https://www.youtube.com/watch?v=TCeXwFWmv1U&feature=youtu.be verify facts in this video and are there any opposing views podcasts?

The search didn't identify the specific video. Let me try a more targeted search to identify what this video is about.Great, I now have enough to give you a thorough breakdown. The video is a recent interview with Ed Zitron — tech critic, PR CEO, newsletter author (Where's Your Ed At), and host of the Better Offline podcast — titled "You are being lied to about the AI data center boom." Here's a fact-check of his key claims and the best opposing voices:

🔍 Ed Zitron's Core Claims — Fact Check

Claim 1: Most announced data centers haven't been built, and many are far behind schedule

✅ Largely supported. As of April 2026, almost half of U.S. data centers planned for this year are expected to be delayed or canceled, with shortages of transformers, switchgear, and batteries among the primary causes. A separate analysis found that almost 40% of projects due this year are at risk of falling behind schedule by more than three months, including projects tied to Microsoft and OpenAI. Only a third of the 12 gigawatts of data center capacity planned for 2026 is currently under construction, according to Sightline Climate. This corroborates Zitron's skepticism about paper announcements vs. operational reality.

Claim 2: Nvidia is warehousing huge numbers of Blackwell GPUs — possibly a million or more

⚠️ Plausible but unconfirmed. Zitron believes that the number of data centers he could confirm as operational indicates that as much as 75% — roughly two million — of units actually sold are awaiting deployment and "gathering dust," unable to generate revenue. Crucially, Bloomberg reported that the vast majority of a Chinese company's (Megaspeed's) $2.4 billion worth of Bianca boards — the circuit boards housing Nvidia's top-end GPUs — were unaccounted for at the sites Nvidia described to Washington. After Bloomberg asked about those products, Nvidia confirmed the boards are in separate warehouses, but declined to specify the number in storage or when they would be deployed. Nvidia's own defense was that "building data centers is a complex process that takes many months and involves many suppliers, contractors and approvals."

Claim 3: Nvidia double-counted GPUs (counting dual-core chips as two units)

✅ Confirmed. Nvidia courted controversy by claiming it had shipped 6 million GPUs after Blackwell came out by counting each of the two cores separately, meaning the actual unit count was closer to 3 million.

Claim 4: AI revenue can't justify the massive CapEx — "no fundamentals to fall back on"

⚠️ Contested but the gap is real. Zitron calculates that to support the estimated $800 billion in GPU sales Nvidia claims will come through by end of 2027, there needs to be 39.6GW of new data centers constructed, and around $383 billion in annual AI compute demand — for an industry that, even with OpenAI and Anthropic's spend, doesn't even reach $70 billion in annual demand. Meanwhile, the five largest US cloud and AI infrastructure providers have collectively committed to spending between $660 billion and $690 billion on capital expenditure in 2026, nearly doubling 2025 levels. Amazon's CEO Andy Jassy, however, defended the plan by noting that AI capacity is being monetized as quickly as it is installed and that AWS reached a $142 billion annualized revenue run rate with growth accelerating to 24% year-over-year, a three-year high.

Claim 5: Hyperscaler "AI revenues" are circular — Big Tech funding AI startups that spend money back on Big Tech cloud

⚠️ Directionally accurate, widely acknowledged. Nvidia's GPUs are valuable because they're scarce — and by trading them directly into an ever-inflating data center scheme, Nvidia is making sure they stay that way, as TechCrunch put it. The circular nature of the deals (Nvidia investing in OpenAI with GPUs, OpenAI spending those back on cloud, etc.) is widely noted — though bulls argue it reflects genuine demand, not fraud.

Claim 6: The bubble could pop in 2026, becoming a 2026 midterms issue

🔮 Opinion / prediction. Zitron predicted the data center bubble could begin to pop this year, with collapses spreading over months or years, arriving in time to become a defining issue in the 2026 midterms, saying "if they think people hated bankers, people hate data centers." This is a forecast, not a verifiable fact — though the political resistance to data centers is documented.

🎙️ Opposing Views — Bullish Podcasts & Perspectives

Here are podcasts and sources that push back with a pro-AI infrastructure view:

1. Thoughts on the Market (Morgan Stanley) Morgan Stanley's Stephen Byrd argues the bullishness on AI will get more bullish over coming months, that model capabilities are advancing faster than corporate adoption can keep up, and that the revenue inflection will likely materialize in 2026. Available on Apple Podcasts.

2. The AI Investor Podcast (24/7 Wall St.) Hosts Eric Bleeker and Austin Smith discuss how AI technology is flowing through the global economy and identify investment opportunities, covering what to expect from Nvidia and breaking down the battle between OpenAI and Google. Bullish, investment-focused take.

3. KKR's "Beyond the Bubble" perspective KKR argues that past tech infrastructure hype cycles — fiber, electrification — suggest the data centers being built are unlikely to go to waste. Instead, these hard assets will form the backbone of a new economy. Their view: bubbles always hurt some investors, but the capacity they create endures.

4. Goldman Sachs AI CapEx Analysis Goldman Sachs projects $765 billion in annual AI CapEx in 2026, growing to $1.6 trillion in annual CapEx in 2031, with a cumulative $7.6 trillion between 2026 and 2031 — framing this as a structural shift, not a bubble.

⚖️ Bottom Line

Zitron's most verifiable claims (GPU double-counting, data center delays, construction vs. announcement gap) are backed by credible reporting. His broader bubble thesis — that there are no fundamentals — is genuinely contested. The bull case rests on the bet that AI demand will grow into the infrastructure being built, much like the internet grew into 1990s fiber. The bear case is that this time, unlike the internet, real revenues still lag the spending by an enormous and widening margin. Both sides are worth tracking.