IBM, Google Miss Quantum Deadlines as Commercial Value Hits Zero

Sabine Hossenfelder

Five years of broken corporate promises show noisy quantum hardware will produce zero commercial value this decade as classical AI matches target benchmarks.

AI-assisted summary of Quantum Computing Failure Now Obvious — original source. Check the original for full context.

Source published: 2026-08-25

Key ideas

Repeated claims of quantum advantage continue to rely on arbitrary tasks like random circuit sampling with zero real-world utility, while nearly all other milestones have been reproduced on classical computers within roughly 18 months. (0:00)
Every major timeline for commercial advantage from Noisy Intermediate-Scale Quantum (NISQ) devices—set by Google, IBM, IonQ, and Kipu Quantum between 2017 and 2023—has officially elapsed without delivering real-world utility. (1:48)
The Boston Consulting Group's 2019 projection of $2 billion to $5 billion in end-user commercial value for targeted quantum sectors by 2024 has yielded virtually zero actual value. (1:48)
Advances in classical AI capable of modeling physical quantum regularities, highlighted by DeepMind's Demis Hassabis, further reduce the commercial necessity of noisy quantum hardware. (4:01)

Detailed notes

## Background This video was published on YouTube by Sabine Hossenfelder on August 25, 2026. The description highlights how major tech companies like Google, IBM, and IonQ repeatedly promised that commercial payoffs from small, noisy quantum computers were just around the corner, yet failed to deliver on their target dates despite ongoing industry optimism.

## TL;DR Sabine Hossenfelder examines the growing disparity between soaring quantum computing hype and the reality of missed commercial milestones. She details how recent demonstrations of 'quantum advantage' rely on arbitrary benchmark tasks like random circuit sampling with zero practical utility, which are often reproduced on classical computers within 18 months. Furthermore, predictions from tech giants and consulting firms promising lucrative business value during the NISQ (Noisy Intermediate-Scale Quantum) era have yielded virtually nothing. She concludes that while quantum computing remains valuable for basic science, viable commercial use cases will not appear this decade, especially as classical artificial intelligence increasingly models complex quantum systems directly.

## Atmosphere & style The video carries Sabine Hossenfelder's signature dry, skeptical, and razor-sharp debunking style, slicing through corporate PR buzzwords with blunt candor. Watching it feels like a refreshing, no-nonsense reality check, shifting from deadpan critiques of tech consultancy predictions to methodical technical scrutiny. The mood is pragmatically analytical yet biting, characterized by pointed quips that underscore the absurd gap between marketing claims and actual scientific delivery.

## Bulletin - [00:00](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=0s) Repeated claims of quantum advantage continue to rely on arbitrary tasks like random circuit sampling with zero real-world utility, while nearly all other milestones have been reproduced on classical computers within roughly 18 months. - [01:48](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=108s) Every major timeline for commercial advantage from Noisy Intermediate-Scale Quantum (NISQ) devices—set by Google, IBM, IonQ, and Kipu Quantum between 2017 and 2023—has officially elapsed without delivering real-world utility. - [01:48](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=108s) The Boston Consulting Group's 2019 projection of $2 billion to $5 billion in end-user commercial value for targeted quantum sectors by 2024 has yielded virtually zero actual value. - [04:01](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=241s) Advances in classical AI capable of modeling physical quantum regularities, highlighted by DeepMind's Demis Hassabis, further reduce the commercial necessity of noisy quantum hardware.

## Key takeaways - [00:00](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=0s) Despite a track record of consistently missed deadlines, quantum computing companies and insiders display unprecedented optimism. - [00:00](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=0s) Recent quantum advantage demonstrations, such as the one from IBM and the University of Chicago, rely on benchmark tasks like random distribution generation that lack any practical utility. - [00:00](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=0s) Philosopher of physics Amit Hagar observes that almost all claimed quantum advantages outside random circuit sampling have been reproduced classically within 18 months. - [00:00](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=0s) Hagar argues that press cycles conflate basic diagnostic engineering with computational advantage, primarily serving to attract research funding. - [01:48](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=108s) Industry claims dating back to 2017 promised that commercial payoffs would emerge within five years using small, noisy NISQ hardware. - [01:48](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=108s) Predictions from Google, IBM, IonQ, and Kipu Quantum concerning near-term commercial NISQ advantages have all missed their projected target windows. - [01:48](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=108s) Consulting forecasts, including Boston Consulting Group's estimate of a multi-billion-dollar end-user market by 2024, failed to materialize into any measurable value. - [04:01](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=241s) John Preskill, who coined the term NISQ, never claimed that noisy devices would yield immediate commercial payoffs, contrasting with subsequent corporate hype. - [04:01](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=241s) Theoretical computer scientists like Scott Aaronson have long urged caution, separating the foundational value of quantum computing from commercial overreach. - [04:01](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=241s) Viable commercial applications for quantum computers are unlikely to arrive within this decade and will likely remain restricted to niche scientific problems rather than sparking broad industrial revolutions. - [04:01](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=241s) Demis Hassabis notes that classical AI models can exploit structural regularities in physical quantum systems, challenging the need for dedicated quantum hardware in many applications.

## Detailed walkthrough ### [00:00](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=0s) The Mirage of Quantum Advantage

Quantum computing has long been heralded as one of the most eagerly anticipated technological revolutions on the horizon, yet actual progress has sharply diverged from the industry's soaring promises. Curiously, despite repeatedly missing targets, sector insiders seem more optimistic than ever. Sabine Hossenfelder introduces this strange paradox, pointing to yet another recent claim of quantum advantage published jointly by IBM and researchers at the University of Chicago. While "quantum advantage" implies executing a calculation that would take unfeasibly long on a traditional supercomputer, it carries no requirement that the calculation serve any practical purpose. In this latest demonstration—as with several past milestones—the benchmark task was simply generating a random distribution of values with zero known real-world utility.

Examining the track record of these announcements, philosopher of physics Amit Hagar recently noted that within roughly 18 months of any claimed quantum advantage milestone, the results have consistently been reproduced or shown to be reproducible on conventional computers. The lone exception remains random circuit sampling. Hagar's assessment is stark:

> "A demonstration that a hardware can run a circuit and that the circuit produces an observable matching [a] known answer is a demonstration of competent diagnostic engineering. The press cycle of the past several years has conflated such results with computational advantage and the conflation has been productive of funding without being productive of the meta fact under investigation."

[01:48](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=108s) The Broken Promises of the NISQ Era

The core root of today's disillusionment lies in claims made five years ago regarding Noisy Intermediate-Scale Quantum (NISQ) devices. Researchers repeatedly insisted that even small, imperfect, error-prone quantum hardware would soon unlock immediate commercial utility. The timeline of those predictions reveals a consistent pattern of unmet deadlines:

- **Google (2017):** Researchers contended that "short-term returns are possible with the small devices that will emerge within the next 5 years." (Didn't happen). - **IBM (2018):** Claimed that in the near term, NISQ machines had "the best potential to deliver business advantage," declaring the dawn of the commercial quantum era over the next few years. (Didn't happen). - **IonQ (2021):** Stated in its annual report that during the NISQ computing era, they expected "quantum machine learning to be the first solution to transition into broadly available applications." (Didn't happen). - **Kipu Quantum (2023):** Co-founder Enrique Solano announced on August 11, 2023, that the startup was working to reach NISQ advantage for industrial use cases "within 18 to 36 months"—a window that has now elapsed. (Didn't happen).

These academic proclamations quickly bled into market research forecasts. In 2019, the Boston Consulting Group estimated that end-user value in targeted sectors would reach a "relatively modest" $2 billion to $5 billion by 2024. In reality, that commercial value turned out to be nil. As Sabine quips, to date, the only genuinely profitable application to come out of quantum computing has been forecasting profitable quantum applications.

```mermaid flowchart TD accTitle: The Quantum Hype Pipeline accDescr: Diagram illustrating how academic claims fed consulting hype and ended in zero real-world value. subgraph Research["Academic & Corporate Claims"] A["Google & IBM (2017–2018):<br/>Near-term NISQ commercial advantage"] B["Startups (2021–2023):<br/>NISQ advantage in 18–36 months"] end

subgraph Forecasts["Consulting Amplification"] C["Boston Consulting Group (2019):<br/>$2B–$5B end-user value by 2024"] end

subgraph Outcome["Real-World Delivery"] D["Classical simulation matches benchmarks"] E["End-user commercial value: NIL"] end

A --> C B --> C C --> D D --> E ```

[04:01](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=241s) Reality Check: Niche Applications vs. Classical AI

The notion that noisy intermediate-scale hardware could support commercial applications was, in Sabine's blunt assessment, "11 out of 10 on the bullshit meter." She points out that John Preskill, the Caltech physicist who originally coined the term NISQ, noticeably never claimed these machines would yield immediate commercial payoffs. The hype cycle formed when academic overpromises spilled over into corporate press releases and consulting reports without anyone checking the assumptions—save for theoretical computer scientists like Scott Aaronson, who maintained that while quantum hardware is deeply interesting for foundational research, commercial claims should be treated with extreme caution.

Sabine clarifies that she is not claiming quantum computing will never find practical applications, but rather that viable commercial use cases will not arrive this decade. When they do, they will likely be confined to extremely narrow, niche scientific problems rather than fueling multi-billion-dollar mass industries or triggering a "new industrial revolution."

Furthermore, DeepMind co-founder Demis Hassabis has highlighted an obstacle the quantum community often ignores: real-world quantum physical systems tend to display structural regularities that modern artificial intelligence running on classical computers can model with astonishing accuracy. If classical AI can approximate complex quantum phenomena, the commercial necessity for expensive quantum computing hardware shrinks even further.

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## Notable quotes > [00:00](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=0s) A demonstration that a hardware can run a circuit and that the circuit produces an observable matching [a] known answer is a demonstration of competent diagnostic engineering. The press cycle of the past several years has conflated such results with computational advantage and the conflation has been productive of funding without being productive of the meta fact under investigation. — Amit Hagar

> [01:48](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=108s) short-term returns are possible with the small devices that will emerge within the next 5 years. — Google

> [01:48](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=108s) the best potential to deliver business advantage — IBM

> [01:48](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=108s) quantum machine learning to be the first solution to transition into broadly available applications. — IonQ

> [01:48](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=108s) within 18 to 36 months — Enrique Solano

> [01:48](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=108s) relatively modest — Boston Consulting Group

> [01:48](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=108s) the only genuinely profitable application to come out of quantum computing has been forecasting profitable quantum applications. — Sabine Hossenfelder

> [04:01](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=241s) 11 out of 10 on the bullshit meter. — Sabine Hossenfelder

> [04:01](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=241s) new industrial revolution

> [05:30](https://www.youtube.com/watch?v=DX_oIQ-tA6M&t=330s) the training grounds

## Mentioned Amit Hagar, Boot.dev, Boston Consulting Group, Caltech, DeepMind, Demis Hassabis, Enrique Solano, Fortran, Go, Google, IBM, IonQ, John Preskill, Kipu Quantum, Python, Sabine Hossenfelder, Scott Aaronson, SQL, University of Chicago

## Video description “Click this link https://boot.dev/?promo=SABINE and use my code SABINE to get 25% off your first payment for boot.dev.”

Quantum computing was supposed to be the next big thing, and for years the companies building it kept promising commercial payoffs were just around the corner and possible already with small, noisy devices (NISQs). Google, IBM, IonQ, they all gave dates. None of them delivered. So why is the industry more optimistic today than ever? Let's take a look.

#science #sciencenews #technology #quantumcomputing

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[Source](https://www.youtube.com/watch?v=DX_oIQ-tA6M)

中文简报

IBM与谷歌错失量子计算既定期限，商业价值归零企业五年来屡屡落空的承诺表明，随着经典人工智能达到目标基准，含噪量子硬件在这十年内将不会产生任何商业价值。 Sabine Hossenfelder剖析了日益高涨的量子计算炒作与屡屡错失商业里程碑的现实之间日益拉大的差距。她详细指出，近期展示的“量子优越性”依赖于随机线路采样等毫无实际效用的武断基准任务，且这些结果通常在18个月内就能在经典计算机上复现。此外，科技巨头和咨询公司预测在NISQ（含噪中等规模量子）时代将产生丰厚商业价值的预言也几乎全盘落空。她得出结论认为，尽管量子计算对基础科学仍具价值，但切实可行的商业应用在这十年内不会出现，尤其是随着经典人工智能越来越多地直接对复杂量子系统进行建模。屡次被宣称的量子优越性仍然依赖于随机线路采样等毫无实际效用的武断任务，而几乎所有其他里程碑在大约18个月内都在经典计算机上被成功复现。 (0:00) Google、IBM、IonQ和Kipu Quantum在2017年至2023年间设定的关于含噪中等规模量子（NISQ）设备实现商业优越性的所有主要时间表均已正式逾期，未带来任何实际效用。 (1:48) 波士顿咨询公司（BCG）在2019年预测到2024年目标量子领域将产生20亿至50亿美元的终端用户商业价值，但实际上产生的价值几乎为零。 (1:48) 正如DeepMind的Demis Hassabis所强调的，经典AI在建模物理量子规律方面的进展进一步降低了含噪量子硬件的商业必要性。 (4:01)

Reading summary

Sabine Hossenfelder argues that the quantum computing industry has systematically failed to deliver on its commercial promises, relying on artificial benchmarks while noisy intermediate hardware fails to produce real-world economic value. Benchmarks Devoid of Practical Utility (0:00) Hossenfelder points to recent milestones, such as a joint demonstration by IBM and the University of Chicago, where claimed "quantum advantage" centered on generating random numerical distributions with no known practical utility. While quantum advantage technically requires only performing a task unfeasible for a classical supercomputer, it demands no real-world relevance. Addressing this pattern, philosopher of physics Amit Hagar observed that outside of random circuit sampling, classical supercomputers have reliably reproduced or simulated claimed quantum advantage milestones within roughly 18 months. Hagar contends that industry press cycles have conflated competent diagnostic engineering with genuine computational advantage, generating investor funding rather than meaningful scientific breakthroughs. Systematic Failures Across the NISQ Timeline (1:48) The current wave of skepticism follows repeated misses across the Noisy Intermediate-Scale Quantum (NISQ) era, during which researchers insisted that imperfect, error-prone processors would generate immediate commercial returns. Google researchers predicted in 2017 that short-term returns would materialize within five years, IBM claimed in 2018 that NISQ systems held near-term commercial potential, and IonQ projected in its 2021 annual report that quantum machine learning would quickly reach broad availability. Most recently, Kipu Quantum co-founder Enrique Solano projected in August 2023 that industrial advantage would arrive within 18 to 36 months—a deadline that has elapsed without delivery. These corporate expectations were further amplified by market researchers. In 2019, the Boston Consulting Group estimated that end-user sector value from quantum computing would reach between $2 billion and $5 billion by 2024. In reality, actual end-user commercial value reached nil, prompting Hossenfelder's observation that the industry's only consistently profitable application has been forecasting future profits. Classical AI and Limited Commercial Viability (4:01) Hossenfelder emphasizes that John Preskill, the Caltech physicist who coined the term NISQ, never claimed intermediate hardware would deliver immediate business payoffs, while computer scientists like Scott Aaronson consistently urged caution regarding commercial claims. Hossenfelder does not argue that quantum computing is entirely impossible, but contends that commercially viable use cases will not emerge this decade. When practical applications do appear, she expects them to be confined to specialized, niche scientific problems rather than powering broad industrial transformations. This commercial window is narrowing further due to rapid advances in conventional computing. DeepMind co-founder Demis Hassabis highlighted that physical quantum systems possess underlying structural regularities that modern artificial intelligence running on classical hardware can model with high accuracy, substantially reducing the economic necessity of developing complex and expensive quantum processors. Hossenfelder concludes that while quantum computing remains an interesting foundational research field, the notion of near-term commercial viability was inflated by corporate press releases and consulting estimates, leaving the industry without practical commercial relevance for the foreseeable future.

Source quotes

[0:00] Amit Hagar: A demonstration that a hardware can run a circuit and that the circuit produces an observable matching [a] known answer is a demonstration of competent diagnostic engineering. The press cycle of the past several years has conflated such results with computational advantage and the conflation has been productive of funding without being productive of the meta fact under investigation. [1:48] Google: short-term returns are possible with the small devices that will emerge within the next 5 years. [1:48] IBM: the best potential to deliver business advantage [1:48] IonQ: quantum machine learning to be the first solution to transition into broadly available applications. [1:48] Enrique Solano: within 18 to 36 months [1:48] Boston Consulting Group: relatively modest [1:48] Sabine Hossenfelder: the only genuinely profitable application to come out of quantum computing has been forecasting profitable quantum applications. [4:01] Sabine Hossenfelder: 11 out of 10 on the bullshit meter. [4:01] new industrial revolution [5:30] the training grounds

Original transcript

[0:00] Quantum computing has been one of the most, if not the most anticipated new technology on the [0:06] horizon, but things haven't been going as many in the industry thought they would. Nevertheless, [0:14] they're more optimistic today than ever. What explains this weird mismatch? Today, I have an [0:21] explanation for this and also clarification on my opinion about quantum computing. Just last week, [0:26] we saw another claim of quantum advantage. This time from IBM and researchers at the University [0:34] of Chicago. Quantum advantage means that a quantum computer did a calculation that would have taken [0:40] unfeasibly long on a conventional computer. It doesn't necessarily mean that the calculation had [0:45] any particular use. Indeed, in this new example, as well as in many previous ones, the calculation [0:51] is to produce a random distribution of values. It's got no known use. There have been other [0:58] previous claims that quantum advantage has been achieved. But as the philosopher Amit Hagar just [1:05] nicely summarized in a new paper, within 18 months of any of these demonstrations, they've all been [1:11] reproduced on a conventional computer or at least been shown to be reproducible. The one exception [1:17] he names was one of those random sampling cases. Hagar is blunt with his judgment. He writes, [1:24] "A demonstration that a hardware can run a circuit and that the circuit produces an observable [1:30] matching unknown answer is a demonstration of competent diagnostic engineering. The press [1:35] cycle of the past several years has conflated such results with computational advantage and [1:41] the conflation has been productive of funding without being productive of the meta fact under [1:48] investigation." What Hagar is referring to is that 5 years ago, many thought that quantum [1:55] computers would already have a commercial use even when they're small and noisy. This [2:01] has been referred to as Noisy Intermediate Scale Quantum computing, NISQ for short. For example, [2:07] in 2017, researchers from Google claimed that we contend that short-term returns are possible with [2:14] the small devices that will emerge within the next 5 years. Didn't happen. In 2018, [2:20] researchers from IBM said that in the near term, NISQ computers have the best potential to deliver [2:26] business advantage. One of them also said that IBM sees the next few years as the dawn of the [2:32] commercial quantum era. Didn't happen. The quantum computing firm IONQ said in its 2021 annual [2:38] report during the NISQ computing era, we expect quantum machine learning to be the first solution [2:44] to transition into broadly available applications. Didn't happen. Enrique Solano, one of the founders [2:51] of the German startup Kipu Quantum said that we work to reach a NISQ advantage for industry [2:57] use cases within 18 to 36 months. that was on August 11th, 2023, almost exactly 36 months ago, [3:06] didn't happen. These claims from researchers then spilled over into marketing analysis. The [3:12] Boston Consulting Group, for example, predicted in 2019 that we anticipate value for end users in [3:18] these sectors to reach a relatively modest 2 to 5 billion by 2024. The value to end users turned [3:25] out to be even more modest, namely nil. To date, the only profitable quantum application has been [3:32] forecasting profitable quantum applications. In my mind, the idea that NISQ [3:37] computers will have any commercial uses was obviously [ __ ] And I mean 11 out of 10 on the [ __ ] meter. [3:45] If you need evidence of this other than my word, I'll pretend to be deeply insulted, but refer [3:51] you to John Preskill, one of the world's experts on quantum computing, who for all I know termed [3:55] the phrase NISQ, and who famously did not claim these devices would have any commercial use. Yes, [4:01] sometimes the relevant bit is what they don't say. So, what I think happened is that academia [ __ ] [4:08] leaked into industry and consulting firms and no one was there to clean up the mess. No one except [4:15] possibly Scott Aaronson who's been saying all along that quantum computers are super interesting from [4:20] a research perspective. But commercial advantage, well, let's not talk about it. Let me be clear. [4:26] I'm not saying that quantum computers will never have a commercial use. I just think it'll take [4:32] a long time to get there. Certainly not in this decade. And the uses will be extremely niche and [4:40] limited. Not a billion-dollar market. I've noticed that people call me pessimistic when it comes to [4:46] quantum computers. I think I'm not. It's just that even my most optimistic self thinks it's idiotic [4:52] that quantum computers will bring a new industrial revolution as some headlines put it. At least [4:58] confidence has reached for tolerance. I also want to remind you that Demis Hassabis, who I've put down as one [5:06] of the most intelligent people on the planet, has argued that whatever quantum processes happen in [5:11] nature, will likely have so many regularities, you can model them with AI on a conventional [5:17] computer. I think scientists aren't taking him seriously enough. If my day had any more hours, [5:24] I try to formalize this into a formal conjecture, but the way things are going, AI will probably [5:30] beat me to it. The last time I wrote code, it was Fortran. This is why I decided I need to [5:38] urgently upgrade my coding skills. If you're in a similar situation, I have a recommendation for [5:43] you. Boot.dev, who've been sponsoring this video. Boot.dev is focused on back-end development. So, [5:50] Python, SQL, Go, the kind of stuff that actually runs the world behind the scenes. [5:55] And you don't just watch lessons, you build real projects, you write code directly in the browser, [6:00] you solve challenges, and you level up like it's a slightly nerdy role-playing game. They [6:06] also have what they call the training grounds. That's basically infinite practice problem. [6:10] I've been meaning for a while to set up a website for all my content, but I want to know what's [6:16] going on with the data and what vulnerabilities to watch out for. And Boot.dev has been really [6:23] eye opening. You can create an account for free, read and watch all the content and try a demo [6:29] of the interactive features. 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