"Not Me" Podcast Episode #7: "Race to Compute"

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"Not Me" Podcast Episode #7: Race to Compute

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"Not Me" Podcast Episode #7: Race to Compute

Why Your Electricity Bill Is About to Become a Geopolitical Crisis

Vladyslav Podoliako

Nov 14, 2025

Everyone’s building AI agents. Nobody’s asking where the power comes from.

I spent the past week inside the Accel 2025 Globalscape report, one of those dense VC documents that usually stays behind closed doors. It’s 64 pages of market data, infrastructure forecasts, and capital flow maps, and I’m breaking it all down in this week’s episode.

We’re not in an AI bubble. We’re in the opening act of an industrial revolution that will require $4.1 trillion in data center spending between 2026 and 2030. Four trillion dollars to build out 117 additional gigawatts of compute capacity globally—enough to power Italy, Spain, and the UK combined.

Let me put that in perspective: the entire cloud infrastructure build from 2010 to 2020 cost a fraction of what we’re about to deploy in the next five years.

OpenAI committed to 30 gigawatts. Meta’s spending $600 billion through 2028. Microsoft signed a 10.5 GW renewables deal. These aren’t pilots—these are bets on a future where compute is the new oil.

But here’s the uncomfortable truth I dive into on the pod: we don’t have the electricity to power it.

The Power Problem Nobody’s Solving

The US has a 36 GW shortfall for data centers between 2025 and 2028. To close that gap, you’d need:

35 new nuclear reactors (a 37% increase over current US capacity), or
1,530 square kilometers of solar panels (larger than Los Angeles)

And you’d need to do it in three years.

The “Super Six” hyperscalers—Nvidia, Microsoft, Apple, Alphabet, Amazon, Meta—now control 50% of the NASDAQ’s market cap. They generated $600 billion in operating cash flow last year. They can finance this build. But they can’t conjure electrons out of thin air.

“We are at the beginning of a new industrial revolution… over the course of the next four or five years we’ll have $2T worth of data centers that will be powering software around the world.”
— Jensen Huang, CEO of Nvidia

I break down the entire energy economics in the episode, including which companies are securing power deals first and why this is really a race for baseload capacity, not better models.

The Model Economy

Text models have converged.

The performance gap between top LLMs (Google, Anthropic, OpenAI, Alibaba, xAI) is just 3%.

But video and computer-use models are still wide open:

Video generation models: 29% performance delta
Computer-use agents: 70% performance delta

Claude Sonnet 4 is dominating computer-use benchmarks. Everyone else? Nowhere close.

On the podcast, I walk through why this matters for where the real alpha is—it’s not in horizontal LLMs anymore. It’s in specialized models that can actually do things:

Legal research (Harvey)
Medical transcription (Abridge)
Permitting workflows (PermitFlow)
Agentic orchestration at enterprise scale

And here’s the kicker: inference costs dropped 97% in 31 months.

GPT-4 at launch: $75 per million tokens
GPT-5 Mini today: $2 per million tokens

I explain why this is both incredible for adoption and brutal for gross margins, which are still stuck at 7–40% for AI apps versus 76% for traditional SaaS.

The Capital Flow: Who’s Winning, Who’s Faking It

Total venture funding in cloud and AI hit $184 billion in 2025. But 60% of that—$110 billion—went to just three companies:

OpenAI: $47B
Anthropic: $19B
xAI: $15B

Model funding is heavily concentrated.

Meanwhile, application-layer funding is thriving. Companies like:

Lovable: $100M ARR in 8 months
Cursor: $500M ARR in 30 months (10x YoY growth)
n8n: 10x YoY revenue growth
ElevenLabs: $200M ARR, doubled in 10 months

These aren’t just fast—they’re operating at efficiency levels never seen in software.

I break down the full funding landscape in the episode, including:

✓ Why EU/IL raised 66% of what the US raised in application funding
✓ The “vibe coding” revolution and why Cursor does $6.1M ARR per employee (vs $0.54M at Salesforce)
✓ Which vertical categories pulled the most capital (spoiler: legal, healthcare, and developer tools)

The Enterprise Adoption Curve: Agents Are Coming, Slowly

45% of companies plan to increase AI budgets by 10–25% due to agentic AI. Another 18% are going 26–50%+.

Current state of deployment:

Salesforce Agentforce: ~$440M ARR, 13K customers
Microsoft Copilot Studio: 230K B2B users, 1M+ agents created
Atlassian AI tools: 3.5M MAUs, 5x QoQ token usage growth

Those numbers sound big until you realize Salesforce has millions of enterprise seats. Agentic AI is not ubiquitous. It’s still a bet.

The issue? LLMs are probabilistic. Enterprises need deterministic.

On the pod, I walk through:

Why companies like UiPath, n8n, and Celonis are building orchestration layers
Real enterprise case studies:
- Fiserv saving 12K hours with 98% automation
- Vodafone automating 33 security workflows, saving 5K person-days
- Duolingo achieving 80% ticket deflection with Decagon
What needs to happen before we hit the inflection point

The good news?

When agents do scale, they’re competing for services budgets, not just software budgets.

That’s a 10x larger TAM, and I explain why this is the sleeper trend of the next five years.

The Vertical Explosion: Where the Real Money Is Moving

The most overlooked insight from the Globalscape report is the vertical AI breakdown:

Healthcare & Life Sciences: $3.4B (Abridge, OpenEvidence, Cradle)
Legal: $3.0B (Harvey, Filevine, PermitFlow)
Developer Tools: $3.9B (Cursor, Lovable, Cognition)
Finance: $3.4B (Rogo, Basis, Tempo)

These aren’t horizontal plays. They’re category killers replacing human-delivered services.

PermitFlow isn’t just software—it’s a replacement for permit consultants
Harvey isn’t just legal search—it’s a junior associate in a box
Abridge isn’t just transcription—it’s a medical scribe replacement

Industries with massive documentation overheads are getting disrupted first.

I dive deep on this “services margin capture” thesis in the episode and why legal, finance, healthcare, and construction are ground zero for the next wave of disruption.

The Security Layer: AI’s New Attack Surface

39% of CISOs say securing AI agents is their top pain point.

The old perimeter security model doesn’t work when your “application” is a probabilistic agent that can:

Call APIs on the fly
Access data lakes
Modify workflows autonomously
Exfiltrate training data

New Attack Vectors:

✗ Prompt injection (getting agents to leak data)
✗ Model poisoning (corrupting training data)
✗ Unauthorized tool use (agents calling APIs they shouldn’t)
✗ Data exfiltration (models trained on proprietary info)

Companies building the AI security stack:

Cyera • Prophet • NOMA • Legion
Tines • Vega • Attestable • OASIS

Oh no quality shrinking, idk why, still figure that out

But most enterprises don’t even have observability into what their AI agents are doing, let alone guardrails.

On the pod, I explain:

Why AI security isn’t optional anymore
Which categories are about to become table stakes
The convergence of data governance, identity management, and AI permissioning

The Uncomfortable Math

Here’s what nobody wants to say out loud: this only works if global GDP grows faster than expected.

To justify $4.1 trillion in AI CapEx, you need data center revenue to hit $3.1 trillion by 2030 (at 20% margins).

That requires:

6.5% global GDP CAGR (2025–2030)
vs IMF’s 5.0% baseline forecast
= 1.5% delta entirely driven by AI productivity gains

Is that even realistic?

Maybe. AI coding assistants are already used by 90% of developers (up from 36% in 2023). Agentic workflows are automating legal research, customer support, financial analysis. The productivity gains are real.

But if we don’t hit that GDP growth? Then $4.1 trillion in CapEx becomes the mother of all sunk costs. And the companies left holding stranded data center capacity will be the bag holders of the decade.

I walk through the entire ROI model in the episode, including:

Why depreciation schedules matter more than you think
What happens if we don’t hit that GDP growth target
Which companies are left holding stranded assets if this bet fails

Five Bets for 2026

The Globalscape report ends with five predictions, and I think they’re directionally right:

Enterprise agentic deployment will scale 10x as orchestration and observability tools mature
AI-native vertical apps will replace human services in legal, finance, and healthcare at scale
AI security becomes mandatory as enterprises demand unified data, identity, and permissioning controls
Vibe coding moves to the enterprise, forcing CIOs to rethink CI/CD and deployment pipelines
Voice and media become the default UX, with synthetic avatars and video agents replacing text interfaces

I’d add a sixth: the power crunch will force consolidation. Not every AI startup will survive the energy bottleneck. The ones that do will have locked in compute capacity early.

I unpack all six predictions in detail on the podcast, including which categories are already showing early signals and where the capital will concentrate next.

Why You Need to Listen

If you’re building in AI, operating a company, or just trying to understand where this is going, this episode is your roadmap.

I’m walking through:

✓ The full $4.1T infrastructure build and who’s financing it
✓ Why the 36 GW power shortfall is the real bottleneck
✓ Which vertical categories are pulling the most capital (and why)
✓ The enterprise adoption timeline and what unlocks mass deployment
✓ The gross margin problem and when it gets solved
✓ Five bets for 2026 that will define the next decade

We’re not in the hype phase anymore. We’re in the infrastructure phase. The companies that survive this build-out will define the next decade of software.