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Pfizer and JPMorgan Prove Quantum Computing Works in 2026
Enterprise Technology
Pfizer and JPMorgan Prove Quantum Computing Works in 2026
By the NeuralWired Research Desk | Updated July 8, 2026
Pfizer cut a four month drug simulation down to days. JPMorgan Chase ran real stock portfolios on a 98 qubit quantum computer using actual market data, not a simulation. Quantum computing real world applications are no longer confined to conference keynotes and vendor slide decks. They are showing up, quietly and specifically, inside three industries: pharmaceuticals, financial services, and logistics.
This isn’t the “quantum computing will change everything” story you’ve read a dozen times since 2019. It’s narrower than that, and more useful because of it. Three companies, three named pilots, three sets of numbers you can check yourself.
A quick correction, because accuracy matters here. You may have seen claims that Pfizer “saved 18 months” on simulation time, or that JPMorgan modeled “10,000 portfolio scenarios in seconds.” Neither figure appears in any primary source from Pfizer, XtalPi, or JPMorgan. What’s actually documented, and cited below, is arguably more interesting: a four month process cut to days, and a real 225 asset portfolio problem run on live trapped ion hardware. No inflation needed.
How Pfizer actually uses quantum physics in drug discovery
Here’s the part most coverage gets wrong: Pfizer isn’t running its drug discovery pipeline on a quantum computer. Not yet, anyway. What it’s doing is arguably more interesting, because it’s already working.
Pfizer partnered with XtalPi, a pharmaceutical tech company founded by MIT trained quantum physicists, on crystal structure prediction. It’s a quantum physics based computational technique, built on AI and cloud infrastructure rather than a standalone quantum processor, that predicts how a drug candidate’s molecules will arrange themselves in three dimensions before a single physical crystal is grown.
That prediction used to take up to four months. Through the XtalPi collaboration, it now takes a matter of days.
Pfizer describes crystal structure prediction as a process the team used to avoid attempting, given how long it took, and now runs on nearly every small molecule project.
Bruno Hancock, Global Head of Materials Science, Pfizer, Groton CT (via Pfizer.com)
Hancock also notes that a single crystal structure prediction requires computing power roughly equivalent to one million laptops. That’s the kind of number that explains why the four month wait existed in the first place, and why shrinking it to days matters commercially, not just academically.
Separately, Pfizer’s internal AI and Digital Accelerator team has published research applying a digitized counterdiabatic version of the Quantum Approximate Optimization Algorithm to molecular docking, the process of predicting how a drug candidate binds to its target protein. As of the March 2025 paper, that work ran on GPU clusters simulating quantum behavior, not on live quantum hardware. Worth flagging plainly: this is AI plus quantum-inspired computation, not a production quantum computer. Conflating the two is the single most common inaccuracy in secondary coverage of this topic.
Pfizer isn’t alone. Merck and GlaxoSmithKline are running comparable quantum-adjacent drug discovery partnerships, which tells you this is a sector-wide bet, not a one-company experiment.
If you want the single most citable data point in enterprise quantum computing right now, this is it. On July 1, 2026, JPMorgan Chase and Amazon’s AWS Center for Quantum Computing published research titled “Quantum-Informed Portfolio Selection,” and it’s the rare quantum paper that used real market data instead of synthetic test cases.
The team framed portfolio diversification as a graph theory problem (specifically, a Maximum Independent Set problem) and ran it on Quantinuum’s 98 qubit Helios trapped ion quantum computer, using actual correlation data from four major stock indices: the DAX, FTSE 100, S&P 100, and Nikkei 225, with as many as 225 individual assets.
The results are blunt about where quantum computing currently stands. Standalone QAOA, the algorithm most associated with near-term quantum optimization, failed completely on the two hardest indices, the S&P 100 and Nikkei 225, hitting a 0% success rate on its own. JPMorgan’s hybrid workaround, an algorithm called qReduMIS, achieved success probabilities of 0.40 on the S&P 100 and 0.95 on the Nikkei 225, with approximation ratios of 0.96 or better across all four indices.
That gap between “pure quantum failed” and “hybrid quantum worked” is the actual story. It’s not that quantum computers solved finance. It’s that a hardware ceiling (98 qubits couldn’t natively handle a 225 asset problem) forced a classical-quantum workaround that ended up working well.
Pistoia has credited JPMorgan’s access to NVIDIA GPU based supercomputing through Argonne National Laboratory as central to running the large-scale numerical studies behind its quantum optimization research.
Marco Pistoia, Head of Global Technology Applied Research and Head of Quantum Computing, JPMorgan Chase (via JPMorganChase Technology Blog)
JPMorgan has since filed a patent for quantum-assisted portfolio selection, and on June 3, 2026, announced a research collaboration with Oxford Quantum Circuits and AMD to build a dedicated Quantum-AI Data Centre in London, giving its quantum team a permanent testing environment for hybrid applications. Goldman Sachs, HSBC, and BBVA are running comparable pilots with IBM, D-Wave, and QC Ware, so read this as a competitive race across Wall Street, not a JPMorgan exclusive.
DHL, Volkswagen, and the logistics pilots nobody’s hyping
Logistics gets less press than pharma or finance in the quantum conversation, which is strange, because the underlying math (vehicle routing, crew scheduling, warehouse allocation) is exactly the kind of combinatorial optimization problem quantum algorithms are theoretically suited for.
DHL is benchmarking quantum-hybrid vehicle routing solvers against classical solvers for last-mile delivery, which accounts for 40 to 50% of total logistics cost. On 50 to 100 stop delivery instances, quantum-hybrid approaches currently perform comparably to classical solvers, with an expected advantage as hardware and problem sizes scale up.
Volkswagen is the most-documented enterprise quantum optimization user in the world, running pilots since 2017 across traffic routing, paint-shop scheduling, and battery chemistry research. Its 2019 Lisbon bus-routing pilot was the first public urban traffic optimization test using quantum methods.
Qantas, Airbus, and IBM Quantum ran a proof-of-concept back in 2021 applying quantum computing to flight crew scheduling, widely considered one of aviation’s hardest combinatorial problems. More recently, Airbus and BMW ran a 2024 Quantum Computing Challenge tasking entrants with optimizing a global aircraft manufacturing supply network for cost, delivery time, and carbon emissions simultaneously. Lufthansa Cargo, Amazon, and Carrefour are testing similar approaches for routing, maintenance prediction, and warehouse operations.
Industry
Lead organization
What’s actually running today
Pharma
Pfizer + XtalPi
Crystal structure prediction: 4 months cut to days
Finance
JPMorgan Chase + AWS
225-asset portfolio diversification on 98-qubit hardware
Logistics
DHL, Volkswagen, Airbus/BMW
Routing and scheduling pilots at 50 to 100 stop scale
What the market data says about where this is headed
The global quantum computing market hit $1.4 billion in 2025, according to the Quantum Economic Development Consortium’s 2026 industry report, one of the few figures in this space with a named, transparent methodology (a survey of over 7,400 quantum-engaged organizations). The broader quantum technology market is projected to roughly double by 2028. Use that figure over the vendor market-research estimates floating around, which range anywhere from $1.6 billion to $3.6 billion for the same 2025 baseline, because each vendor scopes “quantum market” differently.
Banking, financial services, and insurance hold the single largest projected quantum computing end-use market share at 26.11% in 2026, according to Fortune Business Insights, which lines up with why JPMorgan’s research is getting so much attention right now.
Capital is moving fast. Private venture investment in quantum technology hit $4.9 billion in 2025, per McKinsey’s Quantum Technology Monitor 2026, more than doubling the prior record year. McKinsey projects the broader quantum technology market could reach $60 billion to $100 billion globally by 2035, with quantum computing specifically accounting for $43 billion to $71 billion of that.
More than half of quantum computing companies expect at least an 11% revenue increase from 2025 to 2026, and 37% are projecting growth above 25%, per QED-C’s survey data. The global pure-play quantum workforce reached roughly 16,500 professionals in 2025, up about 2,000 in a single year, a growth rate that, as you’ll see below, isn’t keeping pace with demand.
The algorithm gap: why hardware is outrunning usefulness
Here’s the honest part most vendor content skips.
If a business were handed a working quantum computer tomorrow, could it actually run its intended quantum algorithm? For most of the field today, the honest answer is not really.
Robbie King, doctoral researcher in quantum computing, Caltech (as reported in coverage of the field’s current state)
That’s the structural problem underneath every pilot in this article. Qubit counts and error correction are improving faster than most experts predicted a decade ago. The theoretical work needed to find problems where quantum genuinely beats classical computing at useful scale hasn’t kept pace. JPMorgan’s own paper is a case study in this: its hybrid workaround was necessary specifically because the full 225 asset problem exceeded the 98 qubit hardware’s native capacity. That’s a hardware ceiling being managed cleverly, not quantum computing beating classical computing outright.
Talent is a separate, compounding bottleneck. McKinsey has found roughly one qualified quantum candidate for every three job openings in the field, with less than half of quantum computing roles currently filled. Hardware and algorithms could both accelerate tomorrow and adoption would still be capped by how many people know how to build on top of either.
There’s one place where the timeline genuinely has moved up, and it’s not commercial optimization. It’s cryptography.
People whose judgment on quantum hardware and error correction I trust more than my own now tell me a fault-tolerant quantum computer capable of breaking deployed cryptography ought to be possible by around 2029.
Scott Aaronson, Schlumberger Centennial Chair of Computer Science, UT Austin, and elected member of the U.S. National Academy of Sciences (via scottaaronson.blog, May 1, 2026)
Aaronson has spent years as quantum computing’s most credible public skeptic. That he’s now sounding an alarm, specifically about cryptography and not about drug discovery timelines or supply chain ROI, is worth sitting with. Coverage that blends “quantum could break encryption soon” with “quantum will transform your logistics network soon” is collapsing two very different maturity curves into one story. They’re not the same story, and treating them as one is where a lot of quantum journalism goes wrong.
Google set an internal 2029 deadline to migrate to post-quantum cryptography, announced in a March 2026 blog post, part of a broader “harvest now, decrypt later” security conversation now spreading across finance and government. If your organization handles sensitive data with a long shelf life, that deadline applies to you regardless of whether quantum computing ever delivers on the drug discovery or portfolio optimization promises above. For more on what that migration actually involves, see our guide to the NIST post-quantum migration deadlines.
What CTOs and technical leaders should actually do
Nothing here is operational yet for most companies. Every pilot named in this article, Pfizer/XtalPi, JPMorgan/Quantinuum, DHL, Volkswagen, Airbus/BMW, is a hybrid research collaboration, not a production system quietly replacing classical infrastructure. If you’re a technical leader wondering whether to act now, here’s the honest breakdown:
Benchmark before you budget. JPMorgan’s paper shows the crossover point where quantum-hybrid starts beating classical depends heavily on problem structure, not just raw problem size. Run that math on your own optimization workloads before committing spend.
Start quantum talent planning now, not later. With workforce growing roughly 14% a year against surging job openings, waiting until you have a defined quantum use case means competing for talent that’s already scarce.
Treat post-quantum cryptography migration as non-negotiable. Unlike the optimization and simulation use cases above, PQC migration has a real deadline attached to it (2029, per Google’s own internal target) independent of whether quantum delivers business ROI on any particular schedule.
Frequently asked questions
Is quantum computing actually being used today?
Yes, in narrow pilots. Pfizer, JPMorgan, DHL, Volkswagen, and Airbus all run hybrid quantum-classical programs today, but every documented case remains a research pilot or proof-of-concept rather than a production system replacing classical infrastructure at scale, as of mid-2026.
How is quantum computing used in drug discovery?
Quantum physics based computational methods, often paired with AI and cloud computing, predict a drug candidate’s 3D molecular structure through crystal structure prediction, far faster than traditional X-ray crystallography. Pfizer’s XtalPi partnership cut this from up to four months down to a matter of days.
How is quantum computing used in finance?
Banks use quantum and hybrid quantum-classical algorithms mainly for portfolio optimization, risk analysis, and option pricing. JPMorgan Chase’s July 2026 research with AWS ran real stock-index portfolio diversification on a 98-qubit Quantinuum trapped-ion computer using live market data.
What is the market size of quantum computing?
Estimates vary by scope, but the Quantum Economic Development Consortium put the global quantum computing market at $1.4 billion in 2025, with the broader quantum technology market projected to roughly double by 2028, per its 2026 industry report.
Which industries benefit most from quantum computing?
Banking, financial services, and insurance hold the largest projected quantum computing market share, at 26.11% in 2026, per Fortune Business Insights, followed by pharmaceuticals and logistics-manufacturing optimization.
Is quantum computing overhyped?
Partly. Hardware progress has outpaced expert predictions from a decade ago, but the algorithms needed to exploit that hardware for real business problems, and the talent to build them, both lag well behind, according to researchers including Caltech’s Robbie King.
Where this goes next
What you now know that most coverage of this topic gets wrong: quantum computing isn’t quietly running production systems at Pfizer, JPMorgan, or DHL. It’s running specific, named, hybrid pilots, and the results (a four month process cut to days, a 225 asset portfolio problem solved on real hardware) are genuine engineering progress without being business transformation, yet.
Watch three things over the next 6 to 18 months: whether JPMorgan’s qReduMIS approach gets adopted by other banks now racing on the same problem, whether Pfizer or a competitor moves crystal structure prediction work onto actual quantum hardware rather than quantum-inspired classical infrastructure, and whether Google’s 2029 post-quantum cryptography deadline starts pulling forward migration timelines at other major cloud and financial providers.
The technology that will actually reshape your industry in the next few years might not be the one generating the most headlines. Sometimes it’s the specific, unglamorous pilot quietly working, not the sweeping claim that doesn’t hold up under a fact check.
Deployment frequency is up. Lead time is down. Every dashboard is green. And your VP of Engineering still can’t explain why the roadmap slipped a quarter behind. If that sounds familiar, you’re not measuring the wrong things badly. You’re measuring the wrong things well.
DORA metrics, the deployment frequency, lead time, change failure rate, and recovery time framework born out of Google Cloud’s DevOps Research and Assessment program, have become the default scoreboard for engineering performance. In 2024, only 19% of teams surveyed hit “elite” status on that scoreboard. Yet DORA’s own research team has publicly warned against using these numbers to judge team performance at all. So what are engineering leaders supposed to trust instead?
DORA started as a research program, not a dashboard. Dr. Nicole Forsgren, Jez Humble, and Gene Kim built it, and their 2018 book Accelerate introduced what became known as the Four Keys: deployment frequency, lead time for changes, change failure rate, and time to restore service. Google Cloud has run the program since acquiring the founding team’s research in 2018.
In 2024, DORA added a fifth metric: rework rate, which tracks how many deployments are actually emergency fixes for problems the last deployment caused. That addition alone tells you something. The original four measure how fast you move. Rework rate exists because moving fast and moving in circles started to look identical on the old dashboard.
Quick definition: An “elite” DORA performer deploys on demand, has a lead time under one day, keeps change failure rate near 5%, and restores service in under an hour. In 2024, roughly one in five surveyed teams qualified. Source: DORA 2024 State of DevOps Report
The “Elite Performer” Number Nobody Questions
Here’s the stat that gets stapled to every engineering leadership deck: elite performers deploy 182 times more frequently than low performers, and they restore service 2,293 times faster. Those numbers are real, pulled from a survey of more than 39,000 professionals for the 2024 State of DevOps Report. They’re also the least useful numbers in the report if you’re trying to explain a missed quarter.
Look at what happened to the middle of the distribution instead. Between 2023 and 2024, the share of low-performing teams grew from 17% to 25%. The share of high performers shrank from 31% to 22%. The industry didn’t get better at DevOps last year. It got worse, on average, while adopting more DevOps tooling than ever.
Metric
2023
2024
Low-performing teams
17%
25%
High-performing teams
31%
22%
Elite-performing teams
not tracked
19%
That’s the gap the headline is pointing at. A team can hit every DORA benchmark and still be part of a shrinking pool of teams whose actual delivery outcomes are stagnant or backsliding.
Why Speed and Goals Keep Diverging
DORA’s own research team saw this coming. In October 2023, according to reporting cited on DORA’s Wikipedia entry, the team explicitly warned against using the Four Keys to evaluate individual teams’ performance. That’s an unusual thing for a research program to say about its own flagship metrics. It’s also exactly what you’d expect once a research tool turns into a KPI baked into Jira, GitLab, and every engineering-analytics dashboard on the market.
This is Goodhart’s Law showing up in production code. Once deployment frequency becomes the target, it stops measuring what it used to measure. Teams under pressure to hit a number will split pull requests into smaller, more frequent deploys without changing what actually ships. They’ll quietly under-report incidents to protect their change failure rate. None of that improves the product. All of it improves the chart.
Teams pressured to raise their deployment rate by a fixed percentage can hit that target simply by shipping smaller changes more often, without touching the bugs or incidents that actually determine whether users are happy.
Laura Tacho, CTO, DX · getdx.com/podcast
DORA even flags this tension inside its own 2024 data. Teams that adopted internal developer platforms saw individual productivity and overall organizational performance improve, but the report also found decreased change stability and throughput as a side effect. Speed up one lever, and another one moves without anyone touching it.
What AI Adoption Did to the Data
If DORA metrics were shaky before, AI made the cracks visible. The 2024 report found that a 25% increase in AI adoption correlated with a 1.5% decrease in throughput and a 7.2% decrease in stability, DORA’s own team flagged this as correlational rather than causal, but the direction is notable.
By the time the 2025 State of AI-assisted Software Development Report came out, AI use had reached 90% of surveyed professionals, with more than 80% reporting productivity gains. But 30% still said they had little or no trust in the code AI generated for them. The report’s core finding, drawn from nearly 5,000 professionals and over 100 hours of qualitative interviews, was blunt: AI doesn’t fix a broken team. It amplifies whatever was already there. Strong teams get stronger. Struggling teams get their existing dysfunction on fast-forward.
That’s the mechanism behind the headline. A team with process debt that starts using AI coding tools doesn’t quietly improve. It ships more, faster, with the same underlying gaps, and those gaps show up downstream as missed goals rather than upstream as slow commits.
The Case Against DORA Entirely
Not everyone thinks DORA metrics deserve the reverence they get. Dr. Junade Ali, a software engineering manager who ran independent polling with Survation and J.L. Partners, published a pointed critique on HackerNoon in January 2024 arguing the entire premise is backwards.
His research found that both software engineers and the general public rank data security, data accuracy, and bug prevention well above deployment speed when asked what matters in software delivery. That directly contradicts what the Four Keys are built to optimize for. Ali also points out that DORA’s team doesn’t publish raw survey data, unlike polling organizations bound by disclosure rules such as the British Polling Council, which require full data tables within two working days of publication.
It’s hard to find a hypothesis connecting the Four Key Metrics to the outcomes that developers and the public actually say they care about most.
Dr. Junade Ali, Software Engineering Manager · HackerNoon, January 2024
His research also found something worth sitting with: 98% of UK business decision-makers and 96% of their US counterparts agreed that the actual goal of an engineering team is delivering high-quality software on time, not shipping the highest possible number of deploys. Nobody polled thinks speed is the goal. Yet speed is what gets measured, reported, and rewarded.
What Replaces DORA in 2026
The clearest answer to “what should we measure instead” so far is DX Core 4, a framework announced in December 2024 by DX co-founder and CEO Abi Noda and DX CTO Laura Tacho, built with input from DORA co-creator Dr. Nicole Forsgren and Dr. Margaret-Anne Storey. It’s worth being upfront here: DX sells the platform that implements this framework, so its published outcomes come from the vendor itself, not an independent auditor.
With that disclosed, the numbers are still notable. Tested across more than 300 organizations, DX Core 4 has been associated with 3 to 12% increases in engineering efficiency and a 14% increase in R&D time spent on new feature development. The framework’s structure is the real change: it pairs DORA’s speed metrics with effectiveness, quality, and business impact measures, so a team can’t improve one number by quietly breaking another.
The big question is, what should we actually be measuring? DORA’s throughput numbers alone were never built to capture developer experience or business impact.
Abi Noda, Co-founder & CEO, DX · LeadDev, December 2024
The market is already voting with its budget
Platform engineering investment backs this shift up. Gartner projections cited in industry compilations put platform engineering team adoption at 80% of large software organizations by 2026, up from 45% in 2022 (worth verifying directly against a current Gartner release before you cite the figure yourself). The broader DevOps software market itself is priced anywhere from roughly $15 billion to nearly $19 billion for 2026 depending on which research firm you ask, a wide enough range that any single number should be treated as directional, not precise.
Our read: this signals engineering leadership is done treating DORA as a finished answer. The direction for 2026 is DORA plus a counterbalancing quality or business-impact metric, not DORA replaced outright.
Frequently Asked Questions
What are the DORA metrics?
DORA metrics are five software delivery measurements, deployment frequency, lead time for changes, change failure rate, failed deployment recovery time, and rework rate (added in 2024), developed by Google Cloud’s DORA research program to evaluate delivery speed and stability.
What is an elite DORA performer?
In DORA’s 2024 report, elite performers deploy on demand, have lead times under a day, keep change failure rates near 5%, and recover from failures in under an hour. Only about 19% of surveyed teams qualified as elite that year.
Are DORA metrics enough to measure engineering success?
No. DORA’s own team warned in October 2023 against using the Four Keys to evaluate individual teams. Newer frameworks like DX Core 4 pair DORA with developer experience and business impact metrics to avoid a narrow, gameable view of performance.
What is Goodhart’s Law and how does it apply to DORA metrics?
Goodhart’s Law holds that once a measure becomes a target, it stops being a good measure. Applied to DORA, teams pressured to hit deployment-frequency targets can split pull requests artificially or under-report incidents to protect their numbers, without improving actual delivery outcomes.
What is DX Core 4?
DX Core 4 is a 2024 framework combining DORA, SPACE, and DevEx research into four dimensions: speed, effectiveness, quality, and business impact. It was built by DX’s Abi Noda and Laura Tacho with input from DORA co-creator Dr. Nicole Forsgren.
Where This Goes Next
Here’s what the data actually tells you, once you stop reading the headline numbers in isolation: DORA metrics were never designed to be a scoreboard for individual teams, and the program’s own researchers said so in writing back in 2023. What they measure well is delivery speed and stability at an aggregate level. What they can’t tell you is whether that speed is producing anything your business actually wanted.
Over the next 6 to 18 months, expect three things to play out. First, more engineering orgs will pair DORA with a second framework, DX Core 4 or something built in-house, rather than reporting DORA numbers alone in board decks. Second, AI’s split effect (individual productivity up, organizational stability shaky) will keep showing up in DORA’s own annual reports until teams fix underlying process debt instead of layering AI on top of it. Third, watch for tooling vendors to start marketing “beyond DORA” dashboards as a category, the same way “shift-left security” became a category once perimeter security stopped being enough on its own.
Three things worth watching yourself over the next few quarters: whether your org’s change failure rate moves in the same direction as your deployment frequency, whether anyone above you is asking about rework rate at all, and whether a platform engineering investment is quietly trading stability for speed without anyone naming the tradeoff out loud.
Apple Gave Up on Vision Pro. Hospitals Didn’t Get the Memo.
By the NeuralWired Research Desk · July 8, 2026 · 9 min read
Apple sold roughly 390,000 Vision Pro units in 2024. In 2025, that number fell to 45,000, an 88% collapse that even a mid-cycle chip refresh couldn’t reverse. Yet a few weeks ago, a surgeon in Hauppauge, New York used the same headset to help remove a patient’s cataract. MacRumors reported that Apple has quietly stopped building the future of this product. The operating rooms using it right now didn’t get the message, and they’re not slowing down.
That split, a consumer product left for dead and an enterprise product just getting started, is the real Apple Vision Pro story in mid-2026. If you’re evaluating spatial computing hardware for your organization, or just trying to figure out whether visionOS is a platform worth building on, the headlines about Apple “giving up” are only half the picture. Here’s the other half, with the numbers to back it up.
Start with what’s verified. MacRumors reported on April 29, 2026, citing insider sources, that Apple has effectively halted active Vision Pro development following the October 2025 M5 refresh, which did nothing to move demand. Much of the original team, the report claims, has been reassigned, some of them now working under Vision Pro creator Mike Rockwell, who has led Siri since March 2025.
Within days, gHacks, TechSpot, Slashdot, and MacDailyNews had all corroborated the reporting. The consistent detail across every outlet: Apple has sold around 600,000 Vision Pro units total since the February 2024 launch, a fraction of what the company originally hoped for, and the device carries an unusually high return rate for a modern Apple product.
None of this happened in a vacuum. Apple and Meta both pulled back hard on VR marketing in 2025. Sensor Tower data, cited by The Register, shows Apple’s Vision Pro ad spend across eight major markets dropped more than 95% year over year. Meta’s Quest spend fell over 55% in the same window. When both leaders in a category stop advertising it at the same time, that’s not a coincidence. That’s a category telling you something.
The Numbers, Side by Side
IDC’s Francisco Jeronimo laid out the shipment math in a interview with The Register on January 2, 2026. It’s the cleanest single data trail in this entire story, so it’s worth putting in one place.
Metric
Figure
Source
Vision Pro units shipped, 2024
390,000 (~$1.4B revenue)
IDC
Vision Pro units shipped, 2025
45,000
IDC
2026 forecast (rebound tied to rumored cheaper model)
290,000 units (~$636M)
IDC
Cumulative units sold since Feb 2024 launch
~600,000
MacRumors insider sources
VR/MR headset category shipments, 2025
Down 42.8% industry-wide
IDC AR/VR Headset Tracker
Smart glasses shipments, 2025
Up 211%
IDC AR/VR Headset Tracker
Global enterprise AR/VR spend, 2026
~$12B, up ~20% YoY
IDC and adjacent forecasters, via VR.org
Notice that IDC still expects growth in 2026, just not back to 2024 levels. And notice the category-wide pattern: this isn’t an Apple problem specifically. Meta’s own Quest headset shipments fell 42.3% year over year even as its Ray-Ban smart glasses line boomed. Consumers aren’t rejecting Apple. They’re rejecting headsets, full stop, in favor of lighter glasses that don’t require blocking out the room to use them.
“It will never happen.”
Francisco Jeronimo, VP, IDC, on the idea that AR and VR would replace smartphones, via The Register, January 2, 2026
Jeronimo’s broader point, beyond that one blunt line, is that the consumer bet was always the wrong bet. He argues the real opportunity sits with organizations that can justify the cost through measurable efficiency gains, not with mainstream shoppers who were never going to strap on a $3,499 computer to check email.
The Pushback: Not Everyone Agrees Apple Quit
Here’s where the story gets genuinely contested, and where a lot of coverage this spring flattened a real disagreement into a clean narrative. Independent Apple analyst John Gruber, whose Daring Fireball has tracked Apple insider sourcing since 2002, directly disputed the “Apple gave up” framing the day after MacRumors published.
“It is not true that the teams have been redistributed.”
John Gruber, Daring Fireball, April 30, 2026, responding to MacRumors’ reporting
Gruber went further, stating that this was news to Apple’s own Vision Product Group and that visionOS 27, along with new hardware on two fronts, AR glasses and additional immersive Vision headsets, remains in active development for a WWDC 2026 reveal. That’s a specific, on-record contradiction of a specific, sourced claim, from two outlets with real Apple-insider track records.
Our read: this isn’t a case where one side is obviously right. MacRumors and Gruber have both been reliable Apple sources for years. The likeliest explanation sits in the middle: Apple has scaled back Vision Pro’s consumer ambitions and marketing hard, while keeping a smaller, more enterprise-and-glasses-focused platform team alive. Whether that counts as “giving up” depends entirely on what you expected Vision Pro to become in the first place.
Where Vision Pro Is Actually Winning
While the consumer story stalled, a quieter one built momentum inside hospitals and factories. On April 27, 2026, SightMD announced that ophthalmologist Dr. Eric Rosenberg had performed a Vision Pro assisted cataract surgery at its Hauppauge, New York facility using the ScopeXR platform. The actual first case had happened back in October 2025. By April 2026, hundreds of Vision Pro assisted cataract procedures had been completed.
That’s not an isolated stunt. Named institutional deployments now include:
Mayo Clinic, using it for surgical rehearsal and emergency response training
Boston Children’s Hospital, running CyranoHealth’s nurse training app
Stryker, using myMako for orthopedic surgical planning
Cedars-Sinai, for clinician empathy training and the Xaia mental health app
Siemens Healthineers, running its Cinematic Reality anatomy visualization tool
Purdue University, building a digital twin manufacturing training hub
IDC’s Ramon T. Llamas frames the broader XR platform race, of which this is one skirmish, in terms of staying power rather than early wins.
“Meta has a strong start, but both Apple and Google bring expertise.”
Ramon T. Llamas, Research Director, IDC AR/VR, December 2025, via Next Reality
Llamas compares the current jockeying to the early smartphone platform wars of the late 2000s, where the field leader at year one wasn’t necessarily the field leader at year five. It’s a useful frame, though it’s worth being honest about scale. Hundreds of cataract procedures and 60 clinical trials are real proof points, not evidence of mainstream clinical infrastructure. The $12 billion enterprise AR/VR spend figure isn’t Vision Pro specific either. It spans Meta Quest and Pico deployments too.
The CEO Exit That Ties It Together
There’s one more thread that hasn’t been widely connected, and it makes this moment bigger than a hardware sales story. Apple announced on April 20, 2026 that Tim Cook will step down as CEO effective September 1, 2026, with hardware chief John Ternus taking over.
Ternus’s predecessor in the SVP of hardware role, Dan Riccio, stepped aside in 2021 specifically to oversee what became Vision Pro, a project TechCrunch itself has called “ill-fated.” So the product now reportedly stalled out is the same one that pulled Apple’s hardware leadership away from other priorities for years, right as the next CEO inherits a company that has to decide what spatial computing actually means for its next decade.
What This Means If You’re Building on visionOS
If you’re an enterprise IT leader, XR developer, or CTO weighing procurement decisions right now, here’s the practical read.
The de-risking case: Apple pulling back on consumer volume actually helps enterprise buyers in one narrow way. You’re no longer competing with mainstream demand for limited supply, and Apple has an obvious incentive to court exactly the ROI-driven buyers, hospitals, manufacturers, universities, that Jeronimo describes as the real opportunity.
The platform risk case: Even disputed, the MacRumors report puts long-term visionOS support on the table as a real question. Software vendors like Osso Health and Medivis, along with hospital IT teams that have already built on the platform, now need contingency plans if Apple’s investment genuinely slows, regardless of what Gruber says about internal roadmaps.
The realistic timeline: IDC doesn’t expect Vision Pro unit volume to return to 2024 levels even in 2026, forecasting 290,000 units against 390,000 two years earlier. “Enterprise niche saves Vision Pro” is a plausible trajectory. It is not yet a confirmed one.
For a parallel case study in how a bigger platform owner walks away from a metaverse bet while enterprise use cases survive on different infrastructure, see our earlier coverage of Microsoft’s Mesh shutdown and the enterprise metaverse retreat, which covers BMW’s parallel bet on NVIDIA Omniverse for industrial digital twins.
FAQ
Is Apple Vision Pro discontinued in 2026?
No. Apple still sells the M5 refreshed Vision Pro and continues shipping visionOS updates. MacRumors reported in April 2026 that Apple has largely paused active development and reassigned much of the original team, a claim independent Apple writer John Gruber has publicly disputed.
Did Apple give up on the Vision Pro?
Reports differ. MacRumors cited insider sources saying Apple stopped active development and reassigned staff. Daring Fireball’s John Gruber disputed this directly, saying it was news to Apple’s own Vision Product Group and that new hardware and visionOS 27 remain in active development.
How many Apple Vision Pro units have sold?
Roughly 600,000 units total since the February 2024 launch, per MacRumors insider sourcing, well below Apple’s original internal targets. IDC shipment data shows 390,000 units in 2024 and just 45,000 in 2025.
Is Apple Vision Pro used in hospitals?
Yes. Surgeons at SightMD performed a Vision Pro assisted cataract surgery in 2025, and institutions including Mayo Clinic, Boston Children’s Hospital, Cedars-Sinai, and UC San Diego Health use it for surgical training, rehearsal, and clinical trials.
Where This Goes Next
The consumer verdict on Vision Pro is close to final. An 88% sales collapse, a 95% ad spend cut, and a disputed but credible report of internal reassignment all point the same direction. What isn’t final is what Apple does with the platform underneath it.
Watch three things over the next six to eighteen months: whether WWDC 2026 actually delivers visionOS 27 and new hardware as Gruber predicts, whether IDC’s 290,000 unit rebound forecast for 2026 materializes or misses again, and whether hospital deployments like UC San Diego Health’s clinical trials scale from dozens of cases into standard-of-care infrastructure. Any one of those breaking clearly will settle the argument this article can’t.
IoT Data Overload: Why Most of It Never Gets Analyzed
Published July 8, 2026 · NeuralWired.com
GE Digital’s monitoring center processes more than 200 billion data tags a day from a million sensors across 5,000 power plant assets in 60-plus countries. Most companies running IoT fleets never get close to that ratio of data collected to data used. The gap between what your sensors capture and what your systems actually act on is now the defining bottleneck in enterprise IoT, and closing it has become the real argument for edge computing.
If you’re building or budgeting for an IoT data architecture in 2026, this is the article for you: enterprise architects, CTOs, and platform engineering leads who need to know what’s real in the edge computing narrative, and what’s still marketing.
IDC’s Global DataSphere research puts total IoT-generated data at roughly 79.4 zettabytes annually, a figure the firm hasn’t publicly refreshed for 2026 as of this writing. That’s about 79.4 trillion gigabytes, produced by a device population IoT Analytics counted at 21.1 billion active connections at the end of 2025, up 14% year over year.
You’ve probably seen a version of the claim that “99% of IoT data is never analyzed.” Here’s the problem: no primary 2026 source actually supports that exact number.
Fact-check note: The “only a sliver of your data gets used” claim traces back to IDC’s 2014 Digital Universe study, sponsored by EMC, which found less than 5% of the 2013 global data supply was analyzed, and just 1.5% was what IDC called “target rich,” meaning easily accessible, real-time, and high-impact. That’s the real ancestor of today’s “1%” headline framing. It’s directionally accurate but not a fresh 2026 statistic, and any article that presents it as one is passing along a decade-old number with a new coat of paint.
The underlying point still holds. As sensor costs have fallen from over $200 per unit five years ago to under $50 today, the volume of raw data generated has scaled far faster than most organizations’ capacity to analyze it. The bottleneck isn’t collection anymore. It’s filtering.
As AI’s focus shifts from training to inference, edge computing becomes necessary to address latency and privacy needs, opening business models that centralized infrastructure couldn’t support.
Paraphrased from Dave McCarthy, Research Vice President, Cloud and Edge Services, IDC · R&D World, December 2025
Why edge computing, not more cloud storage
Gartner has projected that 75% of enterprise-generated data would be processed outside a traditional centralized data center or cloud, up from under 10% in 2019. That figure has been circulating for several years now without a fresh confirmation, so treat it as directional rather than a current 2026 data point. But the architectural logic behind it hasn’t gone away: if you’re waiting for a round trip to a centralized cloud region before a factory floor sensor can trigger a shutoff valve, you’ve already lost.
AI increasingly lives closer to where data and users actually are, at the edge, on-device, in the real world, rather than centralized in the cloud.
Paraphrased from John Roese, Chief Technology Officer, Dell Technologies · R&D World, December 2025. Dell sells edge hardware, worth noting as context for this view.
Here’s where it gets messy for anyone trying to size a budget: research firms can’t agree on how big the edge computing market actually is.
Source
2025/2026 estimate
Scope
Global Market Insights
~$21.4 billion
Narrow, infrastructure-only
MarketsandMarkets
~$658.1 billion
Broad, includes adjacent AI/hardware spend
IDC (spending, not market size)
~$261 billion (2025), $380B by 2028
Enterprise edge spending, 13.8% CAGR
That’s a roughly 30x spread between the low and high estimates, entirely a function of what each firm counts as “edge computing.” If a vendor hands you a single market-size number without a methodology footnote, ask for one before it goes anywhere near a board deck.
The pilot purgatory problem
Here’s the number that should worry you more than any data-volume statistic: a widely cited McKinsey finding puts 84% of industrial IoT initiatives stuck in pilot mode, with more than a quarter stalled for over two years. Cisco’s older count put pilot survival at just 26%. Adoption isn’t the hard part. Scaling is.
Most IIoT programs collapse under vague mandates rather than specific, measurable KPIs tied to a real downtime-cost baseline.
Paraphrased from Stephan Liozu, Chief Value Officer, Zilliant; adjunct professor, Case Western Reserve University · IndustryWeek
Liozu has relayed a manufacturing CEO’s description of pilot-stage IIoT projects as stuck somewhere worse than purgatory. And the trend line isn’t improving on its own: MaintainX’s 2025 State of Industrial Maintenance survey found predictive maintenance adoption among maintenance teams actually fell, from 30% in 2024 to 27% in 2025. That’s the opposite of the smooth upward curve most vendor decks imply.
What actually works: GE and Shell’s real numbers
Skip the vendor slide decks. Here are two cases with attributable, on-record numbers.
GE Digital’s Global Electricity Monitoring and Diagnostics Center processes over 200 billion data tags daily from a million sensors across 5,000 power plant assets. Our deeper look at GE and Shell’s real IIoT ROI numbers found GE’s actual documented downtime reduction sits at 5%, notably smaller than the rounder 20% figure that circulates in secondhand retellings. That gap matters: it’s a useful reminder that even legitimate case studies get inflated as they pass through marketing copy.
Shell’s 2019 oilfield vibration and pressure-sensor deployment cost $87,000 and returned over $1 million in avoided downtime and deferred maintenance, according to IoT World Today’s reporting. It remains one of the few IIoT ROI cases with sourcing that holds up to scrutiny, which says something about how rare well-documented ROI actually is in this space.
For a sense of what this looks like outside heavy industry, our recent piece on Amazon’s smart building ROI numbers is worth a look. Same underlying discipline, different vertical.
What ties these together: Both Shell and GE’s real numbers came from narrow, specific use cases with a measurable baseline, not a company-wide “digital transformation” mandate. That’s the pattern that separates the 84% stuck in pilot mode from the ones that scale.
The accuracy gap nobody talks about
Consumer AI applications tolerate roughly 95% accuracy. Nobody’s harmed if a recommendation engine gets it wrong occasionally. Industrial systems don’t get that margin.
Consumer AI applications tolerate roughly 95% accuracy, while industrial models require near-zero error margins, 99.5% or higher, a far higher bar than most AI and edge hype accounts for.
Paraphrased from Jeff Winter, VP of Business Strategy, Critical Manufacturing · remarks at IIoT World Days 2025
That gap is why edge AI’s clearest wins so far cluster in more forgiving domains: retail checkout, consumer cameras, building automation. The highest-stakes industrial control loops, the ones the “operational intelligence” narrative loves to reference, are still catching up. If your architecture roadmap assumes consumer-grade edge AI accuracy translates directly to a chemical plant or a power turbine, that assumption needs a second look before it reaches production.
Unplanned downtime already costs U.S. industrial manufacturers around $50 billion a year, with large plants losing an average of $253 million annually. That’s the cost of inaction, and it’s the number that should anchor any edge computing business case instead of a disputed market-size figure.
Three things worth watching over the next 6 to 18 months:
The EU Cyber Resilience Act. Incident-reporting obligations begin September 11, 2026, with full obligations from December 11, 2027. Edge deployments decentralize where sensitive operational data lives, which directly expands compliance scope for manufacturers and IoT vendors.
Inference, not training, drives edge budget. The strongest current argument for edge investment isn’t raw IoT data volume anymore. It’s the shift from centralized model training to real-time inference at the point of use.
Narrow pilots with a real KPI, not company-wide mandates. Every documented ROI case in this piece started with a specific, measurable problem and a cost baseline, not a broad “digital transformation” initiative.
Our take: the data-volume framing that dominates IoT marketing (zettabytes, “you’re only using 1% of your data”) makes for a good headline but a weak business case. The number that actually gets budget approved is the cost of the downtime you’re not preventing.
FAQ
What is IoT edge processing?
IoT edge processing means analyzing or acting on sensor data close to where it’s generated, on the device or a nearby gateway, instead of sending everything to a centralized cloud data center. It reduces latency, cuts bandwidth costs, and enables real-time decisions even with limited connectivity.
How much data do IoT devices generate?
IDC estimates connected IoT devices generate roughly 79.4 zettabytes of data annually in its most recent published forecast, equivalent to about 79.4 trillion gigabytes. IDC hasn’t publicly updated this figure for 2026 as of this writing.
Why do most IoT projects fail to scale?
A widely cited McKinsey finding shows 84% of industrial IoT initiatives get stuck in pilot mode, often because they launch with vague goals like “improve efficiency” instead of a specific, measurable KPI tied to a real cost baseline.
What percentage of enterprise data is processed at the edge?
Gartner projected 75% of enterprise-generated data would be processed outside a centralized data center or cloud, up from under 10% in 2019. The figure is several years old now and should be treated as directional rather than a current data point.
How big is the edge computing market?
Estimates vary enormously: 2025 to 2026 figures range from roughly $21 billion (Global Market Insights) to over $650 billion (MarketsandMarkets), depending entirely on what’s counted as “edge computing.” Treat any single figure with caution unless the methodology is disclosed.
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Deloitte’s AI Hallucination Cost $290K. FINRA Is WatchingEnterprise AI / Compliance
Deloitte’s AI Hallucination Cost $290K. FINRA Is Watching
The Neural Loop | NeuralWired.com
In October 2025, Deloitte admitted it used generative AI to help write a government compliance report, then had to refund part of the fee after the report turned out to be full of fake citations. If you work in finance, compliance, or risk, that sentence should stop you cold. AI hallucination in finance is no longer a theoretical risk buried in a research paper. It’s now a line item in a regulator’s oversight report, a refunded invoice, and a pattern repeating across the professional services firms that finance departments hire to be right.
This piece breaks down what actually happened, what’s verified versus vendor hype, what FINRA’s new 2026 guidance means for your compliance calendar, and what to do about it before your firm becomes the next case study.
In late 2025, Australia’s Department of Employment and Workplace Relations paid Deloitte’s Australian arm roughly AU$440,000 (about US$290,000) to review the Targeted Compliance Framework, the IT system that penalizes welfare recipients who miss job-search requirements. The 237-page report went up on the department’s website in July.
Then Dr. Chris Rudge, a University of Sydney researcher in health and welfare law, started reading it closely.
“You cannot trust the recommendations when the very foundation of the report is built on a flawed, originally undisclosed, and non-expert methodology.”
Dr. Chris Rudge, Researcher in Health and Welfare Law, University of Sydney, via Australian Financial Review
Rudge found invented academic references attributed to real scholars, including Lisa Burton Crawford at the University of Sydney and Björn Regnell at Lund University, neither of whom wrote what the report claimed they wrote. The report also included a fabricated quote it attributed to a Federal Court judgment in the Amato v Commonwealth robo-debt case, misspelling the name of the judge it invented the quote for.
Deloitte confirmed the errors. In the corrected version, published in October 2025, it disclosed for the first time that it had used an Azure OpenAI GPT-4o based tool chain, licensed by the department itself, to fill what it called “traceability and documentation gaps.” Deloitte agreed to repay the final installment of the contract. The exact refund figure was never disclosed publicly, and the department maintained the report’s underlying recommendations still stood.
Jack Castonguay, an accounting professor at Hofstra University, put it bluntly:
“It seems like it was only a matter of time. Candidly, I’m surprised it took this long for it to happen at one of the firms.”
Jack Castonguay, Associate Professor of Accounting, Hofstra University, via CFO Dive
What makes this a trend rather than a one-off is what happened next, at other firms whose entire business model rests on being trusted to get facts right.
Firm
What went wrong
Outcome
Deloitte Australia
Fabricated citations, a fake quote from a court judgment, undisclosed AI use in a government compliance report
Refunded final contract installment, corrected report reissued
EY Canada
Most citations in a loyalty-program safeguards report were hallucinated, including a nonexistent McKinsey citation, per an investigation by AI-detection firm GPTZero
Study withdrawn, per Financial Times reporting
Sullivan & Cromwell
AI-assisted court filing contained inaccurate citations and misquoted the U.S. Bankruptcy Code
Firm apologized to the New York court
Three incidents, three firms, roughly a twelve-month window. None of these are consumer chatbot slip-ups. These are paid deliverables from firms whose entire pitch is analytical rigor.
Regulators Just Made This a Compliance Issue
On December 9, 2025, FINRA published its 2026 Annual Regulatory Oversight Report and, for the first time, gave generative AI its own dedicated section. The report names hallucinations and bias explicitly as risks firms must manage, and it tells firms weighing AI agent deployment to evaluate whether that autonomy creates new supervisory or operational obligations.
FINRA also pushed firms to build testing and monitoring specifically around GenAI accuracy, integrity, and reliability, including ongoing output logging and model tracking rather than a one-time compliance check.
Important nuance: FINRA’s report doesn’t create new binding rules. It signals what examiners will prioritize throughout 2026. Treat it as a preview of what your next exam will ask for, not a law you’re already breaking.
Meanwhile, the EU AI Act’s transparency requirements for high-risk systems take effect on August 2, 2026, with penalties running up to €35 million or 7% of global annual turnover for noncompliance. That one is a hard deadline, not guidance. Full text and compliance timelines are available directly from FINRA’s 2026 Annual Regulatory Oversight Report.
Why This Can’t Just Be Engineered Away
Here’s the part vendors selling “hallucination-free” AI tools would rather you not read closely. In 2024, researchers Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli formally proved, using computational learning theory, that hallucination cannot be fully eliminated from large language models. Their argument: since the formal systems they modeled are a simplified subset of the real world, and hallucination is unavoidable even in that simplified case, it’s unavoidable in the messier real world too.
A separate 2024 paper reached the same conclusion by a different route entirely, tying the problem to the mathematical structure of LLMs themselves and, notably, to Gödel’s First Incompleteness Theorem. Two independent proofs, two different mathematical toolkits, same conclusion. That convergence matters. It means the realistic goal for any finance or compliance team isn’t zero hallucination. It’s traceability: knowing exactly where a given AI output came from and who checked it before it went out the door.
The Numbers Finance Leaders Should Actually Trust
A lot of dramatic statistics circulate around this topic, including some that trace back to vendor blogs rather than verifiable research. Here’s what’s actually sourced and defensible.
Metric
Figure
Source
Hallucination rate on complex financial reasoning tasks
10 to 20%
FAITH framework academic benchmark
Model accuracy on simple lookups vs. multivariate calculations
95.6% down to near 0%
FAITH / FinVerBench benchmark
Enterprises with production RAG systems that had a hallucination incident in the past year
67%
Gartner survey
Firms saying guardrails gave a false sense of security
41%
Gartner survey
Average cost per RAG-misinformation incident, regulated industries
$2.4 million
IDC, March 2026
Global AI governance platform spend
$492 million in 2026, over $1 billion by 2030
Gartner newsroom, Feb 2026
That last figure comes straight from a primary source. Gartner Director Analyst Lauren Kornutick noted that fragmented AI regulation is expected to quadruple by 2030 and extend to roughly 75% of the world’s economies, which is a meaningful part of why governance spend is climbing so fast. See the full Gartner release for the underlying methodology.
Worth naming directly: figures like “$2.3 billion in Q1 2026 trading losses from AI-misstated earnings” and specific hedge fund loss numbers you may have seen elsewhere trace back to vendor marketing content, not independently verifiable reporting. Treat single-source vendor statistics with the same skepticism you’d apply to an unverified AI output. That’s not a throwaway line either. It’s the whole point of this article.
The Uncomfortable Counterpoint
The industry’s default fix for hallucination is retrieval-augmented generation, RAG for short: ground the model’s answers in your own verified documents instead of letting it generate from memory alone. It helps. Commonly cited reductions run 40 to 71% depending on implementation quality.
But Gartner’s 67% figure above is the uncomfortable part. Most enterprises running production RAG systems still had at least one hallucination incident last year. Nearly half said their guardrails created false confidence rather than actual protection. RAG narrows the problem. It doesn’t close it, and given the formal proofs discussed above, it structurally can’t.
Nikki MacKenzie, an assistant professor at Georgia Tech’s Scheller College of Business, frames the real fix as procedural, not technical:
“The responsibility still sits with the professional using it. Accountants have to own the work, check the output, and apply their judgment rather than copy and paste whatever the system produces.”
Nikki MacKenzie, Assistant Professor, Georgia Institute of Technology’s Scheller College of Business, via CFO Dive
Our read: the firms getting burned aren’t the ones using AI. They’re the ones treating AI output as a finished product instead of a first draft that still needs a human signature.
What Finance and Compliance Teams Should Do Now
Build source traceability into every AI-assisted workflow. Every claim, number, or citation generated with AI assistance needs a documented, checkable origin before it leaves the building.
Treat FINRA’s 2026 report as your exam prep, not optional reading. Expect examiners to ask for model risk management documentation and testing logs for GenAI tools specifically.
Map your EU exposure now, not in July 2026. If any part of your operation touches EU customers or markets, the August 2, 2026 high-risk transparency deadline applies regardless of where you’re headquartered.
Stop chasing the lowest hallucination-rate benchmark. Bryan Lapidus, FP&A Practice Director at the Association for Financial Professionals, summed up the mindset shift finance teams need:
“This situation underscores a critical lesson for finance professionals: AI isn’t a truth-teller. It’s a tool meant to provide answers that fit your questions.”
Bryan Lapidus, FP&A Practice Director, Association for Financial Professionals, via CFO Dive
Require human sign-off on anything client-facing or regulator-facing. Deloitte’s internal analytical workflow became a public problem the moment it was published. Assume the same could happen to yours.
Frequently Asked Questions
What is an AI hallucination?
An AI hallucination is fluent, confident-sounding output from a language model that is factually wrong or entirely fabricated, including invented statistics, citations, quotes, or case law that don’t actually exist.
Can AI hallucinations be eliminated?
No. Researchers Xu, Jain, and Kankanhalli formally proved elimination is mathematically impossible in 2024, and a separate paper reached the same conclusion using Gödel’s incompleteness theorem. Mitigation and traceability, not elimination, are the realistic goals.
How much do AI hallucinations cost businesses?
Costs vary by domain and are hard to verify precisely. IDC estimates $2.4 million per RAG-misinformation incident in regulated industries. The clearest verified real-world example remains Deloitte Australia’s partial refund of its AU$440,000 government report.
Does RAG stop AI hallucinations?
RAG reduces hallucinations, commonly by 40 to 71%, but doesn’t eliminate them. A 2026 Gartner survey found 67% of enterprises running production RAG systems still had at least one hallucination incident in the past year.
What did FINRA say about AI hallucinations in 2026?
FINRA’s 2026 Annual Regulatory Oversight Report, published December 9, 2025, added a dedicated GenAI section naming hallucinations and bias as risks firms must test for and govern. It signals 2026 examination priorities rather than creating new binding rules.
Where This Goes From Here
Eighteen months ago, AI hallucination was a chatbot-demo curiosity, a wrong answer about the James Webb telescope, an airline chatbot promising a refund policy that didn’t exist. Now it’s a named risk category in a financial regulator’s annual report and a documented reason a Big Four firm refunded a national government.
Watch three things over the next six to eighteen months: how FINRA’s 2026 examinations actually treat GenAI documentation in practice, whether the EU AI Act’s August enforcement date produces real penalties or mostly warnings, and whether the guardrails market, on track to grow from under $1 billion to over $100 billion by 2034, actually reduces incident rates or just gets better at making firms feel safer than they are.
The lesson from Deloitte, EY, and Sullivan & Cromwell isn’t that AI is too risky to use. It’s that treating AI output as finished work, instead of a draft that needs a human name attached to it, is what actually gets expensive.
Shell turned an $87,000 sensor bet into more than $1 million in returns. Most industrial IoT projects never get that far. Here’s the actual math behind the ones that do.
In 2019, Shell wired up an aging oilfield with $87,000 worth of vibration and pressure sensors. The payout: over $1 million in avoided downtime and deferred maintenance, a return that would make any CFO sit up. That story gets quoted constantly in industrial IoT (IIoT) marketing decks. What doesn’t get quoted nearly as often: 84% of IoT projects never make it past the pilot stage, and more than a quarter of those stay stuck there for over two years.
If you’re a plant manager weighing a predictive maintenance rollout, both of those facts matter. This piece walks through what unplanned downtime actually costs, why so many IIoT projects stall before they scale, and how to build an ROI case that survives contact with a skeptical board.
Start with the number that justifies everything else: unplanned downtime costs U.S. industrial manufacturers roughly $50 billion a year. The average large plant loses about $253 million annually to breakdowns. For Fortune 500 manufacturers, that works out to $2.8 billion a year, close to 11% of revenue, vanishing into machines that stopped when they weren’t supposed to.
It’s not a rare event, either. 82% of manufacturers reported unplanned downtime in the past three years, and the average factory loses roughly 800 hours a year to breakdowns that, in theory, sensors could have flagged in advance.
Here’s where the hardware math has flipped in the plant manager’s favor. Basic industrial IoT sensors now run under $50 a unit, down from over $200 five years ago. A North American plant runs an average of 365 IIoT devices today. Cost is no longer the bottleneck it was in 2019. That changes the calculus: a small, targeted pilot on two or three critical machines is now cheap enough to fund out of an existing maintenance budget, which means you don’t need to wait for a capex approval cycle to test the idea.
Why Most IIoT Projects Never Scale
So if the hardware is cheap and the downtime math is brutal, why isn’t every plant running on sensors already?
Because most projects stall. A McKinsey study, still widely cited years later, found 84% of industrial IoT initiatives get stuck in what the industry now calls “pilot purgatory,” and 28% of those stay there for more than two years. More recent tallies put the failure-to-scale rate above 60%, and Cisco’s older but still-referenced figure puts pilot survival at just 26%.
“It is not purgatory, it is hell!”
Manufacturing CEO, quoted by Stephan Liozu, Chief Value Officer at Zilliant and adjunct professor at Case Western Reserve University’s Weatherhead School of Management, via IndustryWeek
Liozu isn’t a fringe voice here. His point, backed by McKinsey’s own data, is that most IIoT programs collapse under the weight of vague mandates like “improve efficiency” instead of a specific, measurable KPI tied to a named line and a real downtime-cost baseline.
Adoption data backs up the stall-out pattern. According to MaintainX’s 2025 State of Industrial Maintenance survey, predictive maintenance adoption among maintenance teams actually dropped, from 30% in 2024 to 27% in 2025. That’s not the smooth upward curve you’d expect from the marketing. Only 46% of manufacturers have deployed IIoT at the facility level at all, and per McKinsey’s more recent numbers, just 25 to 30% of large manufacturers have scaled beyond a pilot to an enterprise-wide rollout.
Watch this number: Global IIoT market-size estimates for 2026 range from roughly $190 billion (Mordor Intelligence) to over $500 billion (Precedence Research), depending entirely on how each firm defines the market’s scope. When a vendor pitches you urgency based on “the $500 billion IIoT market,” ask which definition they’re using. The spread alone should make you skeptical of any single headline figure used to justify a purchase decision.
Jeff Winter, VP of Business Strategy at Critical Manufacturing, put the technical side of the problem bluntly at IIoT World Days 2025: consumer AI succeeds at around 95% accuracy, but industrial models need a near-zero margin for error, 99.5% or higher. That’s a much higher bar than most of the AI hype cycle accounts for, and it’s a big part of why pilots that look great in a demo fall apart on a real production line.
What Actually Works: Two Real Cases
Strip away the invented statistics you’ll find floating around IIoT marketing content (there’s no verified source for some of the dollar figures companies get credited with online, so treat any suspiciously precise claim with caution) and two real, attributable cases hold up.
Shell: $87,000 In, $1 Million Out
Shell’s oilfield asset-monitoring project is the cleanest real-world proof point available. An $87,000 sensor investment on aging equipment generated over $1 million in returns through avoided downtime and deferred maintenance, according to IoT World Today’s reporting. It’s not a hypothetical ROI model. It’s a documented result from a company that had every incentive to keep quiet if the numbers hadn’t worked out.
GE: The Real Numbers Behind the Headline
GE Digital’s Global Electricity Monitoring and Diagnostics Center processes over 200 billion data tags daily from a million sensors across 5,000 assets in power plants in more than 60 countries. Bill Ruh, then-CEO of GE Digital, put the actual, on-record result this way when the platform launched:
“These analytics provide GE Digital with the unique ability to reduce unplanned downtime by up to 5 percent, reduce false alarms by up to 75 percent, and reduce operations and maintenance costs by up to 25 percent.”
Bill Ruh, then-CEO, GE Digital, GE News press release, October 2017
Notice that’s 5%, not the round 20% figure that circulates in some secondhand summaries. It’s a smaller number, but it’s GE’s own, and it comes with a false-alarm reduction and O&M cost figure attached that most retellings leave out entirely.
Predictive Maintenance: The Realistic Range
Zooming out from single-company cases, here’s where independent research firms land on predictive maintenance’s actual impact:
Metric
Range
Source
Maintenance cost reduction
Up to 25%
Deloitte
Uptime increase
10% to 20%
Deloitte
Downtime reduction (upper bound)
Up to 50%
McKinsey & Co.
Asset lifespan extension (upper bound)
Up to 40%
McKinsey & Co.
Treat the upper-bound figures as ceiling cases, not defaults. A well-scoped, single-line pilot is far more likely to land near the lower end of these ranges in its first year.
Building an ROI Case That Survives the Board
Here’s the actual formula, stripped of vendor gloss: ROI equals annual savings minus annual program cost, divided by annual program cost, times 100. Annual savings breaks down into avoided downtime cost plus avoided emergency maintenance spend, measured against a pre-deployment baseline you establish before you install a single sensor.
A few things the data says you need to get right:
Use your own downtime-cost-per-hour, not an industry average. The $50 billion industry figure is a scale-setter, not an input for your specific calculator.
Stress-test your assumptions by plus or minus 30% before presenting to a board. This is standard practice recommended in IoT World’s own project-scoping framework, and it heads off the “your numbers were too optimistic” objection before it happens.
Budget for realistic retrofit costs. End-to-end IIoT retrofits run anywhere from $1 million to over $10 million depending on facility size and complexity, per Emergen Research. A sub-$50,000 sensor pilot is a very different financial commitment than a plant-wide rollout, and conflating the two in a pitch deck is a fast way to lose credibility.
Set a payback-period expectation that matches your scope. Well-scoped single-line predictive maintenance pilots often show payback in 6 to 18 months. Enterprise-wide rollouts typically take two years or more, and that’s exactly the scope where the pilot-purgatory failure rate climbs.
Our read: the smartest move for most plant managers right now isn’t the big enterprise-wide business case. It’s the small, self-funded pilot on one or two lines, with a hard KPI and a real downtime-cost baseline attached, that proves the model before anyone has to ask a board for millions.
Frequently Asked Questions
How do you calculate ROI for an IIoT project?
ROI equals annual savings minus annual program cost, divided by annual program cost, times 100. Savings combine avoided downtime cost and avoided emergency maintenance spend, measured against a pre-deployment baseline established before installation begins.
How much does unplanned downtime cost manufacturers?
Unplanned downtime costs U.S. industrial manufacturers roughly $50 billion annually. The average large plant loses about $253 million a year, and Fortune 500 manufacturers lose around $2.8 billion annually, close to 11% of revenue.
What percentage of IoT projects fail to scale?
Estimates vary, but a widely cited McKinsey finding shows 84% of industrial IoT projects remain stuck in pilot mode, with more than a quarter stalled for over two years, a pattern the industry calls “pilot purgatory.”
How much can predictive maintenance reduce downtime?
Deloitte and McKinsey benchmarks put predictive-maintenance-driven downtime reduction between 25% and 50%, with maintenance cost savings of 10% to 25%. Results vary heavily by industry, equipment type, and deployment maturity.
What’s a realistic payback period for an industrial IoT project?
Well-scoped, single-line predictive maintenance pilots often show payback within 6 to 18 months. Enterprise-wide rollouts typically take two years or longer, and a meaningful share stall out indefinitely before reaching that point.
What to Watch Next
Three things worth tracking over the next 6 to 18 months: whether predictive maintenance adoption recovers from its 2025 dip or keeps sliding, whether sub-$50 sensor pricing pulls more mid-size manufacturers past the 46% facility-level adoption mark, and whether the gap between market-size hype and the 25 to 30% enterprise-scaling rate starts to close.
None of that changes the math you need today. Get your own downtime-cost baseline, run the small pilot, stress-test the assumptions, and let the numbers, not the vendor slide deck, make the case.
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AI Copyright Rulings Diverge Across 6 Countries in 2026
GLOBAL AI POLICY
Your AI Content Runs on Copyrighted Data. Six Countries Now Disagree on What That Means.
By NeuralWired Research Desk | July 6, 2026
A judge in San Francisco says training an AI model on copyrighted books is fair use. A judge in Munich just ruled the opposite about song lyrics. If your company runs the same generative AI tool in both markets, you are not operating under one set of rules. You are operating under six, and they contradict each other in ways that should worry your legal team more than any single lawsuit does.
This is the real story of AI copyright risk in 2026: not one landmark case, but a fractured global map where the United States, the UK, Germany, China, India, and Canada have each drawn their own line, sometimes in direct opposition to one another. Here is what each country has actually decided, what it means if you build or publish with AI, and where the next ruling could flip the board again.
United States: A Win on Training, a $1.5 Billion Loss on Piracy
Start with the case that set the tone for everything after it. In Bartz v. Anthropic, Judge William Alsup ruled on June 23, 2025 that training Claude on legally acquired books was, in his words, “exceedingly transformative,” comparing it to a human reading widely to learn how to write. That part was a clear win for AI developers.
But Alsup drew a sharp line: acquiring and storing roughly 7 million pirated books to build that training set was not fair use, piracy is piracy, no matter what you do with the files afterward. Anthropic settled for $1.5 billion, covering about 482,000 works at an implied rate of roughly $3,113 per work. Final court approval is set for April 23, 2026. That figure now works as the industry’s first real benchmark for what unauthorized training data can cost.
Two weeks later, Kadrey v. Meta reached a similar outcome on training but split from Alsup’s reasoning entirely. That court found training is fair use “regardless of whether the underlying materials were obtained from legitimate sources or not,” while flagging market dilution as a stronger, if still unproven, theory of harm. Some of Meta’s torrenting claims remain active.
Not every U.S. ruling has gone the AI industry’s way. In Thomson Reuters v. Ross Intelligence, the District of Delaware ruled in February 2025 that training a non-generative legal search tool on Westlaw headnotes was not fair use, the only U.S. loss so far at the training stage. The Third Circuit heard oral argument on the appeal June 11, 2026, and legal analysts expect that ruling to shape every pending generative AI case regardless of the outcome.
Meanwhile, the NYT/Authors Guild v. OpenAI multidistrict litigation is quietly becoming the biggest discovery event in copyright history. Judge Sidney Stein ordered OpenAI to hand over 20 million anonymized ChatGPT logs in January 2026, then expanded that order in March to cover pools of 78 million and 10 million more. If your team pipes proprietary prompts through a vendor’s model, that is now a data governance question, not just a legal one.
“I personally think that training your gen AI model on copyrighted works is fair use, ought to be fair use.”
Jessica Litman, Law Professor, University of Michigan | Source: Generative AI in the Newsroom
The U.S. Supreme Court closed one door entirely on March 2, 2026, denying certiorari in Thaler v. Perlmutter and leaving intact the rule that copyright requires a human author. As of mid-2026, more than 70 active or recently resolved AI copyright suits are working through U.S. courts, with cumulative claimed damages estimated above $50 billion.
United Kingdom: A Hollow Victory for Rights Holders
Getty Images spent years and millions of dollars suing Stability AI over Stable Diffusion. The High Court’s November 2025 ruling in Getty Images v. Stability AI rejected Getty’s secondary infringement claim outright, finding that model weights are not “infringing copies” under UK law because the model does not retain reproductions of the training images themselves.
Getty won only a narrow trademark claim tied to watermarks appearing in early model outputs. Then came the twist: Getty was ordered to pay 69.4% of Stability’s legal costs. Commentators have called it a financially pyrrhic result, a technical win that cost the plaintiff more than it recovered.
Why it matters: The UK court never actually ruled on whether training itself infringes copyright, because Getty withdrew that claim mid trial. That leaves the UK’s core question on AI training completely untested, a gap enterprises operating there should not mistake for settled law.
Germany: The Only Country to Rule Against an AI Company Outright
If the U.S. story is “training wins, piracy loses,” Germany flips that on its head. The Munich Regional Court ruled on November 11, 2025 in GEMA v. OpenAI that OpenAI violated German copyright law by using lyrics from GEMA’s more than 100,000 represented composers and publishers to both train and generate output through ChatGPT. This is the first European court finding of direct copyright liability against a major generative AI company.
GEMA CEO Tobias Holzmüller described the ruling as a precedent that protects authors’ rights against unauthorized AI training and output.
Source: Built In / The Conversation
Separately, three 2026 German rulings from Munich, Frankfurt, and Düsseldorf courts tackled a different question entirely: can AI-generated output itself be copyrighted? Applying the CJEU’s “free and creative choices” standard, the courts found that short or purely functional prompts do not create a protectable work, but human-authored lyrics later modified by AI retained protection because the human’s original creative choices were still recognizable in the final product.
Put simply, Germany is currently the only major jurisdiction to find an AI company directly liable for both training and output infringement, the opposite conclusion the U.S. and UK reached in the same 18 month window.
China: Three Rulings, Three Different Answers
China’s courts have handed down more AI copyright decisions than any other jurisdiction, and they do not agree with each other. In the Guangzhou Internet Court’s 2024 “Ultraman” case, an unnamed LoRA model platform was ordered to pay roughly $4,200 after users generated Ultraman derivative images for commercial gain, the first Chinese ruling to hold an AI platform liable for contributory infringement. The Hangzhou Intermediate People’s Court upheld it on appeal in December 2024.
Then, in September 2025, the Beijing Internet Court ruled in the “Cat Crystal Diamond Pendant” case that copyright can exist in AI-generated images, but only if the claimant documents their actual creative process, prompts, and iterative edits. Post-hoc recreations of the generation process do not count.
Six months earlier, the Zhangjiagang People’s Court went the other direction entirely. In the “Fantasy Wings” case, decided March 19, 2025 and upheld on appeal that April, the court found that prompts alone convey ideas rather than protectable expression, and that unpredictable model output cannot be attributed to the user as their own work, the first Chinese decision to deny AI-generated images copyright protection altogether.
The takeaway: China is not converging toward a single rule. It is producing case-by-case outcomes that hinge entirely on how well a claimant documented their prompting process, which makes prompt logging a genuine legal asset there, not just good practice.
India: The Next Big Swing Vote
India has not issued a merits ruling yet, but the case to watch, ANI Media v. OpenAI, filed in the Delhi High Court in November 2024, could move fast. Judgment was reserved on March 27, 2026, and Justice Bansal has signaled he intends to rule under India’s 1957 Copyright Act without leaning on foreign precedent. That means the outcome could break sharply from the “transformative use” framework the U.S. and UK have relied on.
OpenAI has argued the Delhi court lacks jurisdiction and pointed out that no interim injunction has been granted against it in comparable U.S., Canadian, or German cases. Times of India publisher Bennett Coleman has joined the suit, raising the stakes for a ruling that could set India’s first real doctrine on AI training.
Canada: Cases Move Forward, Questions Stay Open
Canada’s courts have so far only cleared procedural hurdles, not ruled on the substance. The Ontario Superior Court in December 2025 and the Federal Court of Appeal in July 2025 both allowed Canadian AI-training copyright claims to proceed past jurisdictional challenges. A second Quebec class action against Meta over LLaMA training was filed in October 2025.
Canada’s Copyright Act, last substantively updated in 2012, has never been tested on fair-dealing-for-AI-training on the merits. Government consultations wrapped in November 2024 and could still prompt legislative changes before any court gets the chance to rule.
The Global Ruling Matrix
Here is where each jurisdiction actually stands right now, at a glance.
Country
Training on Copyrighted Data
AI-Generated Output Ownership
Status
United States
Fair use if legally acquired; piracy is not covered
Requires human author (Thaler)
Ross Intelligence appeal pending, June 2026
United Kingdom
Untested; Getty withdrew the claim
Not directly ruled on
Narrow trademark ruling only
Germany
Ruled infringing (GEMA)
Protectable only if human creative choices are recognizable
Only jurisdiction with direct liability finding
China
Platform liability found (Ultraman)
Case-by-case; depends on documented creative process
Inconsistent, most active docket globally
India
Undecided
Undecided
Judgment reserved, ANI v. OpenAI
Canada
Undecided on merits
Undecided
Cases cleared jurisdictional stage only
What This Means If You Publish or Build With AI
If your company operates across more than one of these markets, and most sizable enterprises do, a single global AI use policy no longer covers you. Here’s what actually needs to change.
Jurisdiction-by-jurisdiction risk scoring, not a blanket policy
The same generative AI deployment can be compliant in the U.S., directly exposed in Germany, and legally undefined in India. Legal teams need a market-by-market risk matrix, not a single global sign-off.
Treat training and output as two separate legal questions
Nearly every ruling so far, American, German, and Chinese, treats where the training data came from and whether the output resembles existing copyrighted work as entirely separate issues with separate defenses. Audit both independently.
Log your prompts and iterations
Chinese courts explicitly require documented evidence of creative process to claim copyright in AI-assisted output, and German courts apply a similar test. Prompt and revision logs are becoming a legal asset, not just a workflow nicety.
Push for real indemnification in vendor contracts
With a public settlement benchmark now on the record ($3,113 per work in Bartz), legal teams finally have a number to model worst-case exposure against when negotiating indemnification caps with AI vendors.
“The actual practices that the AI companies are engaging in probably go beyond what would be considered a fair use.”
Nina Brown, Attorney and Communications Professor, Syracuse University | Source: Generative AI in the Newsroom
Watch two decisions closely for the rest of 2026: the Third Circuit’s ruling on the Ross Intelligence appeal, and the Delhi High Court’s judgment in ANI v. OpenAI. Either could shift the fragmented map covered above in a matter of weeks.
The Case Against the “AI Is Winning” Narrative
Most coverage of these cases reads like the AI industry is running the table. That’s not quite right. Of the handful of merits rulings issued so far, only Bartz and Kadrey squarely favor AI companies on training, and even those carved out real losses on piracy and acquisition. Germany’s GEMA ruling is a direct counterexample from the same 18 month window, a fact that rarely makes it into U.S.-centric coverage.
The Ross Intelligence loss also remains the outlier working against AI companies, and it is now in front of an appellate court for the first time. Northwestern’s Peter DiCola has cautioned that neither Ross nor the Meta outcome should be treated as decisive, since both are still pending appellate review.
A risk most coverage misses entirely: if courts keep ruling that AI-generated content is broadly uncopyrightable, as Dennis Crouch of the University of Missouri put it, we may be “rapidly building a vast new public domain.” That cuts both ways. Companies pouring money into AI-produced marketing, media, or code face the real possibility that their own outputs carry zero protectable IP, a mirror image of the infringement risk that dominates most headlines.
“If we’re not going to hold the technology maker liable for the technology itself, then the creator of the output is the AI. But we’ve already said they’re not an author. So if they’re not an author then they can’t create an infringing work.”
Rob Heverly, Associate Professor, Albany Law School | Source: Built In
China’s doctrine is not converging with the West either, despite some analyst framing suggesting otherwise. Within an 18 month span, Chinese courts issued a landmark pro-copyrightability ruling, then a flat denial, then a stricter evidentiary bar. That is not a stable rule enterprises can build long-term compliance around, it is a moving target.
FAQ
Is AI-generated content protected by copyright?
In most jurisdictions, no. U.S., German, and Indian law all currently require a human author, and the U.S. Supreme Court declined to revisit that rule in March 2026. China is the exception, allowing copyright where a claimant documents sufficient human creative input through prompting and iteration.
Is it legal to train AI on copyrighted books?
In the U.S., courts have ruled that training on legally acquired copyrighted books is fair use, as in Bartz v. Anthropic and Kadrey v. Meta, but downloading pirated copies is not. Germany’s GEMA ruling reached the opposite conclusion for song lyrics used to train ChatGPT.
Did Anthropic lose its AI copyright lawsuit?
Anthropic won on the core training question, a judge ruled that using books to train Claude was fair use, but lost on the separate question of acquiring pirated copies. That led to a $1.5 billion settlement covering roughly 482,000 works, pending final court approval on April 23, 2026.
Can you copyright something made with Midjourney or ChatGPT?
Only the human-authored elements. U.S. and German courts require the human’s own creative choices to be identifiable in the final work. Simple or purely descriptive prompts generally don’t qualify, though extensively edited or combined human-AI works may retain partial protection.
What is the AI copyright case in India?
ANI Media v. OpenAI, filed in the Delhi High Court in November 2024, alleges OpenAI used ANI’s news content without permission to train ChatGPT. Judgment was reserved on March 27, 2026, and the ruling is expected to set India’s first legal doctrine on AI training and copyright.
Where This Leaves Us
Six countries, six different answers, and none of them final. What you now understand that most coverage glosses over: the U.S. and Germany have both ruled on the training question and landed in opposite places, the UK never actually decided it, China is producing contradictory rulings by the month, and India and Canada haven’t reached the merits yet at all.
Over the next 6 to 18 months, expect the Third Circuit’s Ross Intelligence appeal and the Delhi High Court’s ANI v. OpenAI judgment to be the two rulings that move this map the most. Three things worth watching in the meantime: whether the Bartz settlement’s $3,113-per-work benchmark shows up in future case valuations, whether any jurisdiction outside China starts requiring documented prompt logs as evidence of human authorship, and whether the EU AI Act’s training-data-transparency rules start generating their own wave of litigation separate from copyright court.
Anyone telling you there’s a stable global compliance standard coming in 2026 hasn’t read the actual rulings. There isn’t one. Plan accordingly.
