healthcarereimagined

6 days after we published our first AI Bubble Index, Bloomberg validated what our report detected:
“A light has been shined on the complexity of the financing, the circular deals, the debt issues. OpenAI’s connections now look more like an anchor.”

That is exactly what we described as The Metabolic Mirage (image below). Every business decision makes sense in isolation:

• Nvidia ships GPUs
• Hyperscalers deploy billions to stay competitive
• They fund AI startups, who buy Nvidia chips with vendor financing
• Nvidia’s numbers look great and all players’ equities appreciate

Asset managers surveyed by Fortune magazine just confirmed this: “You can’t call it a bubble when you’re seeing tech companies deliver a massive earnings beat.”

Well, until 1 link breaks.

Since we published a week ago, the structural stress we measured in company financials has become visible in credit markets:

• Oracle Credit Default Swap spreads: highest since the 2009 financial crisis
• Banks: using insurance to hedge AI loan losses
• OpenAI’s revenue to commitment gap: $207B (!)

We normally analyze individual companies and business units for investors and leadership teams to establish whether strategic, value creation or transformation plans can metabolize into performance. We applied the same methodology and math to the AI ecosystem to verify if the massive CAPEX commitments can metabolize in ROI.

The result: a 132x Metabolic Overload score, measuring multiplicative stress across five infrastructure layers.
Capital deployment velocity is running 132 times faster than the physical infrastructure stack can absorb it.

Links to the report, sources & 7-day timeline in comments below.

DecisionDNA #AIInfrastructure #MetabolicOverload

Article link: https://www.linkedin.com/posts/boykester_decisiondna-aiinfrastructure-metabolicoverload-activity-7403700934729560064-Sh-r?

By Jeremy Hsu, December 5, 2025

Silicon Valley and its backers have placed a trillion-dollar bet on the idea that generative AI can transform the global economy and possibly pave the way for artificial general intelligence, systems that can exceed human capabilities. But multiple warning signs indicate that the marketing hype surrounding these investments has vastly overrated what current AI technology can achieve, creating an AI bubble with growing societal costs that everyone will pay for regardless of when and how the bubble bursts.

The history of AI development has been punctuated by boom-and-bust cycles (with the busts called AI winters) in the 1970s and 1980s. But there has never been an AI bubble like the one that began inflating around corporate and investor expectations since OpenAI released ChatGPT in November 2022. Tech companies are now spending between $72 billion and $125 billion per year each on purchasing vast arrays of AI computing chips and constructing massive data centers that can consume as much electricity as entire cities—and private investors continue to pour more money into the tech industry’s AI pursuits, sometimes at the expense of other sectors of the economy.

“What I see as a labor economist is we have starved everything to feed one mouth,” says Ron Hetrick, Principal Economist at Lightcast, a labor market analytics company. “These are now three years that we have foregone development in so many industries as we shove food into a mouth that’s already so full.”

That huge AI bet is increasingly looking like a bubble; it has buoyed both the stock market and a US economy otherwise struggling with rising unemployment, inflation, and the longest government shutdown in history. In September, Deutsche Bank warned that the United States could already be in an economic recession without the tech industry’s AI spending spree and cautioned that such spending cannot continue indefinitely. However it ends, the AI bubble’s most enduring legacy may be the global disruptions from any financial crisis that follows—and the societal costs already incurred from betting so heavily on energy-hungry data centers and AI chips that may suddenly become stranded assets.

warning signs. 

Silicon Valley’s focus on developing ever-larger AI models has spurred a buildout of bigger data centers crammed with computing power. The staggering growth in AI compute demand would require tech companies to build $500 billion worth of data centers packed with chips each year—and companies would need to rake in $2 trillion in combined annual revenue to fund that buildout, according to a Bain & Company report. The report also estimates that the tech industry is likely to fall $800 billion short of the required revenue.

That shortfall is less surprising than it might seem. US Census Bureau data show that AI adoption by companies with more than 250 employees may have already peaked and began declining or flattening out this year. Most businesses still don’t see a significant return on their investment when trying to use the latest generative AI tools: Software company Atlassian found that 96 percent of companies didn’t achieve significant productivity gains, and MIT researchersshowed that 95 percent of companies get zero return from their pilot programs with generative AI. Separately, a paper by Nobel laureate Daron Acemoglu, an MIT professor in economics, estimates AI-driven productivity will produce only a “modest increase” in gross domestic product (GDP) of between 1.1 and 1.6 percent over the next 10 years.

Claims that AI can replace human workers on a large scale also appear overblown, or at least premature. When evaluating AI’s impact on employment, the Yale Budget Lab found that the “broader labor market has not experienced a discernible disruption since ChatGPT’s release 33 months ago,” according to the group’s analysis published in October 2025. Coincidentally, the largest tech companies spending billions of dollars on data centers have also led recent private-sector job cuts by laying off tens of thousands of their own workers—but observers such as Hetrick suggest that the tech companies are actually reducing workforces to help pay for their ongoing AI investments.

Another bubble warning sign: Silicon Valley’s accelerating spending spree on data centers and chips has outpaced what even the largest tech companies can afford. Companies such as Amazon, Google, Microsoft, Meta, and Oracle have already spent a record 60 percent of operating cash flow on capital expenditures like data centers and chips as of June 2025.

The financing ouroboros. Now, tech companies are increasingly resorting to “creative finance” such as circular financing deals to continue raising money for data centers and chips, says Andrew Odlyzko, professor emeritus of mathematics at the University of Minnesota, who has studied the history of financial manias and previous bubbles around technologies like railroads. Such creative finance “generates these very complex structures which nobody fully understands until things blow up and then various people are left to pick up the pieces,” he says. “I’m seeing more of that, and that’s what is getting me concerned; this part is typical of bubbles.”

For example, Meta sold $30 billion of corporate bonds in late October and also secured another $30 billion in off-balance-sheet debt through a joint venture structured by Morgan Stanley, arrangements that can hide the risks and liabilities of such deals. The swift accumulation of $100 billion in AI-related debt per quarter among various companies “raises eyebrows for anyone that has seen credit cycles,” said Matthew Mish, head of credit strategy at UBS Investment Bank, in a Bloomberg interview.

Business journalists and analysts have also called attention to the growing number of “circular finance” deals fueling the AI bubble, with one example being AI chipmaker NVIDIA investing $100 billion in OpenAI even as the latter plans to buy more NVIDIA chips. A recent Morgan Stanley presentation showing the messy entanglement of AI-related deals, illustrated by arrows connecting various tech companies, “resembled a plate of spaghetti,” according to the Wall Street Journal.

As a result, a growing number of business leaders and institutions have voiced alarm about the stock market bubble building around AI, including the Bank of England and the International Monetary Fund. Even bullish tech and financial CEOs such as Amazon’s Jeff Bezos, JPMorgan Chase’s Jamie Dimon, Google’s Sundar Pichai, and OpenAI’s Sam Altman have acknowledged the existence of an AI bubble, despite their optimism about the advance of AI generally.

After the crash. If the stock market craters after a bursting of the AI bubble, it won’t just be financial institutions and venture capitalists losing money. Some 62 percent of Americans who reported owning stocks in 2025, according to a Gallup survey, could also be affected. Another national survey by The BlackRock Foundation and Commonwealth nonprofits also shows that approximately 54 percent of people with incomes between $30,000 and $80,000 have investment accounts. All these people stand to lose much of their investments if the AI bubble pops and market exuberance evaporates. The Economist has highlighted how the top 20 companies on the S&P 500 stock market index currently account for 52 percent of total market value, with the top 10 especially dominated by AI-related companies.

The market mayhem brought on by a deflation of the AI bubble could also mean economic disruption worldwide. Writing for The Economist, Gita Gopinath, former chief economist for the International Monetary Fund, warned that a bursting of the AI bubble on the magnitude of the dot-com bubble collapse in 2000 could have “severe global consequences,” including the wipeout of more than $20 trillion in wealth for American households and $15 trillion in wealth for foreign investors.

Similarly, the International Monetary Fund’s latest World Economic Outlook report described how “an abrupt repricing of tech stocks could be triggered by disappointing results on earnings and productivity gains related to artificial intelligence, marking an end to the AI investment boom and the associated exuberance of financial markets, with the possibility of broader implications for macrofinancial stability.”

If the AI bubble pops, the US government will likely turn to its central bank, the Federal Reserve, to stabilize the wider economy by injecting huge amounts of cash into the financial system, as it did after the 2008 financial crisis, Odlyzko says. But he warned that a new government bailout of the financial system would mean another significant jump in the national debt and increased wealth inequality, because taxpayer dollars would be once again focused on stabilizing a sector in which the wealthiest individuals will benefit disproportionately from recovering corporate profits and rebounding share prices. A repeat of the financial bailout cycle that privatizes the gains of wealthy risk-takers while socializing the losses to everyone else is “likely to lead to even more [political] polarization and perhaps true populist movements,” Odlyzko says.

The United States is less well equipped to handle the AI bubble if it were to burst today because of the weakened US dollar, political pressure on the Federal Reserve’s institutional independence, limitations on economic growth due to President Trump’s sweeping tariffs and trade wars, and record levels of government debt that could constrain attempts to use fiscal stimulus to right-size a sinking economy, Gopinath wrotein The Economist.

Not every major country pursuing lofty AI ambitions is equally vulnerable to such a crash. For example, China faces less risk than Silicon Valley should the AI boom goes bust, even if the event would likely “deflate some froth on both sides,” says Lizzi C. Lee, a fellow on Chinese Economy at the Center for China Analysis at the Asia Society Policy Institute in New York. That is because China’s pragmatic approach to deploying AI more closely resembles “industrial electrification” rather than “funding the dot-com boom,” she says.

“China is structurally less exposed to the ‘AI-for-AI’ bubble because its policy and investment logic centers on integrating AI into the real economy—manufacturing, logistics, public services—rather than frontier models for their own sake,” says Lee. “So, the focus is more on measurable productivity gains, not speculative valuations.”

Tech skeletons. Previous financial bubbles around technologies such as railroads and the internet left behind some functional physical infrastructure, which proved useful for newcomers that swooped in afterward to establish new businesses. But the sudden end of the AI bubble may not yield that kind of silver lining; should the AI boom not produce the requisite income to support continued investment in it, tech companies could find themselves with plenty of underutilized data centers and chips.

Some AI-focused data centers could be repurposed to run less intensive computing workloads. But AI chips usually lose their economic value within just a few years as they fall behind the latest chip technologies, and they also experience rapid physical deterioration from running typical AI workloads, writes Mihir Kshirsagar, Technology Policy Clinic director at Princeton University. Shipments of AI-related chips for data centers reached 3.85 million units in 2023 alone, and that number could grow to 7.9 million by 2030.

A sudden glut of “three-year-old chips at firesale prices” won’t enable newcomers to challenge the entrenched market dominance of existing tech giants, given that the latter can easily afford to run the latest AI hardware, according to Kshirsagar. By comparison, the dot-com bubble collapse allowed newcomers to buy internet fiber at bargain-bin prices, and those physical assets lasted for decades.

Stranded energy infrastructure assets associated with the end of the AI bubble could pose an even bigger complication: Historic electricity demand from the rapid buildout of data centers has spurred utility companies to commit billions of dollars to building new power transmission lines, natural gas pipelines, and power plants, but someone will need to pay the costs of all that energy infrastructure buildout if AI demand fizzles.

Paying for power. Data centers currently represent the fastest-rising source of power demand for the United States, and the electricity needs of individual data center campuses are also growing to gargantuan proportions. Tech companies have rushed to build new gigawatt-scale data centers such as Meta’s “Hyperion” data center in Louisiana, which would consume twice as much electricity as the entire city of New Orleans. Meanwhile, a new Amazon data center campus in Indiana will require as much electricity as half of all homes in the state, or approximately 1.5 million households.

To meet that demand, utility companies are making a projected $1.4 trillion investment in electricity infrastructure between 2025 and 2030. And they are paying for it by raising electricity rates for all ratepayers, including households and small businesses. Utility companies have already gotten approval to increase rates by approximately $29 billion nationwide in the first half of 2025, compared to just $12 billion requested and approved in the first half of 2024. This is taking place while US residential electricity costs have jumped by almost 30 percent on average since 2021, according to a report by the nonprofit PowerLines.

“If you believe the projections here, we’re in the early stages of this buildout for industrial-scale computing, and therefore the infrastructure needed to power these facilities is just starting to get built, which means we’re all just starting to pay for it,” says Ari Peskoe, director of the Electricity Law Initiative at Harvard Law School. “So, there’s a possibility that the consumer impacts get a lot worse, unless there’s significant reforms of how utilities spread the costs of infrastructure.”

There is already some evidence showing that data center demand for power is driving up local electricity costs. A Bloomberginvestigation found that areas of the country with “significant data center activity” saw wholescale electricity prices soar by as much as 267 percent for a single month compared to five years ago. More than 70 percent of regions that saw price increases were located within 50 miles of such data center clusters.

Tech companies are helping to finance some of the energy infrastructure expansion, especially by using power purchase agreements to bring more power generation into the mix. For example, Microsoft and Google have signed power purchase agreements with energy companies to reopen the nuclear power plants at Three Mile Island in Pennsylvania and the Duane Arnold Energy Center in Iowa, respectively.

But utility companies and their other ratepayers still bear the brunt of expenses for building new power plants, local power lines and transformers, and transmission lines to carry electricity across longer distances. Peskoe and Eliza Martin, a Legal Fellow at Harvard’s Environmental & Energy Law Program, reviewed almost 50 regulatory proceedings related to data center utility rates to show how those processes can shift the utility costs to the public.

If the AI bubble pops and many data centers shut down, much of the local energy infrastructure “is probably mostly unnecessary,” says Peskoe. But new power plants could potentially be repurposed for other energy customers if the demand is there, and new transmission lines that carry electricity across long distances are particularly helpful under a variety of future conditions, he says.

Some state public utility commissions have begun working with utility companies to implement measures that would make data centers pay higher electricity rates and interconnection costs for using the electricity grid, along with exit fees that data center owners would have to pay if they cease operations before contracts expire. Nearly 30 states have proposed or approved new electricity rates for large customers such as data centers, according to a databasemaintained by the Smart Electric Power Alliance and North Carolina Clean Energy Technology Center.

But energy infrastructure development costs associated with data centers could still be “socialized” and borne by ordinary utility customers if projects don’t have those protections in place, Peskoe says. “I’m sure there would be some utilities that, if there were a burst of the bubble, would probably go to regulators and say, ‘Hey, we want to recover the cost of these facilities from everyone,’” he says.

The unsustainable bubble. The timing of an AI bubble implosion could also shape its long-term impact on the US power grid’s energy mix and carbon emissions. Data center developers have purchased power from, and sometimes made direct investments in, a diverse array of sources, including renewable power, along with nuclear power and energy storage solutions—and yet half of the rising power demand from US data centers is still likely to be supplied through natural gas generation by 2030, according to an S&P Global report.

This is especially problematic for attempts to mitigate climate change, given that natural gas power plants produce carbon dioxide while natural gas wells and pipelines can leak methane as an especially potent greenhouse gas. But that has not stopped some tech companies from rushing to install dozens of gas turbines as on-site power plants for data centers, including Elon Musk’s xAI company and the Project Stargate joint venture involving OpenAI and Oracle. This AI-driven demand for natural gas generation is occurring even as the United Nations’ Emissions Gap Report 2025 warns that the world already faces escalating climate risks from its failures to limit global warming.

Like financial crises of the past, an abrupt end to the AI bubble could inflict considerable economic pain on millions of people worldwide. But the alternative is the prolonging of an AI bubble that is increasingly unsustainable in both the financial and environmental senses, with the winners mainly being some of the wealthiest companies on the planet and their investors.

“Ultimately, for society’s sake, it would be a wonderful thing the faster this thing goes, because very few people are benefiting from it,” says Hetrick, the labor economist at Lightcast. “Had we spread the wealth and invested in various industries, who knows how many innovations we could have come up with by now while we’ve been incinerating this money.”

Article link: https://thebulletin-org.cdn.ampproject.org/c/s/thebulletin.org/2025/12/when-it-all-comes-crashing-down-the-aftermath-of-the-ai-boom/amp/

ByKevin Cushnie,

Forbes Councils Member.

for Forbes Technology Council

COUNCIL POST | Membership (fee-based)

Nov 25, 2025 at 10:45am EST

Kevin Cushnie leads Product Engineering, Innovation and Transformation for MC Systems.

Global investment in digital transformation is on track to approach $4 trillion by 2027, according to IDC. Yet research from BCG shows that up to 70% of these initiatives fail, and Gartner found that less than half meet or exceed business outcome targets.

The persistence of these failure rates, despite decades of experience and monumental investment, points to a fundamental problem: applying linear, project-based thinking to a challenge that is inherently complex and interconnected.

This disconnect has become especially critical as organizations race to integrate AI into their operations. These technologies go beyond simply task automation; they reshape information flows, decision-making authority and organizational knowledge itself.

Without a systems approach, AI implementations can become another expensive failure in the transformation graveyard.

Why Project-Based Thinking Leads To Failure

The traditional approach treats digital transformation as a checklist: gather requirements, select technology, deploy and train users. This model typically confines the effort to the IT department, treating it as a purely technical task with a defined endpoint. The result is siloed technology investments that collide with organizational reality.

There are several obstacles with this approach: organizational culture, change management failures and corporate inertia, to name a few.

Despite this, organizations continue to underinvest in change management, with only 30% having a focused strategy as part of their digital transformation program, according to TEKsystems research. Investment is being poured into what we can see and measure (technology) while ignoring the invisible forces that determine success (people, culture and processes).

The value leaks out through these unattended human and cultural components, producing low user adoption and missed business outcomes.

Seeing Your Organization As A Living Ecosystem

Systems thinking offers a radically different lens. Rather than viewing your organization as a collection of departments executing tasks, it recognizes that you’re managing an intricate ecosystem where components— people, processes, culture and technology—are interdependent and constantly influencing each other.

Three principles matter most:

1. Relationships between components are as important as the components themselves. A brilliant AI tool means nothing if employees don’t trust it or understand when to override its recommendations.

2. Causality is circular, not linear. The outcome of one process becomes the input for another, creating continuous cycles. Employee resistance to a new system, for instance, might prompt leadership to improve communication, which then reduces resistance.

3. New behaviors emerge from interactions that you cannot predict by studying individual parts in isolation.

Applied to digital transformation, this means recognizing three interconnected subsystems: the human system (culture, fears, skills, trust), the process system (workflows, workarounds, operational realities) and the technology system (tools, platforms, integrations).

Change any one element, and ripples propagate throughout the entire structure.

For AI integration specifically, systems thinking forces leaders to map where AI intersects with human decision-making authority. Where should the algorithm operate autonomously and where must humans retain override capability? How do we design feedback loops so human expertise continuously improves AI performance, while AI-generated insights enhance human capability?

These are systemic questions that determine whether AI augments your workforce or alienates it. Here are three ways to take this from theory to practice: 

1. Map your ecosystem before you deploy.

Ecosystem mapping goes beyond traditional organizational charts to reveal how work actually gets done. It visualizes entities, relationships, value flows and, crucially, the workarounds people have created to manage inefficiencies.

These informal processes signal system stress and reveal where new technology will face friction. For AI implementations, mapping shows you which business processes genuinely benefit from automation versus where human judgment remains essential.

Leaders who map first identify strategic intervention points before committing capital, rather than discovering obstacles after deployment.

2. Reframe resistance as system intelligence.

When employees push back against change, the instinct is to overcome their objections. Systems thinking applies a different approach, seeing resistance as vital system feedback.

People naturally protect stability, predictability and control—it’s how human systems maintain equilibrium.

When Clorox embarked on its five-year, $580 million AI-powered transformation(subscription required), leadership made a strategic choice to upskill staff rather than replace them, as reported by The Wall Street Journal. This decision addressed the root cause of resistance, fear of job vulnerability, and turned a potential balancing force into a reinforcing one. The result was a culture of curiosity and problem-solving that positioned Clorox as an industry model for AI transformation.

Resistance often reveals legitimate concerns about feasibility, unintended consequences or flawed implementation plans. By treating pushback as a diagnostic tool rather than an obstacle, you improve the initiative itself while also building trust.

3. Build continuous feedback loops.

In complex systems, long delays between cause and effect hide critical problems. Annual surveys provide outdated data for initiatives that need real-time adjustment. Instead, embed continuous feedback mechanisms—pulse surveys, sentiment analysis and agile retrospectives.

For AI systems specifically, this means monitoring not just technical performance but human trust, override rates and decision quality. When you can identify patterns immediately and adjust before small issues cascade, you enhance organizational agility while demonstrating that employee input actively shapes decisions.

From Project Manager To Systems Architect

The contrast between Kodak and Domino’s illustrates what’s at stake.

Kodak invented the first digital camera, but couldn’t pivot away from its film-based business model. The company’s structure, incentives and culture acted as a powerful balancing loop that actively resisted change.

Conversely, Domino’s holistically re-engineered its entire customer journey and operational model, redefining itself as a technology company that happens to sell pizza. That systemic alignment drove genuine transformation.

Leaders must evolve from managing isolated projects to architecting learning organizations—building adaptive capacity for continuous evolution rather than executing one-time initiatives.

Systems thinking isn’t just a theory but a strategic necessity in an era where AI, automation and digital platforms fundamentally reshape how organizations create value. The challenge for technology leaders is to shift from managing parts to orchestrating the whole.

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ByKevin CushnieCOUNCIL POST | Membership (fee-based)Kevin Cushnie leads Product Engineering, Innovation and Transformation for MC Systems. Read Kevin Cushnie’s full executive profile here.Read MoreFind Kevin Cushnie on LinkedIn. Visit Kevin’swebsite.

Article link: https://www.forbes.com/councils/forbestechcouncil/2025/11/25/why-digital-transformation-and-ai-demands-systems-thinking/

by Seb Murray

 Oct 9, 2025Share 

What you’ll learn: 

• AI adoption leads to increased company growth in revenue, profits, employment, and profitability.
• Exposure to AI is greatest in higher-paying roles that involve information processing and analysis.
• In the 2014 – 2023 period, AI-exposed roles did not experience job losses relative to other roles, due to offsetting factors.

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Artificial intelligence is often portrayed as a job killer, but new research tracking AI adoption from 2010 to 2023 paints a more nuanced picture: AI’s impact is often on specific tasks within jobs rather than on whole occupations.

A new study co-authored by MIT Sloan associate professor Lawrence Schmidt found that when AI can perform most of the tasks that make up a particular job, the share of people in that role within a company falls by about 14%.

But when AI’s impact is concentrated in just a few tasks within a role — leaving other responsibilities untouched — employment in that role can grow. The reason? With some of their tasks automated, workers can focus on activities where AI is less capable, such as critical thinking or coming up with new ideas.

Even workers in high-wage roles heavily exposed to AI — positions near the top of the pay scale — saw their share of total employment grow by about 3% over five years. That’s because AI boosted firm productivity: Companies that used the technology grew faster, which helped sustain or even expand head count in high-exposure positions.

The takeaway for employers rolling the technology out to their workforce: “Firms that adopt AI don’t necessarily need to shed workers; they can grow and make more stuff and use workers more efficiently than other firms,” said Schmidt, an economist who studies how finance and the job market intersect.More about the methodology

The team used advances in natural language processing to analyze the task requirements and work descriptions of about 58 million LinkedIn profiles and 14 million job postings.

They compared them to roughly 20,000 work activities listed in O*NET, the U.S. Department of Labor’s database, identifying which tasks AI is most capable of performing. 

They then calculated the average exposure for all of the tasks in each job and measured how evenly that exposure was distributed, showing whether AI could handle nearly every task in a role or only a few.

Read more: Artificial Intelligence and the Labor MarketLEARN MORE

The study, co-authored with Menaka Hampole of the Yale School of Management and Dimitris Papanikolaouand Bryan Seegmiller of Northwestern University’s Kellogg School of Management, used natural language processing to construct new measures of workers’ task-level exposure to AI and machine learning from 2010 to 2023, capturing variation across firms, occupations, and time.

What happened to jobs — and why

As of December 2023, AI had not caused major changes in total employment. Losses in highly exposed roles were largely offset by gains in other jobs — and by hiring growth at firms that used AI to become more productive. But the findings predate the widespread rise of generative AI, which the authors note could shift such patterns in the future.

The study also shows that companies can see substantial gains by putting AI to work — and that that growth translates into jobs. Firms that use AI extensively tend to be larger and more productive, and pay higher wages. They also grow faster: A large increase in AI use is linked to about 6% higher employment growth and 9.5% more sales growth over five years.

Which jobs shrank — and which grew 

The analysis shed light on who is most at risk from automation. The researchers found, perhaps surprisingly, that exposure to AI is greatest in high-paying roles, which often involve information processing and analysis — tasks that AI can already do well.

That contrasts with earlier technology waves, such as computerization and factory automation in the 1980s through early 2000s, which — as previous research has shown — mainly displaced middle-skill, routine jobs, like clerical work and basic bookkeeping.

Jobs that shrank:

• Top-paying roles (like management analysts, aerospace engineers, and computer and information research scientists): Employment within firms fell by about 3.5% over five years. These more lucrative roles are more likely to be in firms that adopt AI; the productivity gains that those firms realize more than offset the losses. As a result, the highest-paid jobs still make up a slightly bigger share of employment than those less exposed to AI.
• Business, financial, architecture, and engineering jobs: These shrank — by about 2% to 2.5% over five years — because they have a high share of tasks that match what AI can do. Among them, business and financial jobs are more exposed to AI than architecture and engineering roles, but they’re also more likely to be in firms that use AI heavily, which helps offset some of the losses.

Jobs that grew:

• Legal jobs: These roles gained the most from AI. They face little direct impact from automation and are often in firms that use AI heavily, leading to a predicted 6.4% increase in employment.

Why even low-exposure jobs are at risk

But not all low-exposure roles are safe. Even jobs with little direct impact from AI can shrink if their employers are slow to adopt the technology. The study found that food service jobs wilted relative to other jobs not because AI can do the work but because employers that don’t use AI grow more slowly, reducing demand for workers.

Relatedly, workers who are in occupations involving tasks amenable to AI but work at employers that do not adopt AI are not necessarily better off, since their employers grow less rapidly than their AI-adopting peers.

Generative AI could change the equation

However, the research reflects an earlier phase of AI adoption: The dataset runs up to 2023, so most of it predates the rapid rise of generative AI tools like ChatGPT, which launched in late 2022.

According to Schmidt, it is still an open question as to whether the patterns seen so far will hold in the generative AI era. Because generative AI can learn from fewer examples, he said, it could take on a wider range of tasks, leaving fewer for humans to shift to.

“It’s the million-dollar question,” he said. “Generative AI might reduce the gains from shifting work to people, but it could also make everyone more productive — a rising tide that lifts all boats.”

What employers can do

For Schmidt, the results highlight how much management decisions can shape AI’s impact.

“One of the important roles of firms in minimizing the displacements from AI is to really lean into task reallocation: Take your existing workforce, work with the AI, and make sure time is being reallocated toward tasks where people have a comparative advantage,” he said.

The key is to embrace the technology in the right way, Schmidt said.

“We shouldn’t be afraid of it,” he said. “We should learn how to use it and leverage those productivity gains — not as a substitute for things people are good at, like critical thinking and coming up with new ideas.”

To help companies get the most out of AI, Schmidt outlined a few priorities for business leaders:

• Encourage hands-on use. Do not wait until AI is rolled out across the company; give employees the chance to try different tools now. Early, active use builds skills and confidence.
• Pick the right tools and use them well. Make sure teams choose the versions and features that fit the work, and use them often enough to see real gains.
• Think beyond efficiency. Use AI not only to save time on routine work but also to open up new possibilities, such as tackling complex problems or generating fresh ideas.

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Lawrence Schmidt is an applied economist working at the intersection of finance and macro-labor. His research examines fundamental risk factors that impact the value of human capital and the causes and consequences of imperfect risk-sharing in labor and financial markets. Schmidt’s research tackles these questions by a combination of quantitative models and empirical work, which leverages, and often creates, novel microeconomic datasets, advanced econometric methods, and cutting-edge tools for textual analysis.

Article link: https://mitsloan.mit.edu/ideas-made-to-matter/how-artificial-intelligence-impacts-us-labor-market?

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?