healthcarereimagined

Healthcare leaders are bullish about the benefits of artificial intelligence, a new report from Kyndryl shows, but many are still grappling with basic questions around IT infrastructure, cybersecurity, regulations, workforce and change management.

By Andrea Fox , Senior Editor | October 27, 2025 | 10:34 AM

To unlock the full value of artificial intelligence at scale, healthcare organizations need to modernize their IT stack, improve cybersecurity and invest in upskilling their workforce alongside their technology strategy, a new report suggests.

Drawing on data from its own AI-powered digital business platform, along with insights from some 3,700 business executives worldwide, the 2025 readiness report from infrastructure services company Kyndryl shows that healthcare (and other industries) is at a tipping point.

AI has been making inroads and soon could bring substantial ROI. But there are challenges.

For one, healthcare organizations are still falling behind on mitigating cybersecurity risks. More fundamentally, however, many are still hamstrung by a lack of alignment among C-suite leadership and key frontline staff about how to scale AI beyond initial pilot phases.

To unlock AI’s full value, organizations need to modernize their infrastructure, improve cybersecurity and invest in upskilling their workforce alongside their technology strategy, said Trent Sanders, vice president for U.S. healthcare and life sciences at Kyndryl.

“The organizations that succeed will be those that pair innovation with a culture that’s ready to embrace it,” Sanders told Healthcare IT News.

‘Seamless integration’

The majority of business leaders across industries think AI will transform day-to-day functions over the next 12 months, according to the new report.

But nearly half (49%) of businesses assessed by Kyndryl researchers are still seeing innovation delayed by a lack of technical readiness and a range of uncertainties.

“Among organizations not yet seeing positive returns from AI, 35% blame integration difficulties,” they said in the October report. “Without seamless integration, even the most advanced technologies fail to deliver value.”

But there’s some good news for healthcare: The industry is listed as one of the top performers across industries for AI-enabled automation.

“High automation density correlates with accelerated recovery times, reduced human error and enhanced scalability,” said Kyndryl researchers. “It also enables predictive analytics and supports proactive remediation strategies, and 32% of organizations experienced reduced costs due to automation and optimization in the past 12 months.”

Still, healthcare organizations continue to grapple with how to scale AI and workforce readiness – from technical skills to trust and beyond.

The Kyndryl report notes that healthcare may be particularly vulnerable to AI integration difficulties and faces specific barriers that need to be overcome before more widespread positive ROI.

‘The issue isn’t just technical’

Nearly one in five healthcare technologies are at or nearing their end, creating roadblocks to innovation, Sanders said.

“Healthcare’s complexity is both its strength and its challenge,” he explained.

“Healthcare providers are dealing with legacy systems, fragmented data environments and strict compliance requirements, all of which make AI integration more difficult than in other industries,” said Sanders.

“But the issue isn’t just technical. Integration often stalls because leadership teams aren’t aligned on how to scale AI beyond the pilot phase.”

To unlock ROI, he said he advises healthcare organizations to modernize their infrastructure and build hybrid cloud environments that support secure data flows.

“When technology and leadership move in sync,” said Sanders, “that’s when AI starts delivering real value.”

Healthcare organizations are also behind on their readiness to mitigate business risk – with just 38%, compared to 42% across industries, upgrading their infrastructure and investing in cybersecurity.

“That’s concerning, especially when 85% of healthcare organizations have experienced a cyber-related outage in the past year,” said Sanders, who noted that “agility is as much about speed as it is about resilience.”

Healthcare can improve agility, and some modernization strategies can make that happen quickly, he said.

“Many systems are outdated, and that technical debt slows everything down,” said Sanders. “Quick wins come from replacing end-of-service assets, using AI to strengthen cyber defenses and fostering a culture that supports fast, informed decision-making.”

Automation, where healthcare tends to succeed when compared to other industries, is not only reducing costs for healthcare organizations but also improving scalability.

“Automation is absolutely helping healthcare organizations cut costs, but it’s also doing much more,” Sanders said. “High automation density means fewer manual errors, faster recovery times and better scalability. In healthcare, that translates to smoother operations and improved patient care.”

But that automation “doesn’t work in isolation,” he pointed out. “The organizations seeing the biggest gains are also investing in cloud modernization and aligning their workforce around new ways of working. It’s the combination of automation plus intentional strategy that drives real impact.”

‘Growth rather than disruption’

Kyndryl’s researchers found that 84% of healthcare leaders expect AI to completely transform roles within the next 12 months, despite legacy systems and other integration challenges.

“It’s a bold prediction, and it speaks to the urgency healthcare leaders feel,” said Sanders. “That’s not just optimism; it’s a recognition that AI is no longer optional. It’s the lever for transformation.”

He acknowledged that expectations alone won’t close the gap.

The organizations that succeed in preparing their workforces “will be those that pair innovation with a culture that’s ready to embrace it,” he said.

“When employees are brought into the process, trained and empowered, AI becomes a tool for growth rather than disruption.”

Healthcare organizations can advance AI readiness despite workforce challenges – a shortage of talent and a lack of skills.

“Skilling challenges are one of the biggest hurdles healthcare organizations face,” said Sanders. “AI is evolving fast, but our workforce isn’t always equipped to keep pace; technical skills, cognitive adaptability and trust in AI are all critical, and right now, they’re in short supply.”

Aware of the gap, healthcare leaders are building trust with “transparency, ethical guidelines and involving employees in the implementation process,” he explained.

“From there, it’s about investing in upskilling and reskilling, and creating cultures that embrace change,” said Sanders. “Organizations with adaptable cultures are significantly more likely to report positive ROI on AI. That’s not a coincidence. It’s a blueprint.” 

Andrea Fox is senior editor of Healthcare IT News.
Email: afox@himss.org
Healthcare IT News is a HIMSS Media publication.

Article link: https://www.healthcareitnews.com/news/are-hospitals-and-health-systems-really-ready-ai

If we’re not careful, Microsoft, Amazon, and other large companies will leverage their position to set the policy agenda for AI, as they have in many other sectors.

By Amba Kak, Sarah Myers West, &Meredith Whittaker

December 5, 2023

Until late November, when the epic saga of OpenAI’s board breakdown unfolded, the casual observer could be forgiven for assuming that the industry around generative AI was a vibrant competitive ecosystem. 

But this is not the case—nor has it ever been. And understanding why is fundamental to understanding what AI is, and what threats it poses. Put simply, in the context of the current paradigm of building larger- and larger-scale AI systems, there is no AI without Big Tech. With vanishingly few exceptions, every startup, new entrant, and even AI research lab is dependent on these firms. All rely on the computing infrastructure of Microsoft, Amazon, and Google to train their systems, and on those same firms’ vast consumer market reach to deploy and sell their AI products. 

Indeed, many startups simply license and rebrand AI models created and sold by these tech giants or their partner startups. This is because large tech firms have accrued significant advantages over the past decade. Thanks to platform dominance and the self-reinforcing properties of the surveillance business model, they own and control the ingredients necessary to develop and deploy large-scale AI. They also shape the incentive structures for the field of research and development in AI, defining the technology’s present and future. 

The recent OpenAI saga, in which Microsoft exerted its quiet but firm dominance over the “capped profit” entity, provides a powerful demonstration of what we’ve been analyzing for the last half-decade. To wit: those with the money make the rules. And right now, they’re engaged in a race to the bottom, releasing systems before they’re ready in an attempt to retain their dominant position. 

Concentrated power isn’t just a problem for markets. Relying on a few unaccountable corporate actors for core infrastructure is a problem for democracy, culture, and individual and collective agency. Without significant intervention, the AI market will only end up rewarding and entrenching the very same companies that reaped the profits of the invasive surveillance business model that has powered the commercial internet, often at the expense of the public. 

The Cambridge Analytica scandal was just one among many that exposed this seedy reality. Such concentration also creates single points of failure, which raises real security threats. And Securities and Exchange Commission chair Gary Gensler has warned that having a small number of AI models and actors at the foundation of the AI ecosystem poses systemic risks to the financial order, in which the effects of a single failure could be distributed much more widely. 

The assertion that AI is contingent on—and exacerbates—concentration of power in the tech industry has often been met with pushback. Investors who have moved quickly from Web3 to the metaverse to AI are keen to realize returns in an ecosystem where a frenzied press cycle drives valuations toward profitable IPOs and acquisitions, even if the promises of the  technology in question aren’t ever realized. 

But the attempted ouster—and subsequent reintegration—of OpenAI cofounders Sam Altman and Greg Brockman doesn’t just bring the power and influence of Microsoft into sharp focus; it also proves our case that these commercial arrangements give Big Tech profound control over the trajectory of AI. The story is fairly simple: after apparently being blindsided by the board’s decision, Microsoft moved to protect its investment and its road map to profit. The company quickly exerted its weight, rallying behind Altman and promising to “acquihire” those who wanted to defect. 

Microsoft now has a seat on OpenAI’s board,albeit a nonvoting one. But the true leverage that Big Tech holds in the AI landscape is the combination of its computing power, data, and vast market reach. In order to pursue its bigger-is-better approach to AI development, OpenAI made a deal. It exclusively licenses its GPT-4 system and all other OpenAI models to Microsoft in exchange for access to Microsoft’s computing infrastructure. 

For companies hoping to build base models, there is little alternative to working with either Microsoft, Google, or Amazon. And those at the center of AI are well aware of this, as illustrated by Sam Altman’s furtive search for Saudi and Emirati sovereign investment in a hardware venture he hoped would rival Nvidia. That company holds a near monopoly on state-of-the-art chips for AI training and is another key choke point along the AI supply chain. US regulators have since unwound an initial investment by Saudi Arabia into an Altman-backed company, RainAI, reinforcing the difficulty OpenAI faces in navigating the even more concentrated chipmaking market.

There are few meaningful alternatives, even for those willing to go the extra mile to build industry-independent AI. As we’ve outlined elsewhere, “‘open-source AI”—an ill-defined term that’s currently used to describe everything from Meta’s (comparatively closed) LLaMA-2 and Eleuther’s (maximally open) Pythia series—can’t on its own offer escape velocity from industry concentration. For one thing, many open-source AI projects operate through compute credits, revenue sharing, or other contractual arrangements with tech giants that grapple with the same structural dependencies. In addition, Big Tech has a long legacy of capturing, or otherwise attempting to seek profit from, open-source development. Open-source AI can offer transparency, reusability, and extensibility, and these can be positive. But it does not address the problem of concentrated power in the AI market. 

The OpenAI-Microsoft saga also demonstrates a fact that’s frequently lost in the hype around AI: there isn’t yet a clear business model outside of increasing cloud profits for Big Tech by bundling AI services with cloud infrastructure. And a business model is important when you’re talking about systems that can cost hundreds of millions of dollars to train and develop. 

Microsoft isn’t alone here: Amazon, for example, runs a marketplace for AI models, on which all of its products, and a handful of others, operate using Amazon Web Services. The company recently struck an investment deal of up to $4 billion with Anthropic, which has also pledged to use Amazon’s in-house chip, Trainium, optimized for building large-scale AI. 

Big Tech is becoming increasingly assertive in its maneuverings to protect its hold over the market. Make no mistake: though OpenAI was in the crosshairs this time, now that we’ve all seen what it looks like for a small entity when a big firm it depends on decides to flex, others will be paying attention and falling in line. 

Regulation could help, but government policy often winds up entrenching, rather than mitigating, the power of these companies as they leverage their access to money and their political clout. Take Microsoft’s recent moves in the UK as an example: last week it announced a £2.5 billion investment in building out cloud infrastructure in the UK, a move lauded by a prime minister who has clearly signaled his ambitions to build a homegrown AI sector in the UK as his primary legacy. This news can’t be read in isolation: it is a clear attempt to blunt an investigation into the cloud market by the UK’s competition regulator following a studythat specifically called out concerns registered by a range of market participants regarding Microsoft’s anticompetitive behavior. 

From OpenAI’s (ultimately empty) threat to leave the EU over the AI Act to Meta’s lobbying to exempt open-source AI from basic accountability obligations to Microsoft’s push for restrictive licensing to the Big Tech–funded campaign to embed fellows in Congress, we’re seeing increasingly aggressive stances from large firms that are trying to shore up their dominance by wielding their considerable economic and political power.

Tech industry giants are already circling their wagons as new regulations emerge from the White House, the EU, and elsewhere. But it’s clear we need to go much further. Now’s the time for a meaningful and robust accountability regime that places the interests of the public above the promises of firms not known for keeping them. 

We need aggressive transparency mandates that clear away the opacity around fundamental issues like the data AI companies are accessing to train their models. We also need liability regimes that place the burden on companies to demonstrate that they meet baseline privacy, security, and bias standards before their AI products are publicly released. And to begin to address concentration, we need bold regulation that forces business separation between different layers of the AI stack and doesn’t allow Big Tech to leverage its dominance in infrastructure to consolidate its position in the market for AI models and applications. 

There are few meaningful alternatives, even for those willing to go the extra mile to build industry-independent AI. As we’ve outlined elsewhere, “‘open-source AI”—an ill-defined term that’s currently used to describe everything from Meta’s (comparatively closed) LLaMA-2 and Eleuther’s (maximally open) Pythia series—can’t on its own offer escape velocity from industry concentration. For one thing, many open-source AI projects operate through compute credits, revenue sharing, or other contractual arrangements with tech giants that grapple with the same structural dependencies. In addition, Big Tech has a long legacy of capturing, or otherwise attempting to seek profit from, open-source development. Open-source AI can offer transparency, reusability, and extensibility, and these can be positive. But it does not address the problem of concentrated power in the AI market. 

The OpenAI-Microsoft saga also demonstrates a fact that’s frequently lost in the hype around AI: there isn’t yet a clear business model outside of increasing cloud profits for Big Tech by bundling AI services with cloud infrastructure. And a business model is important when you’re talking about systems that can cost hundreds of millions of dollars to train and develop. 

Microsoft isn’t alone here: Amazon, for example, runs a marketplace for AI models, on which all of its products, and a handful of others, operate using Amazon Web Services. The company recently struck an investment deal of up to $4 billion with Anthropic, which has also pledged to use Amazon’s in-house chip, Trainium, optimized for building large-scale AI. 

Big Tech is becoming increasingly assertive in its maneuverings to protect its hold over the market. Make no mistake: though OpenAI was in the crosshairs this time, now that we’ve all seen what it looks like for a small entity when a big firm it depends on decides to flex, others will be paying attention and falling in line. 

Regulation could help, but government policy often winds up entrenching, rather than mitigating, the power of these companies as they leverage their access to money and their political clout. Take Microsoft’s recent moves in the UK as an example: last week it announced a £2.5 billion investment in building out cloud infrastructure in the UK, a move lauded by a prime minister who has clearly signaled his ambitions to build a homegrown AI sector in the UK as his primary legacy. This news can’t be read in isolation: it is a clear attempt to blunt an investigation into the cloud market by the UK’s competition regulator following a studythat specifically called out concerns registered by a range of market participants regarding Microsoft’s anticompetitive behavior. 

From OpenAI’s (ultimately empty) threat to leave the EU over the AI Act to Meta’s lobbying to exempt open-source AI from basic accountability obligations to Microsoft’s push for restrictive licensing to the Big Tech–funded campaign to embed fellows in Congress, we’re seeing increasingly aggressive stances from large firms that are trying to shore up their dominance by wielding their considerable economic and political power.

Tech industry giants are already circling their wagons as new regulations emerge from the White House, the EU, and elsewhere. But it’s clear we need to go much further. Now’s the time for a meaningful and robust accountability regime that places the interests of the public above the promises of firms not known for keeping them. 

We need aggressive transparency mandates that clear away the opacity around fundamental issues like the data AI companies are accessing to train their models. We also need liability regimes that place the burden on companies to demonstrate that they meet baseline privacy, security, and bias standards before their AI products are publicly released. And to begin to address concentration, we need bold regulation that forces business separation between different layers of the AI stack and doesn’t allow Big Tech to leverage its dominance in infrastructure to consolidate its position in the market for AI models and applications. 

Article link: https://www-technologyreview-com.cdn.ampproject.org/c/s/www.technologyreview.com/2023/12/05/1084393/make-no-mistake-ai-is-owned-by-big-tech/amp/

by Brian Eastwood

 Oct 6, 2025

What you’ll learn:

• Apollo Global Management is assessing AI value across entire industries to cut costs and improve productivity in its portfolio companies.
• Michelin’s proof-of-concept approach has identified 200-plus AI use cases that generate 50 million euros in ROI annually.
• “Vibe analytics” can help leaders get data insights in minutes instead of weeks.
• A new framework examines four modes of human-robot collaboration in warehouses.

When implementing artificial intelligence, enterprise leaders must consider where AI will create value, not just where it will be useful. The latest ideas from MIT Sloan Management Review illustrate how to make this happen in verticals as varied as manufacturing, publishing, cybersecurity, and e-commerce — with specific takeaways for warehouse operations.

Assess AI’s impact across an industry 

One important sign that AI has the potential to create value is the willingness of private equity firms to build AI capabilities into portfolio companies, according to Thomas H. Davenport, a research fellow with the MIT Initiative on the Digital Economy. Consider how Apollo Global Management has taken AI from pilot to production to scale across its portfolio:

• Educational publisher Cengage has cut content production costs by 40% and lead generation costs by 20% through process automation.
• At Yahoo, AI-generated code has helped engineering teams improve productivity by more than 20%.
• Chemical distributor Univar Solutions achieved a 30% engagement rate with an AI agent that reached out to dormant accounts. 

Apollo starts by evaluating AI’s risks and rewards at a macro level — assessing how AI is impacting not just the target company but its entire industry. Davenport and co-author Randy Bean write that this helps Apollo avoid high-risk scenarios and ensure that innovation happens in the right place at the right time. Next, Apollo develops AI use cases for the target company and crafts a post-acquisition implementation plan. 

Apollo also partnered with a venture capital firm to launch an incubator for B2B AI startups that includes companies focused on supply chain resiliency, manufacturing response time, and a range of other AI capabilities. The expectation is that the startups will eventually provide services to Apollo’s portfolio companies.

Find AI’s value at proof of concept

For some enterprises, AI efforts focus on core business processes. For others, it’s all about innovation from the ground up. Multinational manufacturer Michelin Group has managed to do both. In a second analysis, Davenport and Bean highlight how Michelin is accelerating manufacturing innovation, with more than 200 AI use cases across quality control, inventory management, and predictive modeling. 

The primary lever for AI adoption, group chief data and AI officer Ambica Rajagopal said, is identifying potential value at the proof-of-concept stage. Rajagopal’s team also conducts a post-deployment assessment of the actual value delivered.

From there, Michelin’s innovation team of 6,000 employees across 13 countries gets to work. All told, improved productivity from AI projects now generates more than 50 million euros in ROI per year, with a growth rate increase approaching 40% annually. That has helped Michelin empower employees in all roles to harness data, create value, and support the company’s growth. 

Empower leaders to ask questions to data sets

If leaders could get data insights in minutes instead of weeks, they would be well positioned to determine which initiatives show the most promise. Michael Schrage, a research fellow with the MIT Initiative on the Digital Economy, examines an approach dubbed “vibe analytics.” This approach lets decision makers engage directly with data through AI-powered conversation, eliminating the traditional translation process between business questions and technical analysis.

The concept builds on “vibe coding,” an AI-assisted approach that lets people create code using everyday language. Vibe analytics allows leaders to ask questions like “What’s happening with our conversion rates?” and immediately explore potential causes through improvisational dialogue with AI. 

Vibe analytics stands to democratize how knowledge is generated in organizations, Schrage writes. Instead of waiting for static reports, leaders can start a direct dialogue with messy data. And teams can turn KPIs into conversational partners and debug assumptions in real time, accelerating decision-making while revealing unexpected patterns.

Employing vibe analytics, a Southeast Asian telecom company surfaced more financially relevant insights in 90 minutes than it typically generates in 90 days, developing a novel scoring system that reveals which service contracts correlate with higher margins and risks. Meanwhile, a cybersecurity firm discovered actionable patterns in its freemium customer base that its revenue team hadn’t considered.

Help robots and workers get along

While teams of robots and humans are increasingly common in warehouses, effective collaboration remains elusive. Workers struggle to keep pace with robots that rarely need breaks, and they get frustrated with rigid automated systems. Slowing robots down or taking them offline entirely is good for morale but bad for cost management.

With that in mind, Benedict Jun Ma and Maria Jesús Saénz at the MIT Digital Supply Chain Transformation Lab created a framework to describe human-robot collaboration in warehouses and distribution facilities, where they see AI becoming a critical tool for improving how humans and robots work together. They begin with four modes of collaboration:

• Robot-in-lead, ideal for unloading cargo and picking simple orders (such as in a shoe warehouse).
• Human-in-lead, helpful for packaging orders (especially for high-value items).
• Elementary collaboration, where robots gather items for workers to sort.
• Advanced collaboration, where AI helps robots better match human speed and strength, as well as forecast and manage disruption.

As the authors note, AI is vital to advanced human-robot collaboration. It gives robots contextual awareness, such as the processing of fragile goods that require special handling; it also optimizes robots’ movement through the warehouse and supports audio or visual communication with human workers. AI models can also assess robots’ performance, recommend adjustments, and provide alerts to human workers.

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This article draws on insights from MIT Sloan Management Review, which leads the discourse about advances in management practice among influential thought leaders in business and academia. The publication equips its readers with evidence-based insights and guidance to innovate, operate, lead, and create value in a world being transformed by technology and large-scale societal and environmental forces.

Article link: https://mitsloan.mit.edu/ideas-made-to-matter/ai-implementation-strategies-4-insights-mit-sloan-management-review?

by Beth Stackpole

 Aug 19, 2025

Why It Matters

Quantum computing is evolving into a tangible technology that holds significant business and commercial promise, although the exact timing of when it will impact those areas remains unclear, according to a new report led by researchers at the MIT Initiative on the Digital Economy.i

The “Quantum Index Report” is a comprehensive assessment of the technology and the global landscape, from patents to the quantum workforce.

The “Quantum Index Report 2025” charts the technology’s momentum, with a comprehensive, data-driven assessment of the state of quantum technologies. 

The inaugural report aims to make quantum computing and networking technologies more accessible to entrepreneurs, investors, teachers, and business decision makers — all of whom will play a critical role in how quantum computing is developed, commercialized, and governed. 

“There are a lot of folks who are interested in what’s going on in quantum, but the field is impenetrable to them,” said Jonathan Ruane, a research scientist at MIT IDE and editor-in-chief of the “Quantum Index Report.” The report is co-authored by researchers Elif Kiesow and Johannes Galatsanos from MIT IDE, and Carl Dukatz, Edward Blomquist, and Prashant Shuklafrom Accenture.

Senior business executives across industries are fast becoming what Ruane calls “quantum curious,” inspired in part by the rapid rise of artificial intelligence. “The speed at which AI is transforming industries has alerted managers to the concept that technologies that are simmering in the background can explode really quickly and have tremendous impact,” Ruane said. “They want to make sure they have competency and insights into quantum so they don’t get caught out on missing the next big thing.”

A wide range of quantum impacts

The “Quantum Index Report” team considered activity in the quantum sector through a broad range of perspectives, from both publicly available data and novel, original data. The entire report, raw data, and data visualizations are available on an interactive website. 

While the research team acknowledges the still-nascent nature of the quantum computing field and some inherent bias in the 2025 research, including a U.S. focus, Ruane stressed that there is substantial market momentum underway.

Insights from the “Quantum Index Report 2025” include the following:

Quantum processor performance is improving, with the U. S. leading the field. Two-dozen manufacturers are now commercially offering more than 40 quantum processing units (QPUs), which are the processing hardware for a quantum computer. This is an indicator that the technology is becoming more accessible to business. While there have been impressive advancements in performance, QPUs do not yet meet the requirements for running large-scale commercial applications such as chemical simulations or cryptanalysis.

Quantum technology patents are soaring, with the total number increasing fivefold from 2014 to 2024. Corporations and universities are spearheading innovation efforts, accounting for 91% of the patents filed, with corporations holding 54% and universities 37%. China held 60% of quantum patents as of 2024, followed by the U.S. and Japan.

Venture capital funding for quantum technology reached a new high point in 2024. Quantum computing firms received the most funding ($1.6 billion in publicly announced investments), followed by quantum software companies at $621 million. The researchers note that quantum received less than 1% of total venture capital funding worldwide.

Businesses are buzzing over quantum computing.The report tracks how often the technology was mentioned across more than 50,000 corporate communication vehicles, including press releases and earnings calls, from 2022 to 2024. There was a significant uptick in mentions each quarter in 2024, with the frequency outpacing that of previous years by a substantial margin. The researchers said that this positively correlates with the maturing of the quantum market and the growing presence of quantum technology in mainstream business discourse.  

Quantum skills and training are growing in importance as companies begin to focus on workforce development. The demand for quantum skills has nearly tripled since 2018, according to the report. In response, universities are establishing quantum hubs and standing up programs that connect business leaders with researchers. 

“What we are seeing here is rapid progress and developments across a range of vectors — not just the improvement of technology benchmarks or the performance of quantum processing units,” Ruane said. “We are also seeing impact across a wide range of areas that are important to business leaders. It sends a signal that there’s breadth and depth in development.”

READ THE “QUANTUM INDEX REPORT 2025” 

Article link: https://mitsloan.mit.edu/ideas-made-to-matter/new-mit-report-captures-state-quantum-computing?

Today, we’re announcing a major algorithmic breakthrough published in Nature Magazine that marks a significant step toward the first beyond classical, verifiable real-world application of quantum computing.

Google Research’s Quantum AI team has demonstrated the first-ever verifiable quantum advantage running the out-of-order time correlator (OTOC) algorithm, which we call Quantum Echoes.

This is the first time any quantum computer has successfully run a verifiable algorithm on hardware that surpasses the ability of classical supercomputers.

What you should know about Quantum Echoes ✨ :

✨ Verifiable Advantage: The algorithm calculates a specific, predictable value, meaning its result can be verified by another quantum computer of similar caliber. This contrasts with non-verifiable random sampling experiments.

✨ Scale and Speed: Quantum Echoes are useful in learning the structure of quantum systems, from molecules to magnets to black holes, and we’ve demonstrated it runs 13,000 times faster on our Willow quantum processor than the best classical algorithm on a supercomputer. This beyond-classical performance is enabled by the low error rates and long coherence times of our hardware.

✨ Near-Term Application:  A separate proof-of-principle experiment showed how data from Nuclear Magnetic Resonance (NMR) can be used to gain more information about chemical structure than existing methods, opening an avenue for a near-term application only possible on quantum computers.

Quantum computing enhanced NMR could become a powerful tool in drug discovery, helping determine how potential medicines bind to their targets, or in materials science for characterizing the molecular structure of new materials like polymers, battery components or even the materials that comprise our quantum bits (qubits).

We remain focused on scaling our systems toward a full-scale, error-corrected quantum computer. Now, we’re focused on achieving Milestone 3 on our quantum hardware roadmap, a long-lived logical qubit.

Bravo to the Google Research Quantum AI team!

More in the blog by Vadim Smelyanskiy and Hartmut Neven: https://lnkd.in/dg8n7UiV

The Nature paper: https://lnkd.in/dGCgar4z

Technical blog on verifiable quantum advantage by Xiao Mi and Kostyantyn Kechedzhi: https://goo.gle/3JiHUc7

New paper on Quantum computation of molecular geometry via many-body nuclear spin echoes: https://lnkd.in/d-pTxba3

Article link: https://www.linkedin.com/posts/yossimatias_today-were-announcing-a-major-algorithmic-activity-7386780979618738176-Myh9?

Challenge accepted.

This was a question I got recently in a Q&A. I tried to channel my inner Hemingway. Big ideas, small words and short sentences!

So if you fancy learning something new today – here’s my take, and some useful resources worth checking out if you want a deeper dive.

⬇️

Imagine a computer that doesn’t just think in ones and zeros, like the ones we use today. A quantum computer uses “qubits” instead of bits. A bit can be a 1 or a 0. But a qubit can be both at the same time — this is called “superposition”. It’s like flipping a coin and having it be heads and tails until you look.
 
Quantum computers also use something called entanglement. When two qubits are entangled, what happens to one instantly affects the other, even if they’re far apart. This lets quantum computers connect ideas in powerful new ways.
 
Because of superposition and entanglement, a quantum computer can explore many answers at once instead of one by one. That makes it super fast for some problems. It could help discover new medicines, protect data (search “quantum safe”), fight climate change, or even train smarter (ethical) AI.
 
But quantum computers are very hard to build. Qubits are delicate and can lose their power if they get too hot or too noisy. Scientists all over the world are racing to make them stronger and more stable. Quantum computers have to be kept at extremely low temperatures (-459°F) which is even colder than in outer space!
 
If they succeed, quantum computers could solve problems so big that today’s fastest supercomputers would take thousands of years to finish. Quantum computers won’t replace classical computers – but they will help us to solve many problems that we’ve never been able to solve before.
 
Quantum computers are not just faster – they give us a whole new way to understand the world.

[263 words / 2 minutes]

⬇️

Want a Deeper Dive?

🥶 WATCH: Quantum computers exaplained by MKBHD [17 mins]
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📒 READ: Wired’s Easy Guide to Quantum Computing – Why It Works & How It Could Change The World
https://lnkd.in/eiuAHxnQ

📖 FREE book “The Quantum Decade” from IBM Institute for Business Value
https://lnkd.in/ejMCnKTX

🗺️ FUTURE: The Next 5 Years? Technology Atlas by IBM
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Article link: https://www.linkedin.com/posts/jeremypaulwaite_tell-me-about-quantum-computing-in-2-minutes-activity-7384129167895990272-XsbQ?

In a new study, MIT researchers evaluated quantum materials’ potential for scalable commercial success — and identified promising candidates.

Zach Winn | MIT News

Publication Date:

October 15, 2025

People tend to think of quantum materials — whose properties arise from quantum mechanical effects — as exotic curiosities. But some quantum materials have become a ubiquitous part of our computer hard drives, TV screens, and medical devices. Still, the vast majority of quantum materials never accomplish much outside of the lab.

What makes certain quantum materials commercial successes and others commercially irrelevant? If researchers knew, they could direct their efforts toward more promising materials — a big deal since they may spend years studying a single material.

Now, MIT researchers have developed a system for evaluating the scale-up potential of quantum materials. Their framework combines a material’s quantum behavior with its cost, supply chain resilience, environmental footprint, and other factors. The researchers used their framework to evaluate over 16,000 materials, finding that the materials with the highest quantum fluctuation in the centers of their electrons also tend to be more expensive and environmentally damaging. The researchers also identified a set of materials that achieve a balance between quantum functionality and sustainability for further study.

The team hopes their approach will help guide the development of more commercially viable quantum materials that could be used for next generation microelectronics, energy harvesting applications, medical diagnostics, and more.

“People studying quantum materials are very focused on their properties and quantum mechanics,” says Mingda Li, associate professor of nuclear science and engineering and the senior author of the work. “For some reason, they have a natural resistance during fundamental materials research to thinking about the costs and other factors. Some told me they think those factors are too ‘soft’ or not related to science. But I think within 10 years, people will routinely be thinking about cost and environmental impact at every stage of development.”

The paper appears in Materials Today. Joining Li on the paper are co-first authors and PhD students Artittaya Boonkird, Mouyang Cheng, and Abhijatmedhi Chotrattanapituk, along with PhD students Denisse Cordova Carrizales and Ryotaro Okabe; former graduate research assistants Thanh Nguyen and Nathan Drucker; postdoc Manasi Mandal; Instructor Ellan Spero of the Department of Materials Science and Engineering (DMSE); Professor Christine Ortiz of the Department of DMSE; Professor Liang Fu of the Department of Physics; Professor Tomas Palacios of the Department of Electrical Engineering and Computer Science (EECS); Associate Professor Farnaz Niroui of EECS; Assistant Professor Jingjie Yeo of Cornell University; and PhD student Vsevolod Belosevich and Assostant Professor Qiong Ma of Boston College.

Materials with impact

Cheng and Boonkird say that materials science researchers often gravitate toward quantum materials with the most exotic quantum properties rather than the ones most likely to be used in products that change the world.

“Researchers don’t always think about the costs or environmental impacts of the materials they study,” Cheng says. “But those factors can make them impossible to do anything with.”

Li and his collaborators wanted to help researchers focus on quantum materials with more potential to be adopted by industry. For this study, they developed methods for evaluating factors like the materials’ price and environmental impact using their elements and common practices for mining and processing those elements. At the same time, they quantified the materials’ level of “quantumness” using an AI model created by the same group last year, based on a concept proposed by MIT professor of physics Liang Fu, termed quantum weight.

“For a long time, it’s been unclear how to quantify the quantumness of a material,” Fu says. “Quantum weight is very useful for this purpose. Basically, the higher the quantum weight of a material, the more quantum it is.”

The researchers focused on a class of quantum materials with exotic electronic properties known as topological materials, eventually assigning over 16,000 materials scores on environmental impact, price, import resilience, and more.

For the first time, the researchers found a strong correlation between the material’s quantum weight and how expensive and environmentally damaging it is.

“That’s useful information because the industry really wants something very low-cost,” Spero says. “We know what we should be looking for: high quantum weight, low-cost materials. Very few materials being developed meet that criteria, and that likely explains why they don’t scale to industry.”

The researchers identified 200 environmentally sustainable materials and further refined the list down to 31 material candidates that achieved an optimal balance of quantum functionality and high-potential impact.

The researchers also found that several widely studied materials exhibit high environmental impact scores, indicating they will be hard to scale sustainably. “Considering the scalability of manufacturing and environmental availability and impact is critical to ensuring practical adoption of these materials in emerging technologies,” says Niroui.

Guiding research

Many of the topological materials evaluated in the paper have never been synthesized, which limited the accuracy of the study’s environmental and cost predictions. But the authors say the researchers are already working with companies to study some of the promising materials identified in the paper.

“We talked with people at semiconductor companies that said some of these materials were really interesting to them, and our chemist collaborators also identified some materials they find really interesting through this work,” Palacios says. “Now we want to experimentally study these cheaper topological materials to understand their performance better.”

“Solar cells have an efficiency limit of 34 percent, but many topological materials have a theoretical limit of 89 percent. Plus, you can harvest energy across all electromagnetic bands, including our body heat,” Fu says. “If we could reach those limits, you could easily charge your cell phone using body heat. These are performances that have been demonstrated in labs, but could never scale up. That’s the kind of thing we’re trying to push forward.”

This work was supported, in part, by the National Science Foundation and the U.S. Department of Energy.

Article link: https://news.mit.edu/2025/why-some-quantum-materials-stall-while-others-scale-1015

You already know that agents and small language models are the next big things. Here are five other hot trends you should watch out for this year.

By James O’Donnell, Will Douglas Heaven, &Melissa Heikkilä

January 8, 2025

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

For the last couple of years we’ve had a go at predicting what’s coming next in AI. A fool’s game given how fast this industry moves. But we’re on a roll, and we’re doing it again.

How did we score last time round? Our four hot trends to watch out for in 2024 included what we called customized chatbots—interactive helper apps powered by multimodal large language models (check: we didn’t know it yet, but we were talking about what everyone now calls agents, the hottest thing in AI right now); generative video (check: few technologies have improved so fast in the last 12 months, with OpenAI and Google DeepMind releasing their flagship video generation models, Sora and Veo, within a week of each other this December); and more general-purpose robots that can do a wider range of tasks (check: the payoffs from large language models continue to trickle down to other parts of the tech industry, and robotics is top of the list). 

We also said that AI-generated election disinformation would be everywhere, but here—happily—we got it wrong. There were many things to wring our hands over this year, but political deepfakes were thin on the ground. 

So what’s coming in 2025? We’re going to ignore the obvious here: You can bet that agentsand smaller, more efficient, language modelswill continue to shape the industry. Instead, here are five alternative picks from our AI team.

1. Generative virtual playgrounds 

If 2023 was the year of generative images and 2024 was the year of generative video—what comes next? If you guessed generative virtual worlds (a.k.a. video games), high fives all round.

2. Large language models that “reason”

The buzz was justified. When OpenAI revealed o1 in September, it introduced a new paradigm in how large language models work. Two months later, the firm pushed that paradigm forward in almost every way with o3—a model that just might reshape this technology for good. 

Most models, including OpenAI’s flagship GPT-4, spit out the first response they come up with. Sometimes it’s correct; sometimes it’s not. But the firm’s new models are trained to work through their answers step by step, breaking down tricky problems into a series of simpler ones. When one approach isn’t working, they try another. This technique, known as “reasoning” (yes—we know exactly how loaded that term is), can make this technology more accurate, especially for math, physics, and logic problems.

It’s also crucial for agents.

In December, Google DeepMind revealed an experimental new web-browsing agent called Mariner. In the middle of a preview demo that the company gave to MIT Technology Review, Mariner seemed to get stuck. Megha Goel, a product manager at the company, had asked the agent to find her a recipe for Christmas cookies that looked like the ones in a photo she’d given it. Mariner found a recipe on the web and started adding the ingredients to Goel’s online grocery basket.

Then it stalled; it couldn’t figure out what type of flour to pick. Goel watched as Mariner explained its steps in a chat window: “It says, ‘I will use the browser’s Back button to return to the recipe.’”

It was a remarkable moment. Instead of hitting a wall, the agent had broken the task down into separate actions and picked one that might resolve the problem. Figuring out you need to click the Back button may sound basic, but for a mindless bot it’s akin to rocket science. And it worked: Mariner went back to the recipe, confirmed the type of flour, and carried on filling Goel’s basket.

Google DeepMind is also building an experimental version of Gemini 2.0, its latest large language model, that uses this step-by-step approach to problem solving, called Gemini 2.0 Flash Thinking.

But OpenAI and Google are just the tip of the iceberg. Many companies are building large language models that use similar techniques, making them better at a whole range of tasks, from cooking to coding. Expect a lot more buzz about reasoning (we know, we know) this year.

—Will Douglas Heaven

3. It’s boom time for AI in science 

One of the most exciting uses for AI is speeding up discovery in the natural sciences. Perhaps the greatest vindication of AI’s potential on this front came last October, when the Royal Swedish Academy of Sciences awarded the Nobel Prize for chemistry to Demis Hassabis and John M. Jumper from Google DeepMind for building the AlphaFold tool, which can solve protein folding, and to David Baker for building tools to help design new proteins.

Expect this trend to continue next year, and to see more data sets and models that are aimed specifically at scientific discovery. Proteins were the perfect target for AI, because the field had excellent existing data sets that AI models could be trained on. 

The hunt is on to find the next big thing. One potential area is materials science. Meta has released massive data sets and models that could help scientists use AI to discover new materials much faster, and in December, Hugging Face, together with the startup Entalpic, launched LeMaterial, an open-source project that aims to simplify and accelerate materials research. Their first project is a data set that unifies, cleans, and standardizes the most prominent material data sets. 

AI model makers are also keen to pitch their generative products as research tools for scientists. OpenAI let scientists test its latest o1 model and see how it might support them in research. The results were encouraging. 

Having an AI tool that can operate in a similar way to a scientist is one of the fantasies of the tech sector. In a manifesto published in October last year, Anthropic founder Dario Amodei highlighted science, especially biology, as one of the key areas where powerful AI could help. Amodei speculates that in the future, AI could be not only a method of data analysis but a “virtual biologist who performs all the tasks biologists do.” We’re still a long way away from this scenario. But next year, we might see important steps toward it. 

—Melissa Heikkilä

4. AI companies get cozier with national security

There is a lot of money to be made by AI companies willing to lend their tools to border surveillance, intelligence gathering, and other national security tasks. 

The US military has launched a number of initiatives that show it’s eager to adopt AI, from the Replicator program—which, inspired by the war in Ukraine, promises to spend $1 billion on small drones—to the Artificial Intelligence Rapid Capabilities Cell, a unit bringing AI into everything from battlefield decision-making to logistics. European militaries are under pressure to up their tech investment, triggered by concerns that Donald Trump’s administration will cut spending to Ukraine. Rising tensions between Taiwan and China weigh heavily on the minds of military planners, too. 

In 2025, these trends will continue to be a boon for defense-tech companies like Palantir, Anduril, and others, which are now capitalizing on classified military data to train AI models. 

The defense industry’s deep pockets will tempt mainstream AI companies into the fold too. OpenAI in December announced it is partnering with Anduril on a program to take down drones, completing a year-long pivotaway from its policy of not working with the military. It joins the ranks of Microsoft, Amazon, and Google, which have worked with the Pentagon for years. 

Other AI competitors, which are spending billions to train and develop new models, will face more pressure in 2025 to think seriously about revenue. It’s possible that they’ll find enough non-defense customers who will pay handsomely for AI agents that can handle complex tasks, or creative industries willing to spend on image and video generators. 

But they’ll also be increasingly tempted to throw their hats in the ring for lucrative Pentagon contracts. Expect to see companies wrestle with whether working on defense projects will be seen as a contradiction to their values. OpenAI’s rationale for changing its stance was that “democracies should continue to take the lead in AI development,” the company wrote, reasoning that lending its models to the military would advance that goal. In 2025, we’ll be watching others follow its lead. 

—James O’Donnell

5. Nvidia sees legitimate competition

For much of the current AI boom, if you were a tech startup looking to try your hand at making an AI model, Jensen Huang was your man. As CEO of Nvidia, the world’s most valuable corporation, Huang helped the company become the undisputed leader of chips used both to train AI models and to ping a model when anyone uses it, called “inferencing.”

A number of forces could change that in 2025. For one, behemoth competitors like Amazon, Broadcom, AMD, and others have been investing heavily in new chips, and there are early indications that these could compete closely with Nvidia’s—particularly for inference, where Nvidia’s lead is less solid. 

A growing number of startups are also attacking Nvidia from a different angle. Rather than trying to marginally improve on Nvidia’s designs, startups like Groq are making riskier bets on entirely new chip architectures that, with enough time, promise to provide more efficient or effective training. In 2025 these experiments will still be in their early stages, but it’s possible that a standout competitor will change the assumption that top AI models rely exclusively on Nvidia chips.

Underpinning this competition, the geopolitical chip war will continue. That war thus far has relied on two strategies. On one hand, the West seeks to limit exports to China of top chips and the technologies to make them. On the other, efforts like the US CHIPS Act aim to boost domestic production of semiconductors.

Donald Trump may escalate those export controls and has promised massive tariffs on any goods imported from China. In 2025, such tariffs would put Taiwan—on which the US relies heavily because of the chip manufacturer TSMC—at the center of the trade wars. That’s because Taiwan has said it will help Taiwanese firms operating in China relocate back to the island to help them avoid the proposed tariffs. That could draw further criticism from Trump, who has expressed frustration with US spending to defend Taiwan from China. 

It’s unclear how these forces will play out, but it will only further incentivize chipmakers to reduce reliance on Taiwan, which is the entire purpose of the CHIPS Act. As spending from the bill begins to circulate, next year could bring the first evidence of whether it’s materially boosting domestic chip production. 

—James O’Donnell

Correction: we have clarified that Taiwan’s Economy Minister was talking about Taiwanese firms being relocated back to Taiwan.

Article link: https://www-technologyreview-com.cdn.ampproject.org/c/s/www.technologyreview.com/2025/01/08/1109188/whats-next-for-ai-in-2025/amp/

By Jowi Morales published October 2, 2025

That’s an over 55,000% increase in operational 

A group of physicists from Harvard and MIT just built a quantum computer that ran continuously for more than two hours. Although it doesn’t sound like much versus regular computers (like servers that run 24/7 for months, if not years), this is a huge breakthrough in quantum computing. As reported by The Harvard Crimson, most current quantum computers run for only a few milliseconds, with record-breaking machines only able to operate for a little over 10 seconds.

Although two hours is still a bit limited, researchers say that the concept behind this could allow future quantum computers to run for much longer, maybe even indefinitely. “There is still a way to go and scale from where we are now,” says research associate Tout T. Wang, “But the roadmap is now clear based on the breakthrough experiments that we’ve done here at Harvard.”

The main difference between “regular” and quantum computing is that the latter uses qubits, which are subatomic particles, to hold and process data. But unlike the former, which retain information even without power, quantum computers can lose these qubits in a process called “atom loss”. This results in information loss and eventually system failure.

The research team addressed this by developing the “optical lattice conveyor belt” and “optical tweezers” to replace qubits as they’re lost. This system has 3,000 qubits and allows them to inject 300,000 atoms per second into the quantum computer, overcoming the qubit loss. “There’s now fundamentally nothing limiting how long our usual atom and quantum computers can run for,” said Wang. “Even if atoms get lost with a small probability, we can bring fresh atoms in to replace them and not affect the quantum information being stored in the system.”

Other team members believe that this breakthrough will allow us to have quantum computers that can run forever in about three years. Before this, experts said that it was at least half a decade away, if not longer. Quantum computing has the potential to change the way we do computing, breaking barriers in cryptography, finance, medicine, and more. However, despite these advancements, it’s unlikely that we’ll have personal quantum computers in our living rooms and offices within the next decade, unless you’re a physicist or researcher working on these cutting-edge devices.

Article link: https://www.tomshardware.com/tech-industry/quantum-computing/harvard-researchers-hail-quantum-computing-breakthrough-with-machine-that-can-run-for-two-hours-atomic-loss-quashed-by-experimental-design-systems-that-can-run-forever-just-3-years-away

Maybe it’s magic, maybe it’s math—nobody can decide.

By Melissa Heikkilä

July 16, 2024

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

It’s the simplest questions that are often the hardest to answer. That applies to AI, too. Even though it’s a technology being sold as a solution to the world’s problems, nobody seems to know what it really is. It’s a label that’s been slapped on technologies ranging from self-driving cars to facial recognition, chatbots to fancy Excel. But in general, when we talk about AI, we talk about technologies that make computers do things we think need intelligence when done by people. 

For months, my colleague Will Douglas Heaven has been on a quest to go deeper to understand why everybody seems to disagree on exactly what AI is, why nobody even knows, and why you’re right to care about it. He’s been talking to some of the biggest thinkers in the field, asking them, simply: What is AI? It’s a great piece that looks at the past and present of AI to see where it is going next. You can read it here. 

Here’s a taste of what to expect: 

Artificial intelligence almost wasn’t called “artificial intelligence” at all. The computer scientist John McCarthy is credited with coming up with the term in 1955 when writing a funding application for a summer research program at Dartmouth College in New Hampshire. But more than one of McCarthy’s colleagues hated it. “The word ‘artificial’ makes you think there’s something kind of phony about this,” said one. Others preferred the terms “automata studies,” “complex information processing,” “engineering psychology,” “applied epistemology,” “neural cybernetics,”  “non-numerical computing,” “neuraldynamics,” “advanced automatic programming,” and “hypothetical automata.” Not quite as cool and sexy as AI.

AI has several zealous fandoms. AI has acolytes, with a faith-like belief in the technology’s current power and inevitable future improvement. The buzzy popular narrative is shaped by a pantheon of big-name players, from Big Tech marketers in chief like Sundar Pichai and Satya Nadella to edgelords of industry like Elon Musk and Sam Altman to celebrity computer scientists like Geoffrey Hinton. As AI hype has ballooned, a vocal anti-hype lobby has risen in opposition, ready to smack down its ambitious, often wild claims. As a result, it can feel as if different camps are talking past one another, not always in good faith.

This sometimes seemingly ridiculous debate has huge consequences that affect us all. AI has a lot of big egos and vast sums of money at stake. But more than that, these disputes matter when industry leaders and opinionated scientists are summoned by heads of state and lawmakers to explain what this technology is and what it can do (and how scared we should be). They matter when this technology is being built into software we use every day, from search engines to word-processing apps to assistants on your phone. AI is not going away. But if we don’t know what we’re being sold, who’s the dupe?

For example, meet the TESCREALists. A clunky acronym (pronounced “tes-cree-all”) replaces an even clunkier list of labels: transhumanism, extropianism, singularitarianism, cosmism, rationalism, effective altruism, and longtermism. It was coined by Timnit Gebru, who founded the Distributed AI Research Institute and was Google’s former ethical AI co-lead, and Émile Torres, a philosopher and historian at Case Western Reserve University. Some anticipate human immortality; others predict humanity’s colonization of the stars. The common tenet is that an all-powerful technology is not only within reach but inevitable. TESCREALists believe that artificial general intelligence, or AGI, could not only fix the world’s problems but level up humanity. Gebru and Torres link several of these worldviews—with their common focus on “improving” humanity—to the racist eugenics movements of the 20th century.

Is AI math or magic? Either way, people have strong, almost religious beliefs in one or the other. “It’s offensive to some people to suggest that human intelligence could be re-created through these kinds of mechanisms,” Ellie Pavlick, who studies neural networks at Brown University, told Will. “People have strong-held beliefs about this issue—it almost feels religious. On the other hand, there’s people who have a little bit of a God complex. So it’s also offensive to them to suggest that they just can’t do it.”

Will’s piece really is the definitive look at this whole debate. No spoilers—there are no simple answers, but lots of fascinating characters and viewpoints. I’d recommend you read the whole thing here—and see if you can make your mind up about what AI really is.

Article link: https://www-technologyreview-com.cdn.ampproject.org/c/s/www.technologyreview.com/2024/07/16/1095001/a-short-history-of-ai-and-what-it-is-and-isnt/amp/