Investigate specifically why Zitron's thesis could be wrong or overstated as of October 2026.

Zitron’s core claim is a cash-flow mismatch, not just hype. I’m checking the latest revenue, GPU, and balance-sheet numbers that would have to be wrong for that claim to hold.
The Nvidia figures conflict across sources, so I’m checking primary filings and the Microsoft, Google, and Amazon AI revenue lines that actually pay for the chips.
Zitron’s strongest point is an accounting gap, not a demand mirage. The version that says the money is about to run out, and that hyperscalers cannot survive the mismatch, is the part October 2026 has already started to break.

What the thesis actually is

As of mid-October 2026, Ed Zitron’s case is not “nobody uses chatbots.” It is that the industry is spending on a scale that end-customer economics cannot support, and that the numbers used to deny that are circular and unaudited. In a September 23 Investor’s Business Daily interview he put the mechanism bluntly: Big Tech has already spent over $1 trillion of capex, plans roughly another trillion next year, and most of the visible AI revenue still comes from OpenAI and Anthropic, companies the same hyperscalers also have to fund. He has also argued that annualized run rates are “made-up numbers,” that a large share of lab revenue is startups burning venture dollars on tokens, and that private-credit financing of data centers is a “brewing crisis.” The strong form of the call is a cascade: lab cash runs out, neoclouds default, hyperscalers take catastrophic earnings misses, and NVIDIA revenue could fall by as much as 90 percent. [1] [2] [3]

That diagnosis of a gap is partly right. The prediction that the system must therefore seize up on a near-term clock is where the case is overstated.

Revenue is real, growing, and still not the revenue he is scoring against

The cleanest counter to “tens of billions against trillions” is that the revenue base has moved. OpenAI told investors its own annualized revenue was approaching $50 billion at the end of September 2026, up from about $20 billion at the end of 2025, with 77 percent run-rate growth in the third quarter and 107 percent growth in enterprise. The widely cited ~$70 billion figure was investors grossing OpenAI up onto Anthropic’s accounting, not OpenAI’s number. Recognized full-year 2026 revenue is still expected around $35 billion, so the run rate overstates cash already earned. [4] [5] [6]

Anthropic’s leaked draft prospectus is harsher and more useful. Revenue grew twelvefold to nearly $4.6 billion in 2025. Operating loss was $8.06 billion. The headline net loss of about $42 billion included roughly $34 billion of non-cash accounting tied to financing instruments, not cash burned running the business. Compute and infrastructure cost $7.33 billion. By the end of July 2026 its gross annualized revenue was reported at $65 billion, and preliminary second-quarter revenue was reported around $11.5–11.6 billion. Some accounts say the company reached adjusted operating profit in the second quarter; those reports disagree, and the filing is not a public audited S-1. [7] [8] [9] Nathan Benaich’s State of AI Report 2026 put the two labs’ combined reported run rates at about $105 billion by late summer, up from about $30 billion at the start of the year. [10]

The more important revenue is not the labs. It is the clouds that sell the compute:

• Microsoft’s fiscal 2026 revenue was $331.8 billion, up 18 percent, with operating income of $155.2 billion, up 21 percent. Azure topped $100 billion for the first time, up 41 percent, and grew 43 percent in the June quarter. Microsoft 365 Copilot passed 30 million paid seats. Commercial remaining performance obligations hit $678 billion, up 84 percent. OpenAI contributed $24.1 billion, about 7 percent of Microsoft and roughly a quarter of Azure, so concentration is real. Excluding OpenAI, the backlog still grew 25 percent. [11] [12] [13]
• Google Cloud grew 82 percent year over year to $24.8 billion in the June quarter, with operating income of $8.8 billion and a 35.6 percent operating margin, more than triple the year-earlier profit. Backlog was $514 billion. [14] [15]
• AWS grew 37 percent to $42.2 billion in that quarter, its fastest growth in 18 quarters, and management cited a $169 billion annualized run rate. [14] [16]

Zitron is right that ARR is a bad yardstick and that partner-gross versus partner-net makes leaderboards incomparable. He is wrong if “revenue” is treated as fake because it is not yet equal to capex. Cloud AI revenue is showing up in audited public filings, with margins, and with contracted backlog. Goldman Sachs’ September 25, 2026 note is the fair version of his gap: the top five hyperscalers need roughly $300 billion of annual AI revenue to break even on about $800 billion of 2026 AI infrastructure spend, while AI cloud revenue is only about $70 billion above the pre-AI trend, a roughly $230 billion annual shortfall. That is a returns problem. It is not evidence that the revenue line is invented. [17]

Enterprise profit capture is the soft underbelly, and here Zitron’s skepticism has better footing than the lab run rates. McKinsey’s August 2026 survey of 1,719 respondents found that 80 percent report personal productivity gains, but only 37 percent attribute any EBIT impact to AI, unchanged from the prior year. High performers, those attributing at least 5 percent of EBIT to AI, were still about 6 percent. Usage is scaling. Corporate P&Ls are not, yet. [18]

GPU demand is contracted, not hoped for

NVIDIA’s fiscal second quarter, ended July 26, 2026, is the hardest fact against “the chips are a story.” Revenue was $96.2 billion, up 106 percent year over year and 18 percent sequentially. Data Center was $89.0 billion, up 117 percent. GAAP gross margin was 75.0 percent. Guidance for the October quarter was $108 billion, plus or minus 2 percent, with no China data-center compute assumed. Supply and capacity commitments rose to $279 billion from $119 billion the prior quarter. [19] [20]

Customers are paying before delivery. In the first half of fiscal 2027 NVIDIA recorded $15.6 billion of customer advances, more than double the year-earlier period, and recognized $13.0 billion of that as revenue. That is the opposite of a warehouse of unsold GPUs. [21]

The bearish reading of the same filing is also real, and Zitron is closer to right on this slice. Accounts receivable rose to $63.1 billion. Five direct customers were 70 percent of that balance. NVIDIA has extended investment-grade buyers terms of 90 days to a year, and in August 2026 it agreed to guarantee up to $105 billion of lease and power obligations for an OpenAI-affiliated campus in Ohio. Demand is not imaginary. It is concentrated, partly vendor-financed, and partly circular. A 90 percent revenue collapse would require those contracted buyers to walk away from prepaid capacity and from their own cloud backlogs at the same time. Nothing in the July quarter shows that. [21]

Zitron’s claim of $200–350 billion of GPUs sitting unpowered is an estimate, not a filing. Power and shell delays can strand capacity. They have not yet shown up as unsold NVIDIA product.

Debt does not hit the companies he needs it to hit

The balance-sheet split is the main reason “the money will run out” does not travel from CoreWeave to Microsoft.

Microsoft, Amazon, Alphabet, and Meta still sit in a different credit world from the neoclouds. An August 2026 Advisor Perspectives review of the credit data put their debt-to-equity ratios between 0.18x and 0.51x, with ratings in the AA to AAA range and spreads that had not blown out. Oracle was the outlier: five-year CDS from under 50 basis points to about 200, debt-to-equity near 4x, bonds trading like junk despite an investment-grade rating. [22]

Microsoft’s own books show how the insulation works. Long-term debt fell to $31.1 billion at June 30, 2026, down 22.6 percent. Cash and short-term investments were still about $77 billion. Operating income was $155 billion. The obligation that matters is not the bond line. It is leases: long-term leases of about $79 billion, and data-center leases not yet commenced of $329.1 billion, starting between fiscal 2027 and 2033, some subject to conditions. Microsoft also reclassified future data-center leases from finance leases to operating leases, which cut reported calendar-2026 capex guidance from about $190 billion to about $175 billion without changing the build. That is accounting flexibility a junk issuer does not have. Fiscal Q4 free cash flow was $19.6 billion, down 23 percent, but still positive and above estimates. [23] [24] [25] [26]

The group as a whole is stretching. 2026 capex guidance is about $175 billion at Microsoft, about $220 billion at Amazon, $195–205 billion at Alphabet, and $130–145 billion at Meta, roughly $720–745 billion combined before Oracle. Debt’s share of hyperscaler capex rose from about 9 percent in fiscal 2024 to about 32 percent on a trailing basis by mid-2026. Epoch AI’s reading of filings has aggregate cash capex on track to exceed operating cash flow around the third quarter of 2026. Goldman counted nearly $500 billion of AI-related debt issuance in 2026 by early August. [27] [28] [26]

That is a cost-of-capital problem. It is not a solvency problem for companies that still generate more than $100 billion of operating income. They can fund a multi-year gap, slow the build, or reprice capacity to other customers if a lab stumbles. The entities that cannot are the ones already showing stress. CoreWeave’s total liabilities were about $52 billion by June 2026, with standalone debt around $35 billion and quarterly interest of $640 million, more than double a year earlier. On October 14, yields on its 9.625 percent bonds due 2032 hit 15 percent and the stock fell more than 15 percent. Oracle fell 8.8 percent the same day. [29] [30]

Zitron’s error is treating that credit fracture as if it were Microsoft’s balance sheet. A CoreWeave or Oracle problem can hurt NVIDIA’s customer mix and force contract renegotiation. It does not, by itself, make Sundar Pichai or Satya Nadella unable to pay for GPUs.

The labs can still raise, and they can still rewrite the bill

Capital markets have not closed. OpenAI closed $122 billion of committed capital in March 2026 at an $852 billion post-money valuation. As of September 29 it was in early talks to raise at least another $30 billion at about a $1.4 trillion pre-money valuation, as bridge financing after Sam Altman pushed an IPO out of 2026. Terms can change. The round is not closed. The existence of investor-led demand at a higher mark, six months after the largest private round in the sector’s history, is incompatible with “they cannot raise.” [31] [32] [33]

Anthropic raised $65 billion at a $965 billion valuation in May, confidentially filed, and is discussed at $1.8–2 trillion, with a roadshow reported for the week of November 9. The same draft that bulls use for the valuation is the document that makes Zitron’s commitment point: at least $518 billion of cloud, compute, and equipment obligations, about 80 percent non-cancelable or payable regardless of use, including about $111 billion with Google, $110 billion with Amazon, $161 billion of Broadcom-linked equipment leases, and $31.4 billion with Microsoft. The filing says that if spend falls short, Anthropic must pay Google the difference. [7] [34] [33]

Those commitments are the real risk. They are also renegotiable in a way a bond covenant is not, because the counterparties are the same companies that own the capacity, hold equity, and want the labs as anchor tenants. A restructuring would be ugly for Anthropic’s valuation. It would not automatically zero the GPUs. Microsoft, Amazon, and Google can reallocate unused capacity to other cloud customers, which is exactly what a $514 billion Google Cloud backlog and a $678 billion Microsoft backlog are for. The circularity cuts both ways: the hyperscalers are on the hook, and they are also the residual buyers.

The track record is a sequence of missed ceilings

Dan Luu’s September 1, 2026 review of resolved Zitron predictions is the cleanest public scorecard, and the pattern is specific ceilings stated with high confidence, then missed:

• February and March 2024: generative AI had hit the upper limit of what it could do. Wrong.
• October 2024: OpenAI’s forecasts, including $11.6 billion for 2025, were so absurd they were close to a “financial crime.” OpenAI’s 2025 revenue was $13.07 billion. Wrong on the number he chose to mock. The Financial Times, looking at the same materials Zitron shared, also calculated the cash loss nearer $8 billion on $13.07 billion of revenue after a conversion-related write-down, not the larger burn figure he emphasized. [35] [36]
• February 2025: Anthropic’s $34.5 billion revenue forecast for 2027 was “laughable.” By mid-2026 the company’s reported run rate was already above that. Wrong, or at minimum no longer laughable. [35]
• February 2025: Google’s 500 million Gemini-user target was so unrealistic Sundar Pichai should be fired. Gemini later cleared 650–750 million. Wrong. [37] [38]
• October 2025: asked when the bubble pops, “no later than Q2 2026.” That date has passed. Wrong as a testable upper bound. [35]
• November 2025: the models are not getting better and are roughly what they will always be. Wrong on the capability record since. [35]

Kelsey Piper’s April 2026 critique in The Argument is methodological rather than a prediction scorecard. She argues that the 2024–25 case was a business case, companies were not using the tools and would run out of cash, and that the 2026 case shifted toward Enron- or FTX-style fraud allegations once usage and revenue made the older case harder. She also notes he has analogized OpenAI to FTX while conceding he does not have proof that free tokens are booked as revenue. That is a real method problem: stacking unproven fraud claims so each one props up the next. [39]

Timothy B. Lee documented a concrete source failure. A Zitron spreadsheet meant to show Anthropic’s 2025 revenue was only $3.6 billion, implying the company’s statements did not add up, skipped February 1–10, double-counted March 1–10, treated August 21–October 21 as one month, and omitted October 21–November 1. Fixing the date errors produced roughly $4.3–4.5 billion, in line with later reported figures. Lee said he emailed the errors and got no response. A linked spreadsheet is not a check. [40] [37]

None of this means the capex gap is fake. It means timelines, capability ceilings, and “the filings don’t match” claims have a poor hit rate, and that high rhetorical certainty has not updated when the numbers did. In Vanity Fair he brushed off the boy-who-cried-wolf question by saying 2024 was a long time ago and he had been naive about how long the system would tolerate the gap. That is an admission that the mechanism can persist longer than the call. [41]

Incentives reward the unbroken call

Zitron is not a short seller with a disclosed position. He is a publisher. Where’s Your Ed At, the Better Offline podcast, and a social audience of about 156,000 followers monetize a crisp, certain bear case. Nuance does not book the CNBC, Bloomberg, and YouTube circuit he has been on through 2026. Dan Luu’s review quotes the standard attention mechanism: crisp positions with no room for doubt outperform “the market could go either way.” [42] [35]

WIRED’s October 2025 profile documented the other incentive tension. He runs EZPR. At the time of that profile he said the firm had four clients and that he did not want to pitch generative AI, while the piece also described him pitching an LLM-connected client and representing automation products such as DoNotPay. He told WIRED the AI criticism had not cost him PR business. The conflict is narrower than “he is paid by OpenAI.” It is that the public product is anti-hype certainty, and the private product has included AI-adjacent clients. That does not falsify a capex spreadsheet. It does mean the brand cannot easily publish “I was early, and the gap is narrowing.” [43]

A separate, quieter bias is definitional. If revenue must be profitable, diversified, non-circular, and large enough to cover a year of capex in that same year, then almost any infrastructure build fails the test until it is finished. Telecom dark fiber and early AWS both failed that test. Zitron’s answer, in a June 2026 interview, is that those analogies are “hogwash” and that the industry needs at least $200 billion a year of AI-compute revenue to make sense. On current cloud and lab figures, that bar is no longer absurd. It is also not yet cleared on Goldman’s $300 billion break-even math. The disagreement is now about slope and who holds the residual risk, not about whether revenue exists. [44]

What would falsify him in the next 6–12 months

Through October 2027, the strong thesis dies if the following hold together. None of them requires AGI.

• NVIDIA prints the $108 billion October-quarter guide, or something close, keeps gross margin around 74 percent, and does not guide a sequential air pocket. A second quarter of rising backlog or customer prepayments would make “unsold GPUs in warehouses” very hard to maintain. [19]
• OpenAI closes the reported $30 billion-plus round at or above a $1 trillion valuation, and Anthropic either lists above $1 trillion or raises again without a down round. Failure to close, or a pulled IPO, would strengthen him.
• Microsoft, Amazon, and Alphabet keep cloud growth well above 20 percent and do not cut 2027 capex by a large fraction while talking about excess capacity. A voluntary slowdown because returns are late is not the same as a cash crisis. A guidance cut paired with rising credit spreads at the AA/AAA names would be.
• Incremental AI cloud revenue moves materially off Goldman’s ~$70 billion-above-trend figure toward the $150 billion-plus range, shrinking the $230 billion annual gap. If that gap is unchanged or wider after another year of 30–40 percent cloud growth, his returns math wins even if nobody goes bankrupt. [17]
• Audited or prospectus-quality lab numbers show cash losses narrowing, or at least growing slower than revenue, rather than compute bills compounding faster than sales. Anthropic’s claimed adjusted operating profit quarter, if it survives the S-1, is an early version of this. A cash runway under 12 months without new capital would revive the “money runs out” line for the labs specifically.
• McKinsey-style surveys show EBIT attribution rising well above 37 percent, or the high-performer share moving off 6 percent. If individual productivity stays high and enterprise profit attribution stays flat through 2027, the “useful but not worth the capex” case survives.

What would confirm the strong thesis instead: a NVIDIA guide-down, a hyperscaler capex cut of the sort that implies cancelled orders rather than lease reclassification, a failed OpenAI round, an Anthropic listing pulled or priced as a disappointment, and a CoreWeave-style credit event that forces Microsoft, Amazon, or Google to take visible losses on guarantees. One neocloud bond at 15 percent is not that event. A cluster of them, plus an Oracle contract rewrite that hits hyperscaler earnings, would be.

How strong the case is

Split the thesis, because the blended version is what makes him sound either prophetic or unserious.

The narrow case is moderately strong, about 6.5 out of 10. ARR is a sloppy metric. Lab GAAP revenue is far below run rate. Anthropic’s $518 billion of largely non-cancelable commitments against $4.6 billion of 2025 revenue is an extraordinary mismatch. Goldman’s ~$230 billion annual revenue gap is a mainstream version of his arithmetic. Enterprise EBIT attribution is flat. Credit stress is real at CoreWeave and Oracle. Vendor financing and guarantees make the demand partly circular. Those points do not require believing the revenue is fraudulent.

The strong case is weak, about 3 out of 10. The bubble did not pop by the second quarter of 2026. Models kept improving. Lab revenue and cloud revenue both accelerated. NVIDIA is supply-constrained at 75 percent margins with a $279 billion commitment book. Microsoft cut traditional debt while printing $155 billion of operating income. OpenAI raised $122 billion in March and is in talks for more at a higher valuation. The “money runs out” mechanism does not survive contact with AA and AAA balance sheets that can choose to spend. Collapsing those facts into an imminent 90 percent NVIDIA drawdown requires the same style of ceiling call that has already failed.

Overall, as a forecast of systemic unwind in the next year, the case is about 4 out of 10. As a standing warning that returns are late, commitments are too rigid, and the weakest financed intermediaries can break without the technology being fake, it is the part worth keeping. The error is treating a timing and capital-structure problem as if missed deadlines and fortress balance sheets have not already changed the odds.

---

Recent Findings Supplement (October 2026)

OpenAI and Anthropic have demonstrated accelerating revenue run rates and enterprise traction in Q3 2026 that exceed many prior projections, directly challenging narratives of unsustainable cash burn without corresponding monetization.[1][2]

• OpenAI reported an annualized revenue run rate approaching $50 billion at the end of September 2026 (net of partner shares, per investor documents reviewed by FT/Bloomberg), up from roughly $40 billion in August and more than double the ~$20 billion figure at the end of 2025. Enterprise revenue run rate grew 107% in Q3, with overall run rate growth at 77%; consumer additions in Q3 alone exceeded all of 2025. The company projects $70 billion or more annualized by year-end 2026, driven primarily by enterprise.[3][4]
• Anthropic posted Q3 2026 revenue of $16.8 billion (up 45% sequentially from $11.6 billion in Q2 and $4.7 billion in Q1), achieving adjusted profitability in the quarter. Its annualized run rate had already surpassed $65 billion by late July 2026 (from ~$47 billion in May and ~$9 billion at end-2025), with 2025 full-year revenue at ~$4.6 billion (12x growth from prior year). Analyst estimates from investor meetings point to a potential $110 billion run rate by year-end.[2][5]
• These figures reflect real paid usage growth (enterprise subscriptions, coding tools, API) rather than solely hype-driven projections, though annualized run rates are non-GAAP and exclude full cost structures.

This traction implies that demand-side monetization is scaling faster than some skeptics anticipated, reducing the immediacy of a revenue cliff even amid high inference and training costs.[6]

NVIDIA’s results and hyperscaler capex guidance confirm sustained, broad-based GPU and infrastructure demand into late 2026, with diversification beyond any single lab.[7]

• NVIDIA reported fiscal 2026 revenue of $215 billion (up 65% YoY), with data center at $193.7 billion; Q2 fiscal 2027 data center revenue reached $89 billion (up 117% YoY) on Blackwell ramp, hyperscale strength, and expanding customers (AI labs, enterprises, sovereigns). CFO highlighted “fastest product ramp” expectations for next-generation Vera Rubin.[8][9]
• Outside China, demand remains robust; in China, NVIDIA’s data-center AI accelerator share fell to 34% (shipments down 44% YoY to 678k units) as Huawei rose to 28%, but total Chinese accelerator market grew and non-GPU accelerators surged.[10]
• Hyperscalers (Amazon, Microsoft, Alphabet, Meta, Oracle) are guiding combined 2026 capex in the $600–820 billion range (potentially >$1 trillion in 2027), with AI infrastructure as the driver; Amazon raised its 2026 capex outlook to ~$220 billion.[11][12]

Persistent hardware demand and diversified buyers indicate that GPU utilization and buildout are not stalling, countering claims of imminent overcapacity or demand collapse.[8]

Hyperscalers and AI labs are using a mix of public bonds, private credit, SPVs, leases, and vendor backstops that distribute risk and leverage their scale/cash flows, insulating core operations from immediate distress.[13]

• AI-related corporate debt issuance reached ~$450–570 billion in 2026 (multiple estimates), with hyperscalers accounting for a large share (e.g., Amazon’s $54 billion bond sale); tech IG bond issuance rose to ~8% of total U.S. IG. Aggregate lease/commitment liabilities exceed $1.5 trillion (much off-balance-sheet).[12][14]
• Structures include SPVs/joint ventures (e.g., Meta data center with private credit), sale-leasebacks, and vendor financing (NVIDIA backstopping >$100 billion for an OpenAI Ohio project; Broadcom packages for Anthropic/OpenAI). Credit metrics are weakening but protected by scale, dominance, and cash generation; analysts note downside risk is limited.[13][15]
• OpenAI closed a $122 billion round in March 2026 at $852 billion post-money (Amazon, NVIDIA, SoftBank, MGX among backers) and is now in talks for $30 billion+ at ~$1.4 trillion pre-money.[1]

These mechanisms allow continued spending without immediate balance-sheet collapse for the largest players, shifting some risk to specialized financiers while preserving flexibility.[16]

Analyses from September–October 2026 document a pattern of high-confidence predictions by Zitron on AI timelines, company viability, and market outcomes that did not materialize, alongside questions about methodology and selective framing.[17]

• Documented misses include forecasts of Meta as a “dying company” (actual revenue/profit growth through 2025–H1 2026), AI bubble popping “no later than Q2 2026,” models hitting a permanent capability wall by late 2025, and collapses at specific firms (e.g., Cohere, Cursor). OpenAI and Anthropic revenue exceeded or approached targets he questioned.[18][18]
• Critiques highlight spreadsheet errors in revenue estimates (e.g., Anthropic), treatment of temporary constraints (data, scaling) as permanent, and a rhetorical style of near-certainty on falsifiable claims. Outlets and analysts (e.g., Dan Luu compilations) note audience incentives for contrarian positioning via newsletter/podcast.[17][19]

Zitron continues to highlight valid concerns around un-audited ARR figures, warehouse GPU inventory, and financing costs (e.g., October 2026 commentary on private credit as a “brewing crisis”), but the track record of mistimed calls weakens the predictive power of his broader thesis.[20]

Both labs are successfully raising or preparing to raise capital at escalating valuations, with Anthropic advancing toward a potential >$2 trillion IPO (targeted November 2026 or later) and OpenAI pursuing a large private bridge round ahead of a 2027 public listing.[21]

• Anthropic’s pre-IPO investor meetings highlighted Q3 results and adjusted profitability; OpenAI’s latest round involves UAE funds (e.g., MGX) and strategic partners.[2][1]
• These outcomes show continued access to private (and soon public) capital despite losses and commitments ($518 billion+ for Anthropic infrastructure; similar scale for OpenAI).

Successful execution here would further insulate the ecosystem by providing equity-like funding and liquidity, testing whether revenue growth can eventually support the capital structure.[22]

Indicators that would falsify or significantly weaken Zitron’s core view (unsustainable economics, overhyped demand, inevitable reckoning) in the next 6–12 months include: sustained or accelerating revenue growth into 2027 with improving unit economics; hyperscaler capex delivering measurable ROI or utilization without major impairments; successful high-valuation IPOs or further raises without dilution spirals; and NVIDIA maintaining or growing data-center revenue amid Blackwell/Rubin ramps.[23]

Conversely, sharp revenue deceleration, widespread project delays/cancellations, or credit events in private financing vehicles would strengthen it.[24]

Overall, Zitron’s case on financing risks, opacity in metrics, and long-term ROI uncertainty retains substantive elements and aligns with some analyst caution, but recent revenue traction, demand persistence, and capital access indicate it is overstated in its immediacy and breadth as of October 2026; the evidence tilts toward continued (if volatile) expansion rather than near-term collapse.[6]

New data since mid-2026 has consistently shown faster monetization and investor willingness to fund the buildout than the most pessimistic timelines projected.