healthcarereimagined

Despite billion-dollar investments, the technology faces hurdles that keeps its future uncertain

by Elizabeth Walsh

November 13, 2025 

KEY INSIGHTS

• Chemistry problems could be among the first to be solved via quantum computers.
• Companies are already demonstrating how the technology can solve chemistry problems.
• Applications important to industry will require millions of qubits, but other challenges must first be overcome.

At the 2025 Quantum World Congress, a glittering bronze chandelier with protruding wires hung suspended in a glass case. Around it, a crowd queued to snap photos. For many people there, it was the closest they’ve ever come to a quantum computer.

The meeting, held in September just outside Washington, DC, attracted hundreds of researchers, investors, and executives to talk about technology that could revolutionize computing. But throughout the presentations of breakthroughs that are bringing the potential of quantum computing closer to reality, an uncomfortable truth often went unspoken: the exotic-looking quantum machines have yet to outperform their classical counterparts.

For decades, quantum computing has promised advances in fields as varied as cryptography, navigation, and optimization. And the field where it could bring advantages over classical computing soonest is chemistry. Chemical problems are suited to the technology because molecules are themselves quantum systems. And, in theory, the computers could simulate any part of a quantum system’s behavior.

Artificial intelligence is a once speculative technology that has become crucial in chemistry, and quantum computing is following a similar trajectory, says Alán Aspuru-Guzik, a professor of chemistry at the University of Toronto and senior director of quantum chemistry at the computer chip giant Nvidia. It took decades for AI to go from an uncertain future to a multibillion-dollar industry. Quantum computing may require a similarly long runway between early funding and commercial adoption, he says.

But while the billions of dollars in investment pouring into the quantum computing market this year underline quantum’s potential, experts say the technology has yet to bring practical benefits. The runway is strewn with both hardware and software problems that need to be remedied before the promise can ever be realized. Profits for builders of the computers and the companies that might use them could still be decades away.

What is a quantum computer?

First proposed in the early 1980s by physicist Richard Feynman, quantum computers harness the principles of quantum mechanics—wave-particle duality, superposition, entanglement, and the uncertainty principle—to solve problems. Using qubits—units of information that can be built from different materials and exist in multiple states at once—a quantum computer can process a lot of data in parallel.

Classical computers can model small numbers of qubits by brute-force calculation, but the resources required to crunch those numbers grow exponentially with every added qubit. Classical computers quickly fall behind.

In 1998, a team of researchers from the University of California, Berkeley; Massachusetts Institute of Technology; and Los Alamos National Laboratory built the first quantum computer using only 2 qubits. Today’s devices, made by a handful of companies, now reach 100 or more qubits, which are contained on chips that resemble those of classical computers.

A team at the Cleveland Clinic has modeled the solvent effects of methanol, ethanol, and methylamine using quantum hardware with an algorithm that samples electron energy. But the model still struggles to capture weak forces like hydrogen bonding and dispersion, says Kenneth Merz, a quantum molecular scientist at the institution who was involved in the study.

Researchers are also developing algorithms that address specific chemical problems. Using a new quantum algorithm, scientists at the University of Sydney achieved the first quantum simulation of chemical dynamics, modeling how a molecule’s structure evolves over time rather than just its static state The quantum computer company IonQ developed a mixed quantum-classical algorithm capable of accurately computing the forces between atoms, and Google recently announced an algorithm that could someday be used for analyzing nuclear magnetic resonance data.

The South Korean quantum algorithm start-up Qunova Computing has built a faster, more accurate version of VQE, says CEO Kevin Rhee. With it, Qunova modeled nitrogen reactions in molecules important for nitrogen fixation. In tests, the method was almost nine times as fast as one done on a classical computer, he says.

Quantum computers are also beginning to be used to model proteins. With the aid of classical processors, a 16-qubit computer found potential drugs that inhibit KRAS, a protein linked to many cancers. And IonQ and the software company Kipu Quantum simulated the folding of a 12-amino-acid chain—the largest protein-folding demonstration on quantum hardware to date.

Quantum’s advantage is still elusive

But many of these use cases don’t claim quantum advantage—the idea that a task can be done better, faster, or cheaper than with classical methods. And the problems being worked on now are too narrow to benefit industry, Merck’s Harbach says. “For academia, it’s about proving the technology. For us, it’s proving the value,” he says.

Many algorithms are restricted in what they can do because getting many qubits to work together is still difficult, Kais says. While companies like Qunova claim their algorithms can successfully handle up to 200 qubits, many chemistry problems will require substantially more.

“For academia, it’s about proving the technology. For us, it’s proving the value.”Philipp Harbach, head of digital innovation at the group science and technology office, Merck KGaA

Modeling cytochrome P450 enzymes or iron-molybdenum cofactor (FeMoco) are the kinds of tasks industrial researchers would like to see quantum computing take on. These are complex metalloenzymes that are important to metabolism and nitrogen fixation, respectively, and are difficult for classical computers to simulate.

In 2021, Google estimated that about 2.7 million physical qubits would be needed to model FeMoco; other studies around that time made similar estimates for P450. The French start-up Alice & Bob announced in October that its qubits could reduce the total requirement to a little under 100,000, still far more than what today’s hardware and algorithms can offer.

Tasks such as simulating large biomolecules and designing novel polymers, battery materials, superconductors, and catalysts each would require a similar number of qubits. But scaling quantum systems isn’t easy, because qubits are extremely fragile and easily lose their quantum states.

In the meantime, some companies are developing “quantum-inspired” algorithms, which take techniques that work on a quantum computer and run them on a classical one to solve similar problems, IBM’s Garcia says. Fujitsu, for example, is creating quantum-inspired software to discover a new catalyst for clean hydrogen production, and Toshiba is making optimization algorithms for choosing the best answer in a large dataset. But the inspired algorithms can’t fully replicate a quantum computer, she says.

The fault in our quantum computers

The reason quantum computers have largely failed to do anything better than a classical computer comes down to how qubits work, says Philipp Ernst, head of solutions at PsiQuantum, a quantum computer developer.

The number of qubits in a quantum computer matters. The more qubits it has, the greater its processing power and potential to run complex algorithms that solve larger problems. But qubit quality is just as important: a quantum computer may contain many qubits, but if they’re unstable or can’t interact with one another, the computer doesn’t have much practical use, Ernst says.

Scientists say we are in the noisy intermediate-scale quantum (NISQ) era, which is characterized by low qubit counts and a sensitivity to the environment that causes high error rates and makes computers unreliable.

Quantum noise comes from a lot of places, including thermal fluctuations, electromagnetic interference, material disturbances, and other interactions with the environment. Any amount of noise can mess with qubits by causing them to lose either their superposition or their entanglement.

“Quantum computing is essentially where conventional computing was in the ’60s or ’70s . . . nobody at that point in time came up with or could imagine how AI would be run and used today.”Philipp Ernst, head of quantum solutions, PsiQuantum

For problems like modeling protein folding, noisy qubits are a big issue. “If you try to model 10–15 amino acids, all hell breaks loose. The errors kill you,” Cleveland Clinic researcher Merz says.

Rather than trying to make machines that compensate for errors by piling on the qubits, researchers are pivoting to fault-tolerant computers that can detect and correct errors. These machines use many physical qubits to form a single logical qubit, which stores information redundantly so that if one qubit fails, the system can correct it before data are lost and errors build up.

“You basically need error correction, and you need several hundred logical qubits in order to do something useful,” Ernst says.

Quantum computer companies have made steps toward fault tolerance. Quantinuum recently reported simulating the ground-state energy of hydrogen using error-corrected logical qubits. Microsoft, working in partnership with Quantinuum, demonstrated a record 12 logical qubits operating with high reliability. IBM is developing quantum error-correction codes to reduce the number of physical qubits needed, and Alice & Bob is innovating “cat qubits” that naturally resist certain types of errors.

Most of these companies say they will launch fault-tolerant quantum computers with enough qubits for complex calculations by 2030.

Companies are taking different approaches to make qubits, a unit of information that can be in multiple states at once.

Superconducting

Credit: SpinQ

Technology: Tiny circuits made from materials such as aluminum and niobium that carry current without resistance

Advantage: Run operations quickly

Drawback: Can lose their quantum state quickly

Companies: IBM, Microsoft, Google

Photonic

Credit: Xanadu Quantum Technologies

Technology: Single photons encoding information

Advantage: Stable and can work at room temperature

Drawback: Hard to control photons

Companies: PsiQuantum, Xanadu Quantum Technologies

Trapped ion

Credit: Quantinuum

Technology: Charged atoms suspended in a vacuum and controlled by electromagnetic fields

Advantage: Stable

Drawback: Slow operation speed and hard to scale

Companies: IonQ, Quantinuum

Spin or neutral atom

Credit: Intel

Technology: Atoms held by optical tweezers or single-electron spins confined in solid-state materials

Advantage: Scalable and can operate at temperatures slightly above absolute zero

Drawback: Still in early research phase

Companies: Academic laboratories only

Topological

Credit: Microsoft

Technology: Exotic quasiparticles in 2D materials to encode data

Advantage: Good at maintaining a quantum state

Drawback: Hard to build and still experimental

Companies: Microsoft

Fault tolerance isn’t enough

As the number of qubits grows, getting them to work together becomes harder. In many systems, qubits can easily interact only with their nearest neighbors, and linking distant ones on different chips takes extra steps that slow things down. Making accurate multiqubit gates and managing thousands of control signals to influence the qubits adds to the difficulty.

Quantum computers may one day solve problems faster than classical computers because they can process many possibilities at once, but currently, most quantum processors are actually slower than classical chips. Each step can take thousands of times longer than in modern classical computers.

And because qubits are extremely fragile, their environment must remain stable. Some types of qubits need to operate near absolute zero, and heat is generated as qubits are added. The large installations that are required to do complex calculations need a lot of space and energy to keep everything cold.

Not surprisingly, building a quantum computer isn’t cheap. A single qubit costs around $10,000. Some estimates place a large-scale quantum computer at tens of billions of dollars, while smaller ones that are not fault tolerant are in the mid-to-high tens of millions.

If you have a 100-qubit quantum computer and a classical computer that can solve the same problem, then you have to consider the cost, Qunova’s Rhee says. “The quantum computers will be 10 times or 100 times more expensive.”

Because of costs and space requirements, quantum computing is unfolding as a cloud service. IBM, Google, and Amazon already rent out early-stage quantum processors by the minute. To access IBM’s fleet of quantum computers, prices range from $48 to $96 per minute.

IBM quantum scientist Maika Takita works on a superconducting quantum computer.  Credit: IBM

And yet quantum is bringing in billions

Despite the lack of real-world industry applications, and cost and engineering challenges yet to be overcome, investment in quantum computing is robust. Kais says the fear of missing out on the next big thing, as many did in the early days of AI, might be driving investors.

The consulting firm McKinsey & Co. projects the industry could be worth anywhere from $28 billion to $72 billionby 2035, up from $750 million in 2024. In the first quarter of 2025 alone, investors poured $1.25 billion into quantum hardware and over $250 million into software, according to Quantum Insider, a market intelligence firm.

Industry leaders like Quantinuum (valued at $10 billion) and IonQ (valued at nearly $19 billion) continue to draw major backing, while newer companies such as PsiQuantum are raising hundreds of millions of dollars. Governments in the US, China, the European Union, and Japan are also ramping up multimillion-dollar programs to support the technology.

Chemical and pharmaceutical industry players are starting to invest too. In the chemical sector, BASF, Covestro, Johnson Matthey, and Mitsubishi Chemical are partnering with quantum computing vendors to explore materials simulation and catalysis. Major drug companies—including AstraZeneca, Bayer, Merck KGaA, Novartis, Pfizer, Roche, and Sanofi—have disclosed quantum initiatives ranging from drug discovery partnerships to internal quantum-algorithm work.

“If [a pharmaceutical company] hasn’t invested yet, they will,” Nvidia’s Aspuru-Guzik says.

But even with rapid progress in quantum computing hardware and software, it’s not yet clear when quantum advantage will arrive and how much it will achieve.

“Quantum computing is essentially where conventional computing was in the ’60s or ’70s . . . nobody at that point in time could imagine how AI would be run and used today,” Ernst says.

While most experts agree that “useful” quantum chemistry is still years away—likely beyond 2030, and possibly not until 2040—they are adamant it will happen.

“There aren’t any fundamental things which prevent us from building these machines . . . it’s more of an engineering type of problem,” North Carolina State’s Kais says. “‘I’m really optimistic that within maybe the next 5 years we’ll start seeing computers solve very, very complex problems.”

Linde Wester Hansen, the head of quantum applications at Alice & Bob, says chemical companies should begin preparing now. Even today, they could benefit from modeling smaller molecules with quantum tools, she says.

Yet quantum computers’ success in chemistry or any other field is not preordained. “It’s not clear that they will have any impact,” SandboxAQ’s Lewis says. “It could be that quantum computers are very expensive prime number factoring machines, or it could be this massive disruptive thing.”

Article link: https://cen.acs.org/business/quantum-computing-chemistrys-next-AI/103/web/2025/11

Ontology is having its moment. There was a time when we called it the “O word.” Nothing killed a conversation with business – or IT – faster than mentioning ontology. The rule was simple: deliver value, keep the ontology part quiet.

But things have changed.

Ontology is shaping up to be the buzzword of 2026. A big part of that is Palantir’s extraordinary rise – their entire Foundry platform is built on ontological modeling. Microsoft is now moving ontology into Fabric. The race is on.

Why now?

Because structured ontologies offer something generative AI desperately needs: grounding.

LLMs are creative but lack logical constraints. Ontologies provide the formal structure to anchor meaning and harmonize wildly different data sources into a coherent semantic layer. When you need precision over plausibility, ontologies are one of the most reliable ways to keep AI from hallucinating.

But what actually is an ontology?

The idea traces back 2,000 years to Aristotle, who tried to formalize how we perceive reality. Ontology today is that same craft: defining the concepts and relationships that shape our world in a logically rigorous way.

Fast forward to the early web. Tim Berners-Lee envisioned the Semantic Web – an internet where data, not just documents, was linked. Give every ontological concept a unique identity, he argued, and meaning becomes a first-class citizen of the web.

Google later operationalized part of this vision with Schema.org, creating a shared vocabulary to help search engines understand the public internet.

But the current frontier isn’t the public web – it’s the enterprise.

Companies want AI agents that can reason about their internal data – their customers, assets, processes. They need semantic clarity applied to their own messy, private reality. Disambiguating meaning isn’t just useful anymore. It’s essential.

Here’s the problem: you can’t outsource your ontology. Enterprises need their own internal version of schema.org – fully owned, built on open standards. Anything less means IP leakage and vendor lock-in. In the age of AI, your ontology is a key part of your competitive moat.

So, ontology is back. And this time it’s not theory – it’s critical infrastructure.

⭕ Your Schema.org: https://lnkd.in/eumPB3Hj

⭕ Your Ontology Your IP: https://lnkd.in/ersgR-Df

Article link: https://www.linkedin.com/posts/tonyseale_ontology-is-having-its-moment-there-was-activity-7400096205990494208-iLxo?

Introduction to Disconnected Systems Leading to Disconnected Care

Disconnected systems in healthcare undermine the promise of healthcare by failing to advance collaboration and coordination, resulting in providers being forced to make decisions without proper data. This causes gaps in care that directly affect safety and quality. Furthermore, patients often switch between different care facilities, yet their data often does not move with them. This results in disconnected care rather than a seamless, whole-person experience.

Fragmented EHRs Put Patient Safety at Risk

Fragmented EHR records create challenges that impact patients and providers. For instance, when information is incomplete or delayed, providers may accidentally order tests that were already ordered elsewhere or even miss crucial medication changes that should have affected their treatments. This lack of insight forces clinicians to rely on patient recall and manual workarounds, which ultimately are not reliable and increase the risk of preventable errors. Furthermore, for patients, these gaps translate into repeated procedures, inconsistent care plans, and frustration with having to navigate a system that often feels confusing and scattered.

Additionally, patient harm becomes even more crucial when data such as allergies, active prescriptions, or discharge instructions fail to update across systems. This goes past just a minor inconvenience and becomes an active risk to patient safety. For instance, this can cause medication errors, missed diagnoses, and delayed interventions. Furthermore, this is even more of a prominent issue within large federal health environments, where patients often receive care across multiple systems that operate independently rather than collaboratively.

How HITS Focuses on Reconnecting People & Data

HITS addresses this challenge by focusing on human-centered solutions that are built around real clinical workflows. This means actively engaging with stakeholders to understand how information fits into their daily routines and where breakdowns occur. By designing workflows so that the right information appears at the right moment, HITS helps to reduce cognitive burden and ensure that technology supports decision-making rather than hinders it. These refined workflows and standardized data exchange form the foundation for systems that promote collaboration in care. Furthermore, these redesigned workflows ensure that interoperability feels natural rather than forced.

Furthermore, HITS understands that technology alone is not enough without meaningful support, training, and communication. HITS does this by providing strategic communications and change management, which guide providers through new tools and processes with clear guidance, hands-on training, and continuous support. By prioritizing both end-user adoption and system integration, HITS ensures that organizations build environments where technology, workflow, and people all move together in the same direction.

Importance of Shifting Away from Disconnected Care

Disconnected systems continue to deliver disconnected care, directly affecting patients. This is especially critical for patients with chronic illnesses, who require day-to-day coordinated care. Therefore, HITS aims to combine standardized data exchange, intuitive provider workflows, and training, so that health systems transform fragmented environments into integrated systems where every provider has access to a complete picture before moving forward. This ensures that all patients receive care that is safer and coordinated.

HITS

HITS provides healthcare management services & works with users to develop health informatics tools that promote safe, secure, and reliable care experiences. We believe technology must be designed with empathy, accountability, transparency, and human-centered design at its core. By combining government expertise with healthcare management, we deliver collaborative, high-quality solutions across military, federal, and commercial sectors. We take pride in our services and settle for nothing other than 100% quality solutions for our clients. Having the right team assist with data sharing is crucial to encouraging collaborative and secure care. If you’re looking for the right team that delivers technology with heart, HITS is it! You can reach out to us directly at info@healthitsol.com. Check out this link if you’re interested in having a 15-minute consultation with us: https://bit.ly/3RLsRXR.

References

1. https://pmc.ncbi.nlm.nih.gov/articles/PMC10170908/
2. https://insiteone.com/healthcares-silent-crisis-how-fragmented-it-systems-compromise-patient-care/

Article link: https://healthinformationtechnologysolutions.com/disconnected-systems-lead-to-disconnected-care/

by

Kristin Burnham

 Apr 8, 2025

Why It Matters

From risk management policies to the five stages of AI ethics, here’s how some organizations approach ethical AI. Share 

Artificial intelligence is revolutionizing industries, by automating customer service, optimizing supply chains, personalizing marketing campaigns, and in countless other ways. But with great power comes great responsibility — and risks. 

Organizations today must work to ensure that the AI systems they build or implement are safe, secure, unbiased, and transparent, according to Thomas Davenport, a Babson University professor and visiting scholar at the MIT Initiative on the Digital Economy. 

During a recent webinar hosted by MIT Sloan Management Review, Davenport highlighted a number of ethical risks that AI can introduce to businesses. These include algorithmic bias in machine learning, varying levels of model transparency, cybersecurity vulnerabilities, and the possibility that AI will serve users insensitive or inappropriate content. Organizations must also contend with whether AI will deliver useful results. 

To counteract these risks, organizations need to embed ethical practices into AI solutions from the start, Davenport said. They also need to ensure that teams and individuals are engaged in ethical AI as a part of their everyday work. 

Here’s a look at several responsible AI strategies that organizations are using today and how businesses can progress from discussing AI ethics to taking action. 

AI strategies at Unilever and Scotiabank 

Companies are adopting a variety of strategies to integrate AI ethics into their operations, Davenport said, including appointing heads of AI ethics, performing research about the topic, conducting beta testing, and using external assessors to evaluate use cases.

Consumer packaged goods company Unilever, for example, created an AI assurance function that examines each new AI application to determine its risk level in terms of both effectiveness and ethics, Davenport said. This process requires individuals who propose a use case to fill out a questionnaire. An AI-based application then determines whether the use case is likely to be approved or whether there are underlying problems with the use case. This process ensures that AI models are aligned with ethical guidelines before deployment. 

Scotiabank developed an AI risk management policy and a data ethics team to advance a data ethics policy, Davenport said. The policies are part of the Canadian bank’s code of conduct, which all employees must agree to follow each year. The company also requires mandatory data ethics education for all employees working in the customer insights, data, and analytics organization or on other analytics teams. Scotiabank also worked with Deloitte to develop an automated ethics assistant — similar to Unilever’s — that reviews each use case before its deployed. The bank also involves employees in managing unstructured data to determine the most effective answers to customer questions.

“[This] kind of democratization of the process is important not only to your ethics, but also to your productivity as an organization in getting these systems up and running,” Davenport said.

5 stages of AI ethics

Davenport identified five stages that play crucial roles in fostering ethical AI development, deployment, and use within organizations. As companies advance through the stages, they move from talk to action, he noted. 

1. Evangelism. In this stage, representatives of the company speak internally and externally about the importance of AI ethics.
2. Policies. The company deliberates on and approves a set of corporate policies to ensure ethical approaches to AI are established.
3. Documentation. The company records data on each AI use case. This could include the use of model cards, which explain how models were designed to be used and how they have been evaluated.
4. Review. The company performs or sponsors a systematic review of each use case to determine whether it meets the company’s criteria for responsible AI.
5. Action. The company develops a process whereby each use case is either accepted as is, returned to the proposing owner for revision, or rejected.

Davenport said that it’s important for organizations to make strategic plans for integrating ethics into their AI strategy. “What use cases might make sense for us? We develop a model, we deploy the model, we monitor the model, and ethics come into place throughout that entire process,” he said. “That’s what the most successful organizations do with regard to AI ethics.”  

WATCH THE WEBINAR: HOW TO BUILD AN ETHICAL AI CULTURE

Article link: https://mitsloan.mit.edu/ideas-made-to-matter/how-organizations-build-a-culture-ai-ethics?

There’s an accelerating cat-and-mouse game between web publishers and AI crawlers, and we all stand to lose. 

By Shayne Longprearchive page

February 11, 2025

We often take the internet for granted. It’s an ocean of information at our fingertips—and it simply works. But this system relies on swarms of “crawlers”—bots that roam the web, visit millions of websites every day, and report what they see. This is how Google powers its search engines, how Amazon sets competitive prices, and how Kayak aggregates travel listings. Beyond the world of commerce, crawlers are essential for monitoring web security, enabling accessibility tools, and preserving historical archives. Academics, journalists, and civil societies also rely on them to conduct crucial investigative research.  

Crawlers are endemic. Now representing half of all internet traffic, they will soon outpace human traffic. This unseen subway of the web ferries information from site to site, day and night. And as of late, they serve one more purpose: Companies such as OpenAI use web-crawled data to train their artificial intelligence systems, like ChatGPT. 

Understandably, websites are now fighting back for fear that this invasive species—AI crawlers—will help displace them. But there’s a problem: This pushback is also threatening the transparency and open borders of the web, that allow non-AI applications to flourish. Unless we are thoughtful about how we fix this, the web will increasingly be fortified with logins, paywalls, and access tolls that inhibit not just AI but the biodiversity of real users and useful crawlers.

A system in turmoil 

To grasp the problem, it’s important to understand how the web worked until recently, when crawlers and websites operated together in relative symbiosis. Crawlers were largely undisruptive and could even be beneficial, bringing people to websites from search engines like Google or Bing in exchange for their data. In turn, websites imposed few restrictions on crawlers, even helping them navigate their sites. Websites then and now use machine-readable files, called robots.txt files, to specify what content they wanted crawlers to leave alone. But there were few efforts to enforce these rules or identify crawlers that ignored them. The stakes seemed low, so sites didn’t invest in obstructing those crawlers.

But now the popularity of AI has thrown the crawler ecosystem into disarray.

As with an invasive species, crawlers for AI have an insatiable and undiscerning appetite for data, hoovering up Wikipedia articles, academic papers, and posts on Reddit, review websites, and blogs. All forms of data are on the menu—text, tables, images, audio, and video. And the AI systems that result can (but not always will) be used in ways that compete directly with their sources of data. News sites fear AI chatbots will lure away their readers; artists and designers fear that AI image generators will seduce their clients; and coding forums fear that AI code generators will supplant their contributors. 

In response, websites are starting to turn crawlers away at the door. The motivator is largely the same: AI systems, and the crawlers that power them, may undercut the economic interests of anyone who publishes content to the web—by using the websites’ own data. This realization has ignited a series of crawler wars rippling beneath the surface.

The fightback

Web publishers have responded to AI with a trifecta of lawsuits, legislation, and computer science. What began with a litany of copyright infringement suits, including one from the New York Times, has turned into a wave of restrictions on use of websites’ data, as well as legislation such as the EU AI Act to protect copyright holders’ ability to opt out of AI training. 

However, legal and legislative verdicts could take years, while the consequences of AI adoption are immediate. So in the meantime, data creators have focused on tightening the data faucet at the source: web crawlers. Since mid-2023, websites have erected crawler restrictions to over 25% of the highest-quality data. Yet many of these restrictions can be simply ignored, and while major AI developers like OpenAI and Anthropic do claim to respect websites’ restrictions, they’ve been accused of ignoring them or aggressively overwhelmingwebsites (the major technical support forum iFixit is among those making such allegations).

Now websites are turning to their last alternative: anti-crawling technologies. A plethora of new startups (TollBit, ScalePost, etc), and web infrastructure companies like Cloudflare (estimated to support 20% of global web traffic), have begun to offer tools to detect, block, and charge nonhuman traffic. These tools erect obstacles that make sites harder to navigate or require crawlers to register.

These measures still offer immediate protection. After all, AI companies can’t use what they can’t obtain, regardless of how courts rule on copyright and fair use. But the effect is that large web publishers, forums, and sites are often raising the drawbridge to all crawlers—even those that pose no threat. This is even the case once they ink lucrative deals with AI companies that want to preserve exclusivity over that data. Ultimately, the web is being subdivided into territories where fewer crawlers are welcome.

How we stand to lose out

As this cat-and-mouse game accelerates, big players tend to outlast little ones.  Large websites and publishers will defend their content in court or negotiate contracts. And massive tech companies can afford to license large data sets or create powerful crawlers to circumvent restrictions. But small creators, such as visual artists, YouTube educators, or bloggers, may feel they have only two options: hide their content behind logins and paywalls, or take it offline entirely. For real users, this is making it harder to access news articles, see content from their favorite creators, and navigate the web without hitting logins, subscription demands, and captchas each step of the way.

Perhaps more concerning is the way large, exclusive contracts with AI companies are subdividing the web. Each deal raises the website’s incentive to remain exclusive and block anyone else from accessing the data—competitor or not. This will likely lead to further concentration of power in the hands of fewer AI developers and data publishers. A future where only large companies can license or crawl critical web data would suppress competition and fail to serve real users or many of the copyright holders.

Put simply, following this path will shrink the biodiversity of the web. Crawlers from academic researchers, journalists, and non-AI applications may increasingly be denied open access. Unless we can nurture an ecosystem with different rules for different data uses, we may end up with strict borders across the web, exacting a price on openness and transparency. 

While this path is not easily avoided, defenders of the open internet can insist on laws, policies, and technical infrastructure that explicitly protect noncompeting uses of web data from exclusive contracts while still protecting data creators and publishers. These rights are not at odds. We have so much to lose or gain from the fight to get data access right across the internet. As websites look for ways to adapt, we mustn’t sacrifice the open web on the altar of commercial AI.

Shayne Longpre is a PhD Candidate at MIT, where his research focuses on the intersection of AI and policy. He leads the Data Provenance Initiative.

Article link: https://www-technologyreview-com.cdn.ampproject.org/c/s/www.technologyreview.com/2025/02/11/1111518/ai-crawler-wars-closed-web/amp/

When IBM CEO Arvind Krishna says quantum computing is “three to five years away from shocking people,” it’s time to sit up and take notice.

I shared earlier highlights of the AI portion of Arvind’s discussion with Malcolm Gladwell. They also went into #quantum, which is getting closer to becoming a reality for enterprises.

3-5 years is 2028-30 — not so far into the future. And when he says “shocking,” he’s talking about:

• Making billions in the financial markets. Appr. $13 trillion moves through the financial markets each day. Even a 1 basis point improvement is $130 billion. Now you see why the recent paper by HSBC that “using quantum computer, bond trading was 34% more accurate than their prior technique” caused a ripple.
• Dramatic improvements in operational efficiency. “Let’s take a post office in a mid-sized country [that] … burns 1 billion gallons of fuel per year.” Optimizing last-mile delivery is the classic #travelingsalesman problem. But we can currently only get to 80% efficiency. With quantum, if we can get another 10%, that’s 100M gallons of fuel, which could drive hundreds of millions in savings.

“These are pretty attractive problems to go after.” Indeed.

Today’s challenge is scale. “Quantum computing today is where GPUs and AI were in 2015.” But if your business has challenges that are solvable with quantum, and you’re not starting to experiment with it now, you may risk being “out of business in 10 years.”

Quantum represents a “new kind of math” which enables us to solve “new kinds of problems.” For retailers and brands, I think about three things:

• Merchandise planning at the item x store level that’s accurate to within 1-2% of demand.
• Personalization at scale that actually delivers on the 1to1 promise we’ve been chasing for 25 years.
• The ability to monitor E2E (farm to home) supply chains and dynamically trigger IFTTT actions with little-to-no human intervention.
  (and i’m sure there are many others)

Arvind sees quantum as being “equal to semiconductors” in the ranking of technology advancements of the past 150 years. Yet it’s barely part of today’s conversations.

The internet had its #NetscapeMoment.
With AI, it was the launch of #ChatGPT.
Quantum will hit its #TippingPoint as well … and it will probably happen sooner than we think.

YouTube video — https://lnkd.in/eE9FdEU3
IBM Smart Talks — https://lnkd.in/eHhgTF7z
(also available on all major #podcast platforms)

Article link: https://www.linkedin.com/posts/karlhaller_quantum-travelingsalesman-netscapemoment-activity-7392561559732105217-J44C?

Most people don’t realize this, but the belief-to-reality ratio in quantum computing is completely upside down.

After four decades of work and hundreds of billions spent, we still do not have a single fault-tolerant quantum computer. Yet if you look around the ecosystem, about 80 to 90 percent of people are still believers. Maybe 5 to 10 percent are cautious skeptics. And roughly 1 percent are true non-believers who actually understand the physics well enough to see the dead ends coming.

Quantum systems collapse faster than they compute, and classical overhead grows faster than any claimed quantum advantage. That’s the bottleneck no roadmap, no marketing, and no optimism has ever solved.

The believers are not stupid. They are just trapped in three patterns.

• First, sunk cost. When so much money, reputation, and career prestige is tied to a dream, no one wants to be the first to say it failed.
• Second, optimism. People love the story that relentless effort will eventually bend nature to our will. Sometimes it does. Sometimes nature says no.
• Third, jargon. Quantum mechanics is weird enough that anything wrapped in the right vocabulary can sound plausible. Marketing teams take advantage of that.

I am on the other side for a simple reason. I do not care about narratives. I only care about physics and engineering. If there were a real path to scalable, fault-tolerant quantum computing, I would have spotted it, worked on it, and solved it already. There is no path. That is why the field relies on belief instead of reproducible results.

Quantum computing runs on hype, hope, and heroic storytelling.
My work runs on physics.

That is the real divide.

Read my other post to see why I don’t believe in quantum computing.
https://lnkd.in/ghQZvYfa

Article link: https://www.linkedin.com/posts/alan-shields-56963035a_the-quantum-mirage-most-people-dont-realize-activity-7394590236082814977-019u?

by Beth Stackpole

Aug 19, 2025

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.

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

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

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.

Here are five insights from the inaugural report.

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” 

New technologies are helping vulnerable communities produce maps that integrate topographical, infrastructural, seasonal, and real-time data — an essential tool for many humanitarian endeavors.

By Dr. Werner Vogels 

October 14, 2025

This is a condensed version of a talk that Amazon vice president and chief technology officer Dr. Werner Vogels gave at the AI for Good Global Summit in July 2025 in Geneva.

In January 2007, my mentor, friend, and fellow computer scientist Jim Gray, a Turing Award laureate often described as the father of modern database systems, disappeared while sailing solo to the Farallon Islands off San Francisco. Despite deploying every technological resource imaginable, from repositioning government satellites to mobilizing thousands of recruits through Amazon’s Mechanical Turk to analyze satellite images, we never found him. If we had today’s AI resources, would the result have been different? Maybe. There are things that we can do now that we definitely could not do in 2007.

While Jim’s friends were able to use their private-sector relationships and government clearances to access real-time satellite data, most vulnerable communities remain invisible in our digital representations of Earth. The Haiti earthquake of 2010 made this painfully clear. International rescue teams arrived in Port-au-Prince to find a city that was, for all practical purposes, unmapped. Emergency responders had GPS coordinates but couldn’t navigate because the maps they had couldn’t distinguish between alleys and major roadways or locate critical infrastructure like hospitals and shelters.

The data divide

The situation in Haiti isn’t unique. Consider Makoko, a community in Lagos, Nigeria, that is home to more than 300,000 people living on stilt houses in the Lagos Lagoon. On most maps, this entire community appears as a blank blue spot. These people are effectively invisible, unable to access basic services because they don’t exist in our spatial data models.

The reason for this omission is simple: most maps are created for commercial purposes, not humanitarian needs. We meticulously map shopping districts in major cities but leave vast swaths of the developing world uncharted. This creates what I call the “data divide”, a disparity in data access that mirrors and exacerbates existing social inequalities. When we only map what’s profitable, we perpetuate these inequalities and leave the most vulnerable communities exposed.

Now, if you think about maps, there’s not just one map of the earth. The moment you have a traditional map in your hand, it is out of date. Effective maps are multilayered systems operating across different timescales.

First, there’s the Earth layer, the slow-changing geographical features that remain constant over decades or centuries. The Himalayas or Amazon Basin won’t be moving anytime soon. Then there’s the infrastructure layer — roads, bridges, and buildings that evolve over years. Next comes the seasonal layer, which tracks changes in vegetation, water levels, and other environmental factors that shift with the seasons. Finally, there’s the real-time layer, a constantly fluctuating stream of data about human activity, weather patterns, and emergency situations.

Humanitarian mapping must integrate all these layers. During a flood, for example, we need real-time data about water levels (real-time layer), historical flood patterns (seasonal layer), existing drainage infrastructure (infrastructure layer), and underlying topography (Earth layer). Combining these data streams requires sophisticated AI models that can handle multiple data types and temporal scales.

Democratizing Earth data

The good news is that the tools for data collection have become much more accessible. The number of Earth observation satellites has exploded from about 150 in 2008 to over 10,000 today. These satellites offer not just high-resolution imagery but advanced sensors like multispectral imagers, radar, and lidar.

In the aftermath of the Haiti earthquake, roughly 600 members of the OpenStreetMap community were able to create the first reliable crisis map within 48 hours. It only took two days to go from unmapped to mapped. This crowdsourced map became the default navigation tool for every major responding organization, from the UN to the US Marine Corps. OpenStreetMap has since evolved into a global platform for collaborative mapping, with spinoffs like the Humanitarian OpenStreetMap Team (HOT) and Missing Maps focusing specifically on crisis response.

Drones have emerged as a powerful complement to satellites, filling gaps where satellite imagery is insufficient or too expensive. The Mapping Makoko project trained local residents to pilot drones and map their community. This initiative did more than create a map; it empowered residents with a tool for political advocacy, demonstrating the power of democratized data collection.

While satellites and drones provide macro-level data, mobile devices and Internet-of-things (IoT) sensors offer granular, real-time information. With over eight billion mobile devices globally, we have an unprecedented opportunity for crowdsourced data collection. In Southeast Asia, the Grab app (a super-app providing everything from ride hailing to food delivery) has created detailed maps of previously unmapped areas simply by tracking the routes of its drivers, who are familiar with neighborhoods, alleys, and unmarked homes. Similarly, India’s Namma Yatri app connects auto-rickshaw drivers with passengers while simultaneously generating accurate street maps of informal settlements.

IoT sensors embedded in infrastructure provide another layer of real-time data. Environmental sensors tracking air quality, water levels, or seismic activity can feed directly into mapping systems, creating a dynamic representation of a community’s current state.

Building with open data

During a recent visit to Rwanda, I saw firsthand how data-driven mapping can transform healthcare delivery. The Rwanda Health Intelligence Center uses real-time data to track healthcare utilization across the country. By combining this with geospatial data, they’ve calculated the maximum walking distance for pregnant women to reach a health center. This data directly informs where to build new facilities, optimizing resource allocation.

Another inspiring example is the Ocean Cleanup project, which aims to remove 90% of ocean plastic by 2040. They’ve developed a river model using drones, AI analysis, and GPS-tagged dummy plastics to predict plastic-flow patterns. This data-driven approach allows them to position their cleanup systems in the most effective locations, while AI-powered cameras on bridges identify different types of plastic in real time.

The sheer volume of geospatial data — hundreds of petabytes from satellites, drones, and IoT sensors — requires robust infrastructure. Cloud platforms like Amazon S3, which processes over a quadrillion requests every year, make it possible to store and process this data at scale. Our Open Data Sponsorship Program further removes barriers by covering costs for high-value public datasets, including OpenStreetMap, Sentinel-2 imagery, and various environmental-sensor data.

Planetary problem-solving machine

The combination of open data, advanced AI models, and cloud infrastructure creates what I call a planetary problem-solving machine. This trio can tackle challenges that were previously intractable. Open data ensures transparency and verifiability, while AI extracts insights that would be impossible for humans to discern.

When we have data that could save lives or protect the environment, keeping it private is morally indefensible. The United Nations’ 17 Sustainable Development Goals all depend on geospatial data. Whether it’s ending poverty, achieving food security, or combating climate change, every goal requires location-based data to measure progress and guide interventions.

The question for all of us is, what data do we have that could be useful to others? And more importantly, what data can we open up? If we don’t act, we risk perpetuating a world where the most vulnerable remain invisible, where disasters are compounded by lack of information, and where progress is measured only in places that are profitable.

It is for this exact reason, that in 2024 I launched the Now Go Build CTO Fellowship. Bringing together technology leaders from non-profits and social good organizations that are working to address climate change, disaster management, healthcare accessibility, food security, education and pairing them with experts at Amazon, AWS and beyond. I’ve seen first-hand, how these Fellows are using data to solve the world’s hardest problems, whether that’s measuring crop yields, connecting surplus food with charities and families, or piloting drones in conflict areas, none of which is possible without maps.

Maps have always been more than navigation tools: they’re instruments of power. In the digital age, they’re becoming tools of justice, healthcare, and environmental protection. By making the invisible visible, we can create a more equitable world.

Now go build.

Article link: https://www.amazon.science/blog/why-ai-for-good-depends-on-good-data?

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