastructure.
Main Segment 1: AI's Looming Supply Crunch
Theme:
The surging demand for AI is creating severe bottlenecks in processing capacity, hardware, and infrastructure, threatening the pace of AI innovation and its costs.
Key Discussion Points and Insights
-
AI's Exponential Growth Outpaces Infrastructure (01:20–03:21)
- Silicon Valley companies have entered a phase described as "token maxing," processing exponentially more data tokens.
- OpenRouter reported weekly tokens processed quadrupled in three months alone.
- “The AI companies are actually struggling to keep up with demand. And in recent times, they have actually been throttling access to some of their tools.”
—Shailesh Chitnis (02:35)
- Examples:
- Anthropic imposed restrictions during peak usage.
- OpenAI shut down its Sora tool to reallocate scarce compute resources.
-
Where the Shortage Lies (03:21–04:13)
- GPU (graphics processing unit) shortages are acute—these processors are essential to AI model operations.
- Data centers are running out of both processing power and physical components (e.g., transformers, switches).
- “Essentially the tech world is running out of processing power to be able to enable the kind of demand that we are seeing.”
—Shailesh Chitnis (03:26)
-
Tech Industry’s Response: Massive Investment (04:16–04:53)
- US cloud giants (Amazon, Meta, Microsoft) expected to spend close to $700 billion on AI data centers in 2026.
- AI model makers (Anthropic, OpenAI) are also investing and negotiating exclusive access to resources.
-
Bottlenecks: Slow Construction, Scarce Parts (04:58–06:10)
- Building new data centers is slowed by local opposition over land, electricity, and water usage.
- Severe shortages in old-school components (“transformers and switches”) stretch lead times to 3–5 years.
- GPU manufacturing can’t keep pace with demand; companies forced to use “old” chips (2–3 years old) previously considered obsolete.
-
Critical Choke Points in Hardware (06:17–07:54)
- Nvidia dominates with over two-thirds of the world’s AI processing power; its chips are “essentially sold out.”
- Only one major manufacturer, TSMC (Taiwan Semiconductor Manufacturing Company), produces the majority of cutting-edge AI chips.
- “TSMC has been expanding capacity...capex increasing by $60 billion this year. But again, it’s still not as much as companies would like.”
—Shailesh Chitnis (07:54)
-
Industry Frustration and the Scale of the Problem (08:01–08:48)
- Sam Altman (OpenAI): “TSMC should just build more capacity.”
- Elon Musk aims to build his own “Terrafab” by 2030, needing a staggering $5–13 trillion in capital.
- These ambitions show the magnitude of the compute crunch.
-
Consequences for AI Progress and Pricing (09:06–09:52)
- If the crunch persists, firms may need to raise prices—upending the trend of declining AI costs.
- Slower or more expensive access could restrict new adoption and slow down AI development.
- “There have been those that have called the supply crunch a ‘natural break’ on this reckless AI spending.”
—Shailesh Chitnis (09:06)
Notable Quotes & Memorable Moments
- “Literally the tech industry is running out of [transformers and switches] and lead times for some of them stretching between three to five years.”—Shailesh Chitnis (05:26)
- “Elon Musk...called it Terrafab. His ambition is by 2030 to build a fab that will have more capacity than all the current fabrication plants put together...that would require anywhere between $5 to $13 trillion.”—Shailesh Chitnis (08:29)
- “The longer the supply crunch continues, the more pressure it will put on the firms to raise prices...that could then slow down adoption as well.”—Shailesh Chitnis (09:06)
Main Segment 2: The Evolution of AI-Powered Scams
Theme:
Online scam operations, supercharged by AI and malware-as-a-service, are becoming faster, more sophisticated, and global in reach.
Key Discussion Points and Insights
-
A New Breed of Scams (11:16–14:19)
- Case study: "Amber," an Indonesian accountant, lost over $26,000 after downloading a malware-infected app imitating a government tax app.
- The malware harvested extensive biometric and personal data, draining multiple accounts almost instantaneously.
- “She couldn't see it happening on the phone. It just looked like the tax app was running.”
—Sue Lin Wong (13:16)
-
From Slow-Burn Frauds to Instant Heists (14:19–15:25)
- Traditional scams (romance, investment) required grooming over months; now, “what happened to Amber happened in minutes, if not seconds.”
- Once inside, criminals can rapidly target everyone in a victim’s network.
-
AI and Malware-as-a-Service (15:34–17:12)
- Scammers buy ready-made malware and stolen personal data from sources like Telegram.
- AI tools enable multilingual, easily updatable, and highly convincing frauds, evading detection.
- “These gangs are running vast, vast businesses...perhaps brings in US$500 billion, which is the equivalent of the global illicit drug trade.”
—Sue Lin Wong (16:23)
- Infoblox, a security firm, connected these operations to malware clusters targeting people in over 20 countries.
-
Rapid, Ongoing Evolution (17:12–18:03)
- AI chatbot integration, deepfake voice tools, and anti-facial recognition techniques are emerging in the criminal toolset.
- Though developing countries are the current main targets, US and European consumers are expected to be next.
-
Law Enforcement Response and Challenges (18:14–19:11)
- Governments are increasing enforcement, but criminals move operations in response to crackdowns, exploiting endemic corruption.
- “It’s not really as if one country can clamp down on this and the whole problem will go away.”—Sue Lin Wong (18:46)
Main Segment 3: Allbirds’ Radical Pivot to AI Infrastructure
Theme:
The end of an era for “millennial brands”—the struggles of direct-to-consumer startups, highlighted by Allbirds’ abrupt shift from sustainable shoes to AI compute provision.
Key Discussion Points and Insights
-
Allbirds’ Story and Sudden Pivot (19:50–21:43)
- Once a darling of Silicon Valley, Allbirds’ wool footwear loses its appeal.
- “So it's selling off all of its footwear assets. No more sneakers. And it's decided to rename itself New Bird AI and to pivot to compute infrastructure for artificial intelligence.”—Shira Aviona (20:43)
- This marks a dramatic break for a company built on sustainability and minimalism.
-
The Rise and Fall of Millennial Brands (21:01–22:59)
- Companies founded in the 2010s marketed chic, minimal styles online, thriving on cheap ads and VC funding.
- Changing macroeconomics: higher interest rates, more competition in digital advertising, and VC dollars drying up.
- “If you only sell through your own website, the best way to get customers is by serving them ads on social media...that’s become significantly more expensive.”
—Shira Aviona (22:25).
-
Fates of Similar Startups (23:02–23:32)
- Some, like Dollar Shave Club, were sold to conglomerates, but most face tough transitions or outright pivots.
-
Is This Really a “Pivot”? (23:40–24:13)
- The move keeps the public stock ticker (“BIRD”) while selling everything else; a technical, if not existential, continuity.
- “There is when you would like to retain your stock ticker.”—Shira Aviona (23:40)
- Legally permissible, but more a symptom of business failure than of adaptation.
Timestamps for Key Segments
- Introduction & AI Supply Crunch Framing — 01:11–02:35
- AI Data Crunch and Infrastructure Shortages — 02:35–09:52
- Rise in AI-Powered Scams — 11:16–19:11
- Allbirds’ Pivot from Footwear to AI — 19:50–24:13
Standout Quotes
- “Elon Musk’s ambition is by 2030 to build a fab that will have more capacity than all the current fabrication plants put together...that would require anywhere between 5 to 13 trillion [dollars].”—Shailesh Chitnis (08:29)
- “We've seen the US Government come out and say they were going to try and crack down on this industry. The problem is that the criminals are making so much money...they're incentivized to find workarounds.”—Sue Lin Wong (18:14)
- “There is [such a pivot] when you would like to retain your stock ticker.”—Shira Aviona (23:40)
Summary
This episode provides a sobering look at how relentless AI expansion is running into the intractable realities of physical infrastructure and finite supply chains. The desperation for GPUs and data centers is reshaping both Big Tech and manufacturing strategies, while fueling a wave of technological innovation—and frustration. Meanwhile, as AI amplifies criminal capabilities, new malware-based scams target users globally with terrifying speed and sophistication. Lastly, the shift of Allbirds from ecological footwear to AI compute provision captures how once-trendy consumer brands face existential pivots in an unforgiving new economic climate.