Separating AI reality from hyped-up fiction isn’t always easy. That’s why we’ve created the AI Hype Index—a simple, at-a-glance summary of everything you need to know about the state of the industry.
The past few months have demonstrated how AI can bring us together. Meta released a model that can translate speech from more than 100 languages, and people across the world are finding solace, assistance, and even romance with chatbots. However, it’s also abundantly clear how the technology is dividing us—for example, the Pentagon is using AI to detect humans on its “kill list.” Elsewhere, the changes Mark Zuckerberg has made to his social media company’s guidelines mean that hate speech is likely to become far more prevalent on our timelines.
Estimates for how much it would cost VA to fully deploy its new electronic health record system have ranged from $16.1 billion to almost $50 billion.
The Department of Veterans Affairs needs to figure out the cost of its troubled electronic health record modernization program as it moves to restart deployments of the new software, lawmakers and witnesses warned during a House hearing on Monday.
VA initially signed a $10 billion contract — which was later revised to over $16 billion — with Cerner in May 2018 to modernize its legacy health record system and make it interoperable with the Pentagon’s new health record, which was also provided by Cerner. Oracle later acquired Cerner in 2022.
Almost as soon as the new EHR system went live in 2020 at the Mann-Grandstaff VA Medical Center in Spokane, Washington, however, the modernization project was beset by a host of problems, including cost overruns, patient safety concerns and technical glitches. VA subsequently paused deployments of the EHR system in April 2023 as part of a “reset” to address problems at the facilities where the software had been deployed.
Following the successful joint VA-DOD rollout of the EHR software at the Captain James A. Lovell Federal Health Care Center in North Chicago, Illinois last March — the sixth facility to use the new system — and citing significant improvements at the other sites using the Oracle Cerner EHR, VA announced in December that it was “beginning early-stage planning” to restart deployments in mid-2026.
Even as VA looks to implement the Oracle Cerner EHR system at four of its Michigan-based medical facilities in 2026, members of the House Veterans’ Affairs Technology Modernization Subcommittee pressed the department on Monday to pin down the long-term costs and time commitments needed to complete the modernization project.
Rep. Tom Barrett, R-Mich., the panel’s chairman, said he was not convinced VA has fixed all of the EHR program’s problems during its reset period and noted that — despite the fact VA is 7 years into the original 10-year contract for the project — “Congress has not received a schedule nor an up-to-date cost estimate to evaluate this program’s current state.”
Cost estimates for the entirety of the project have varied, with GAO Information Technology and Cybersecurity Director Carol Harris noting that these have ranged from $16.1 billion to an independent analysis that pegged the project’s total cost at almost $50 billion.
“While the latter is more realistic, neither reflects the many changes and delays to the program,” Harris said.
Seema Verma, Oracle Health’s executive vice president, said that an accelerated deployment of the new system — bolstered by progress made during the reset phase — would help drive down the project’s total cost and said that the company did not agree with the $50 billion figure cited by Harris.
The department and Oracle Cerner previously renegotiated their existing contract in May 2023 to include more accountability provisions in the agreement and to change the terms of the remaining contract from a 5-year term to five 1-year terms.
With the current contract slated to fully expire in May 2028, and VA still needing to deploy the new software to more than 160 other VA medical facilities, Neil Evans — acting program executive director of VA’s Electronic Health Record Modernization Integration Office — said the project would not be completed by that time.
To get a better understanding of the full scope of the modernization project, acting VA Inspector General David Case said the department “must develop and maintain an integrated master schedule to clearly track and project the program’s cost to completion.”
Evans told the lawmakers that the department is committed to developing a detailed integrated master schedule and updated life cycle cost estimate to help guide the modernization project’s path forward.
“We recognize the importance of providing this information to inform decisionmaking and ensure the success of future deployments,” he said.
The department said it will be adding nine sites to its 2026 deployment schedule, although it added that it will announce the specific facilities later this year.
The Department of Veterans Affairs is planning to increase deployments of its new Oracle Cerner electronic health record system to 13 VA medical facilities in 2026, with the ultimate goal of completing the troubled modernization project as early as 2031.
VA said in December that it would be rolling out the new EHR software at a total of four medical sites in mid-2026. In a Thursday release outlining its new plans, however, VA said it would announce the nine additional medical facilities slated to receive the modernized EHR system later this year, after consulting with VA officials, clinicians and Oracle Cerner representatives.
The department said it is “pursuing a market-based approach to site selection for its deployments going forward,” which will enable it “to scale up the number of concurrent deployments, while also enabling staff to work as efficiently as possible.”
The announcement comes as VA moves out of an operational pause on most rollouts of the new software that was instituted in April 2023 following a series of technical glitches, patient safety concerns and training challenges.
VA has deployed the software at just six of its 170 medical centers. One of those rollouts occurred at a joint VA-Defense Department medical facility in North Chicago last March during the agency’s “reset” period, with that deployment being seen, in part, as a crucial test of the efforts to right the modernization project.
During his January confirmation hearing, current VA Secretary Doug Collins echoed bipartisan concerns about the EHR modernization project but told lawmakers that the effort — which is intended to help streamline the delivery of medical records for servicemembers transitioning from active duty to civilian life — was necessary and that “there’s no reason in the world we cannot get this done.”
At the time, Collins also told lawmakers that he believed the department could restart deployments faster than the mid-2026 timeframe that the then-Biden administration set for resuming EHR system rollouts.
“America’s Veterans deserve a medical records system that’s integrated across all VA and DOD components, and that is exactly what we will deliver,” Collins said in a Thursday statement. “We can and will move faster on this important priority. But we’re going to listen to our doctors, nurses and vendor partners along the way in order to ensure patient safety, quality and customer service.”
Although VA said its goal is to complete all deployments of the new EHR system as soon as 2031, that timeframe already exceeds the limits of the current contract it has with Oracle Cerner.
The department initially entered into a 10-year, $10 billion contract with Cerner in May 2018 to modernize its legacy health record system. The cost of that contract was later revised to over $16 billion, and Cerner was subsequently acquired by Oracle in 2022.
VA and Oracle Cerner subsequently renegotiated their existing contract in May 2023 to include more accountability provisions in the agreement, as well as to revise the remaining contract from an additional 5-year term to five 1-year terms.
In addition to pressing VA to develop a master schedule for future EHR system deployments, lawmakers have pushed for the department to figure out the total cost of its modernization project.
During a House hearing last month, Republicans and Democrats both calledfor the VA official overseeing the EHR system’s rollout to develop an updated lifecycle cost estimate. Estimates for the project’s full completion have ranged from $16.1 billion to almost $50 billion.
Last Thursday, OpenAI released GPT-4.5, a new version of its flagship large language model. With each release of its GPT models, OpenAI has shown that bigger means better. But there has been a lot of talk about how that approach is hitting a wall—including remarks from OpenAI’s former chief scientist Ilya Sutskever. In this edition of What’s Next in Tech, find out everything you need to know about OpenAI’s latest model.
TOMORROW: The AI model market is shifting. Are you ready? Join us tomorrow, March 5, for our latest LinkedIn Live, “Disruption in the AI Model Market,” where we’ll break down the biggest changes shaping the landscape, and what they mean for you. Register for free today.
OpenAI says GPT-4.5 is its biggest and best chat model yet—but it could be the last release in the company’s classic LLM lineup.
Since the releases of its so-called reasoning models o1 and o3, OpenAI has been pushing two product lines. GPT-4.5 is part of the non-reasoning lineup—what Nick Ryder, a research scientist at the company, calls “an installment in the classic GPT series.”
All large language models pick up patterns across the billions of documents they are trained on. Smaller models learned syntax and basic facts. Bigger models can find more specific patterns like emotional cues, such as when a speaker’s words signal hostility, says Ryder: “All of these subtle patterns that come through a human conversation—those are the bits that these larger and larger models will pick up on.”
OpenAI won’t say exactly how big its new model is. But it claims the jump in scale from GPT-4o to GPT-4.5 is the same as the jump from GPT-3.5 to GPT-4o. Read the full story to learn more about how GPT-4.5 was trained, the benchmarks it has been tested on, and some of the model’s skills.
While Modular Open Systems Approach (MOSA) is familiar within the DoD, a new guidebook sheds light on statutes and policies that require it. Implementing a Modular Open Systems Approach in Department of Defense Programs guidebook delivers practical advice on planning, executing, and assessing MOSA. Office of the Under Secretary of Defense for Research and Engineering (OUSD(R&E)) Systems Engineering and Architecture (SE&A) prepared this guidebook. The office works to ensure architectures are modular and open to enhance competition, incorporate innovation, support interoperability, and enable rapid insertion of technology in DoD acquisitions, providing cutting-edge capabilities to the warfighter. Inside this new guidebook you will find: ✅ Implementation principles and best practices 📈 Benefits and challenges of MOSA 💡 Real-world insights from DoD and industry experts Learn more about MOSA: https://lnkd.in/dexBVdVN Download the guidebook here: https://lnkd.in/eQnE4m-W#MOSA#DoD#Acquisition#Innovation#OpenSystems
Ever since World War II, the US has been the global leader in science and technology—and benefited immensely from it. Research fuels American innovation and the economy in turn. Scientists around the world want to study in the US and collaborate with American scientists to produce more of that research. These international collaborations play a critical role in American soft power and diplomacy. The products Americans can buy, the drugs they have access to, the diseases they’re at risk of catching—are all directly related to the strength of American research and its connections to the world’s scientists.
That scientific leadership is now being dismantled, according to more than 10 federal workers who spoke to MIT Technology Review, as the Trump administration—spearheaded by Elon Musk’s Department of Government Efficiency (DOGE)—slashes personnel, programs, and agencies. Meanwhile, the president himself has gone after relationships with US allies.
These workers come from several agencies, including the Departments of State, Defense, and Commerce, the US Agency for International Development, and the National Science Foundation. All of them occupy scientific and technical roles, many of which the average American has never heard of but which are nevertheless critical, coordinating research, distributing funding, supporting policymaking, or advising diplomacy.
They warn that dismantling the behind-the-scenes scientific research programs that backstop American life could lead to long-lasting, perhaps irreparable damage to everything from the quality of health care to the public’s access to next-generation consumer technologies. The US took nearly a century to craft its rich scientific ecosystem; if the unraveling that has taken place over the past month continues, Americans will feel the effects for decades to come.
Most of the federal workers spoke on condition of anonymity because they were not authorized to talk or for fear of being targeted. Many are completely stunned and terrified by the scope and totality of the actions. While every administration brings its changes, keeping the US a science and technology leader has never been a partisan issue. No one predicted the wholesale assault on these foundations of American prosperity.
“If you believe that innovation is important to economic development, then throwing a wrench in one of the most sophisticated and productive innovation machines in world history is not a good idea,” says Deborah Seligsohn, an assistant professor of political science at Villanova University who worked for two decades in the State Department on science issues. “They’re setting us up for economic decline.”
The biggest funder of innovation
The US currently has the most top-quality research institutes in the world. This includes world-class universities like MIT (which publishes MIT Technology Review) and the University of California, Berkeley; national labs like Oak Ridge and Los Alamos; and federal research facilities run by agencies like the National Oceanic and Atmospheric Administration and the Department of Defense. Much of this network was developed by the federal government after World War II to bolster the US position as a global superpower.
Before the Trump administration’s wide-ranging actions, which now threaten to slash federal research funding, the government remained by far the largest supporter of scientific progress. Outside of its own labs and facilities, it funded more than 50% of research and development across higher education, according to data from the National Science Foundation. In 2023, that came to nearly $60 billion out of the $109 billion that universities spent on basic science and engineering.
Past government reports on improper spending are having a moment with Musk’s followers. What do they show?
The return on these investments is difficult to measure. It can often take years or decades for this kind of basic science research to have tangible effects on the lives of Americans and people globally, and on the US’s place in the world. But history is littered with examples of the transformative effect that this funding produces over time. The internet and GPS were first developed through research backed by the Department of Defense, as was the quantum dot technology behind high-resolution QLED television screens. Well before they were useful or commercially relevant, the development of neural networks that underpin nearly all modern AI systems was substantially supported by the National Science Foundation. The decades-long drug discovery process that led to Ozempic was incubated by the Department of Veterans Affairs and the National Institutes of Health. Microchips. Self-driving cars. MRIs. The flu shot. The list goes on and on.
In her 2013 book The Entrepreneurial State, Mariana Mazzucato, a leading economist studying innovation at University College London, found that every major technological transformation in the US, from electric cars to Google to the iPhone, can trace its roots back to basic science research once funded by the federal government. If the past offers any lesson, that means every major transformation in the future could be shortchanged with the destruction of that support.
The Trump administration’s distaste for regulation will arguably be a boon in the short term for some parts of the tech industry, including crypto and AI. But the federal workers said the president’s and Musk’s undermining of basic science research will hurt American innovation in the long run. “Rather than investing in the future, you’re burning through scientific capital,” an employee at the State Department said. “You can build off the things you already know, but you’re not learning anything new. Twenty years later, you fall behind because you stopped making new discoveries.”
A global currency
The government doesn’t just give money, either. It supports American science in numerous other ways, and the US reaps the returns. The Department of State helps attract the best students from around the world to American universities. Amid stagnating growth in the number of homegrown STEM PhD graduates, recruiting foreign students remains one of the strongest pathways for the US to expand its pool of technical talent, especially in strategic areas like batteries and semiconductors. Many of those students stay for years, if not the rest of their lives; even if they leave the country, they’ve already spent some of their most productive years in the US and will retain a wealth of professional connections with whom they’ll collaborate, thereby continuing to contribute to US science.
The State Department also establishes agreements between the US and other countries and helps broker partnerships between American and international universities. That helps scientists collaborate across borders on everything from global issues like climate change to research that requires equipment on opposite sides of the world, such as the measurement of gravitational waves.
The international development work of USAID in global health, poverty reduction, and conflict alleviation—now virtually shut down in its entirety—was designed to build up goodwill toward the US globally; it improved regional stability for decades. In addition to its inherent benefits, this allowed American scientists to safely access diverse geographies and populations, as well as plant and animal species not found in the US. Such international interchange played just as critical a role as government funding in many crucial inventions.
Ever since World War II, the US has been the global leader in science and technology—and benefited immensely from it. Research fuels American innovation and the economy in turn. Scientists around the world want to study in the US and collaborate with American scientists to produce more of that research. These international collaborations play a critical role in American soft power and diplomacy. The products Americans can buy, the drugs they have access to, the diseases they’re at risk of catching—are all directly related to the strength of American research and its connections to the world’s scientists.
That scientific leadership is now being dismantled, according to more than 10 federal workers who spoke to MIT Technology Review, as the Trump administration—spearheaded by Elon Musk’s Department of Government Efficiency (DOGE)—slashes personnel, programs, and agencies. Meanwhile, the president himself has gone after relationships with US allies.
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These workers come from several agencies, including the Departments of State, Defense, and Commerce, the US Agency for International Development, and the National Science Foundation. All of them occupy scientific and technical roles, many of which the average American has never heard of but which are nevertheless critical, coordinating research, distributing funding, supporting policymaking, or advising diplomacy.
They warn that dismantling the behind-the-scenes scientific research programs that backstop American life could lead to long-lasting, perhaps irreparable damage to everything from the quality of health care to the public’s access to next-generation consumer technologies. The US took nearly a century to craft its rich scientific ecosystem; if the unraveling that has taken place over the past month continues, Americans will feel the effects for decades to come.
Most of the federal workers spoke on condition of anonymity because they were not authorized to talk or for fear of being targeted. Many are completely stunned and terrified by the scope and totality of the actions. While every administration brings its changes, keeping the US a science and technology leader has never been a partisan issue. No one predicted the wholesale assault on these foundations of American prosperity.
“If you believe that innovation is important to economic development, then throwing a wrench in one of the most sophisticated and productive innovation machines in world history is not a good idea,” says Deborah Seligsohn, an assistant professor of political science at Villanova University who worked for two decades in the State Department on science issues. “They’re setting us up for economic decline.”
The US currently has the most top-quality research institutes in the world. This includes world-class universities like MIT (which publishes MIT Technology Review) and the University of California, Berkeley; national labs like Oak Ridge and Los Alamos; and federal research facilities run by agencies like the National Oceanic and Atmospheric Administration and the Department of Defense. Much of this network was developed by the federal government after World War II to bolster the US position as a global superpower.
Before the Trump administration’s wide-ranging actions, which now threaten to slash federal research funding, the government remained by far the largest supporter of scientific progress. Outside of its own labs and facilities, it funded more than 50% of research and development across higher education, according to data from the National Science Foundation. In 2023, that came to nearly $60 billion out of the $109 billion that universities spent on basic science and engineering.
Past government reports on improper spending are having a moment with Musk’s followers. What do they show?
The return on these investments is difficult to measure. It can often take years or decades for this kind of basic science research to have tangible effects on the lives of Americans and people globally, and on the US’s place in the world. But history is littered with examples of the transformative effect that this funding produces over time. The internet and GPS were first developed through research backed by the Department of Defense, as was the quantum dot technology behind high-resolution QLED television screens. Well before they were useful or commercially relevant, the development of neural networks that underpin nearly all modern AI systems was substantially supported by the National Science Foundation. The decades-long drug discovery process that led to Ozempic was incubated by the Department of Veterans Affairs and the National Institutes of Health. Microchips. Self-driving cars. MRIs. The flu shot. The list goes on and on.
In her 2013 book The Entrepreneurial State, Mariana Mazzucato, a leading economist studying innovation at University College London, found that every major technological transformation in the US, from electric cars to Google to the iPhone, can trace its roots back to basic science research once funded by the federal government. If the past offers any lesson, that means every major transformation in the future could be shortchanged with the destruction of that support.
The Trump administration’s distaste for regulation will arguably be a boon in the short term for some parts of the tech industry, including crypto and AI. But the federal workers said the president’s and Musk’s undermining of basic science research will hurt American innovation in the long run. “Rather than investing in the future, you’re burning through scientific capital,” an employee at the State Department said. “You can build off the things you already know, but you’re not learning anything new. Twenty years later, you fall behind because you stopped making new discoveries.”
A global currency
The government doesn’t just give money, either. It supports American science in numerous other ways, and the US reaps the returns. The Department of State helps attract the best students from around the world to American universities. Amid stagnating growth in the number of homegrown STEM PhD graduates, recruiting foreign students remains one of the strongest pathways for the US to expand its pool of technical talent, especially in strategic areas like batteries and semiconductors. Many of those students stay for years, if not the rest of their lives; even if they leave the country, they’ve already spent some of their most productive years in the US and will retain a wealth of professional connections with whom they’ll collaborate, thereby continuing to contribute to US science.
The State Department also establishes agreements between the US and other countries and helps broker partnerships between American and international universities. That helps scientists collaborate across borders on everything from global issues like climate change to research that requires equipment on opposite sides of the world, such as the measurement of gravitational waves.
The international development work of USAID in global health, poverty reduction, and conflict alleviation—now virtually shut down in its entirety—was designed to build up goodwill toward the US globally; it improved regional stability for decades. In addition to its inherent benefits, this allowed American scientists to safely access diverse geographies and populations, as well as plant and animal species not found in the US. Such international interchange played just as critical a role as government funding in many crucial inventions.
Several federal agencies, including the Centers for Disease Control and Prevention, the Environmental Protection Agency, and the National Oceanic and Atmospheric Administration, also help collect and aggregate critical data on disease, health trends, air quality, weather, and more from disparate sources that feed into the work of scientists across the country.
The National Institutes of Health, for example, has since 2015 been running the Precision Medicine Initiative, the only effort of its kind to collect extensive and granular health data from over 1 million Americans who volunteer their medical records, genetic history, and even Fitbit data to help researchers understand health disparities and develop personalized and more effective treatments for disorders from heart and lung disease to cancer. The data set, which is too expensive for any one university to assemble and maintain, has already been used in hundreds of papers that will lay the foundation for the next generation of life-saving pharmaceuticals.
Beyond fueling innovation, a well-supported science and technology ecosystem bolsters US national security and global influence. When people want to study at American universities, attend international conferences hosted on American soil, or move to the US to work or to found their own companies, the US stays the center of global innovation activity. This ensures that the country continues to get access to the best people and ideas, and gives it an outsize role in setting global scientific practices and priorities. US research norms, including academic freedom and a robust peer review system, become global research norms that lift the overall quality of science. International agencies like the World Health Organization take significant cues from American guidance.
What’s coming next for technologies like EVs and wind power?
US scientific leadership has long been one of the country’s purest tools of soft power and diplomacy as well. Countries keen to learn from the American innovation ecosystem and to have access to American researchers and universities have been more prone to partner with the US and align with its strategic priorities.
Just one example: Science diplomacy has long played an important role in maintaining the US’s strong relationship with the Netherlands, which is home to ASML, the only company in the world that can produce the extreme ultraviolet lithography machines needed to produce the most advanced semiconductors. These are critical for both AI development and national security.
International science cooperation has also served as a stabilizing force in otherwise difficult relationships. During the Cold War, the US and USSR continued to collaborate on the International Space Station; during the recent heightened economic competition between the US and China, the countries have remained each other’s top scientific partners. “Actively working together to solve problems that we both care about helps maintain the connections and the context but also helps build respect,” Seligsohn says.
The federal government itself is a significant beneficiary of the country’s convening power for technical expertise. Among other things, experts both inside and outside the government support its sound policymaking in science and technology. During the US Senate AI Insight Forums, co-organized by Senator Chuck Schumer through the fall of 2023, for example, the Senate heard from more than 150 experts, many of whom were born abroad and studying at American universities, working at or advising American companies, or living permanently in the US as naturalized American citizens.
Federal scientists and technical experts at government agencies also work on wide-ranging goals critical to the US, including building resilience in the face of an increasingly erratic climate; researching strategic technologies such as next-generation battery technology to reduce the country’s reliance on minerals not found in the US; and monitoring global infectious diseases to prevent the next pandemic.
“Every issue that the US faces, there are people that are trying to do research on it and there are partnerships that have to happen,” the State Department employee said.
A system in jeopardy
Now the breadth and velocity of the Trump administration’s actions has led to an unprecedented assault on every pillar upholding American scientific leadership.
For starters, the purging of tens of thousands—and perhaps soon hundreds of thousands—of federal workers is removing scientists and technologists from the government and paralyzing the ability of critical agencies to function. Across multiple agencies, science and technology fellowship programs, designed to bring in talented early-career staff with advanced STEM degrees, have shuttered. Many other federal scientists were among the thousands who were terminated as probationaryemployees, a status they held because of the way scientific roles are often contractually structured.
“Diplomacy is built on relationships. If we’ve closed all these clinics and gotten rid of technical experts in our knowledge base inside the government, why would any foreign government have respect for the US in our ability to hold our word and in our ability to actually be knowledgeable?” a terminated USAID worker said. “I really hope America can save itself.”
Now the Trump administration has sought to reverse some terminations after discovering that many were key to national security, including nuclear safety employees responsible for designing, building, and maintaining the country’s nuclear weapons arsenal. But many federal workers I spoke to can no longer imagine staying in the public sector. Some are considering going into industry. Others are wondering whether it will be better to move abroad.
“It’s just such a waste of American talent,” said Fiona Coleman, a terminated federal scientist, her voice cracking with emotion as she described the long years of schooling and training she and her colleagues went through to serve the government.
No vaccine is perfect, but these medicines are still saving millions of lives.
Many fear the US has also singlehandedly kneecapped its own ability to attract talent from abroad. Over the last 10 years, even as American universities have continued to lead the world, many universities in other countries have rapidly leveled up. That includes those in Canada, where liberal immigration policies and lower tuition fees have driven a 200% increase in international student enrollment over the last decade, according to Anna Esaki-Smith, cofounder of a higher-education research consultancy called Education Rethink and author of Make College Your Superpower.
Germany has also seen an influx, thanks to a growing number of English-taught programs and strong connections between universities and German industry. Chinese students, who once represented the largest share of foreign students in the US, are increasingly staying at home or opting to study in places like Hong Kong, Singapore, and the UK.
During the first Trump administration, many international students were already more reluctant to come to the US because of the president’s hostile rhetoric. With the return and rapid escalation of that rhetoric, Esaki-Smith is hearing from some universities that international students are declining their admissions offers.
Add to that the other recent developments—the potential dramatic cuts in federal research funding, the deletion of scores of rich public data sets on health and the environment, the clampdown on academic freedom for research that appears related to diversity, equity, and inclusion and the fear that these restrictions could ultimately encompass other politically charged topics like climate change or vaccines—and many more international science and engineering students could decide to head elsewhere.
“I’ve been hearing this increasingly from several postdocs and early-career professors, fearing the cuts in NIH or NSF grants, that they’re starting to look for funding or job opportunities in other countries,” Coleman told me. “And then we’re going to be training up the US’s competitors.”
The attacks could similarly weaken the productivity of those who stay at American universities. While many of the Trump administration’s actions are now being halted and scrutinized by US judges, the chaos has weakened a critical prerequisite for tackling the toughest research problems: a long-term stable environment. With reports that the NSF is combing through research grants for words like “women,” “diverse,” and “institutional” to determine whether they violate President Trump’s executive order on DEIA programs, a chilling effect is also setting in among federally funded academics uncertain whether they’ll get caught in the dragnet.
To scientists abroad, the situation in the US government has marked American institutions and researchers as potentially unreliable partners, several federal workers told me. If international researchers think collaborations with the US can end at any moment when funds are abruptly pulled or certain topics or keywords are suddenly blacklisted, many of them could steer clear and look to other countries. “I’m really concerned about the instability we’re showing,” another employee at the State Department said. “What’s the point in even engaging? Because science is a long-term initiative and process that outlasts administrations and political cycles.”
Meanwhile, international scientists have far more options these days for high-caliber colleagues to collaborate with outside America. In recent years, for example, China has made a remarkable ascent to become a global peer in scientific discoveries. By some metrics, it has even surpassed the US; it started accounting for more of the top 1% of most-cited papers globally, often called the Nobel Prize tier, back in 2019 and has continued to improve the quality of the rest of its research.
Where Chinese universities can also entice international collaborators with substantial resources, the US is more limited in its ability to offer tangible funding, the State employee said. Until now, the US has maintained its advantage in part through the prestige of its institutions and its more open cultural norms, including stronger academic freedom. But several federal scientists warn that this advantage is dissipating.
“America is made up of so many different people contributing to it. There’s such a powerful global community that makes this country what it is, especially in science and technology and academia and research. We’re going to lose that; there’s not a chance in the world that we’re not going to lose that through stuff like this,” says Brigid Cakouros, a federal scientist who was also terminated from USAID. “I have no doubt that the international science community will ultimately be okay. It’ll just be a shame for the US to isolate themselves from it.”
Quantum computing is maturing at a rapid pace, and it is quite plausible that quantum computers capable of solving problems of value to businesses will be available this decade. At the same time, quantum computing likely will not supplant classical computing in the foreseeable future—after all, quantum computing architectures are best equipped to solve certain problems, but not every problem. Quantum computers almost certainly will work in concert with classical processing, where each computing architecture will handle those parts of a calculation that it is best suited to tackle. For that to happen, the quantum computing hardware will require software that combines quantum with classical computing. It also must be devised in a user-friendly way so that nonquantum scientists and software developers working on problems such as modeling molecules with unprecedented accuracy and calculating interesting properties of structured datasets can run quantum computational tasks without having in-depth knowledge of quantum computing.
Briefly, quantum computers solve problems coded in the form of qubits (short for quantum bits; these are units of information). This information is processed by complex hardware consisting of trapped atoms, artificial atoms engineered from superconducting wire—wire that can carry current without resistance—or other physical systems that can be put into quantum states. A qubit chip can look much like a classical computing chip but is capable of different kinds of mathematical operations beyond the binary code and logic operations of classical computing. By incorporating properties of quantum mechanics such as superposition (a set of qubits can be in multiple states at the same time until they are measured), interference (some of those states can cancel out), and entanglement (the ability to create correlations between qubits inaccessible to classical computers), quantum information can be processed in ways that are fundamentally different from how we compute with classical bits, in which the basic units of information exist in one of two distinct states, 1 and 0. This could accelerate the development of computer systems that can exactly predict the behavior of real-world natural systems such as chemical reactions, or perform algebra using a computer with exponentially more computational space than a classical computer (like optimizing energy grids).
The core unit of a quantum computation is the quantum circuit, which refers not to a physical circuit and electronic parts but rather to a computational routine that runs on a quantum processing unit instead of a central processing unit (the core computational unit of a classical computer). A quantum circuit begins when we encode information in qubits, then apply a sequence of operations—the quantum equivalent of a classical computer’s “IF,” “AND,” and “OR— to those qubits. It ends when we “measure” the qubits, receiving the output of the calculation. Measuring a qubit projects it onto classical states (known as “collapsing the wave function”), meaning that quantum circuits can only output a single string of binary code as a result of the execution. A quantum circuit may also include concurrent classical computing. For example, individual qubits might be measured in the midst of a computational procedure with the result stored as classical bits, and then fed back into the same circuit later. Unlike a classical routine, quantum circuits are innately probabilistic—different runs of the same circuit may lead to different output strings of binary digits, based on the probabilities determined at the time of measurement. For example, an algorithm that estimates the energy of a molecule with a quantum computer may result in a distribution of possible energies, which can be averaged into an expectation value.
The complexity of a quantum circuit is determined as a function of the number of qubits (width) and importantly, the number of quantum instructions that the circuit can run before the qubits can no longer accurately store quantum information (depth). In practice, depth is limited by properties such as noise from external sources (a phenomenon known as decoherence), or the process in which qubits “forget” their quantum information. Depth and width are critical for determining the potential types of problems that might be addressed by quantum computers, though these parameters are also moving targets as hardware improves.
On another front, quantum computing researchers are searching for techniques that allow quantum computers to correct errors in real time, called error correction, while working to build hardware less prone to noise and thus capable of running more complex quantum routines. At the same time, thanks to hardware advances and new postprocessing techniques called error mitigation, quantum computers can now use quantum circuits to run calculations that cannot be exactly simulated using more computationally expensive brute-force classical methods for certain types of problems in chemistry. Known as utility-scale problems, these typically require quantum circuits with 100+ qubits and 1000+ quantum gates. Although today’s quantum computers are not mature enough to run certain important quantum tasks such as the famous Shor’s algorithm used to factor numbers, they still have the potential to provide more timely value for research problems with the help of error mitigation. Several such methods are already promising, especially in the field of chemistry, in which techniques such as sample-based quantum diagonalizationmay be used to calculate the properties of molecules. Other algorithms for chemistry, data analysis, and optimization look encouraging for the near future.
These methods necessitate high-performance quantum software. Utility-scale quantum algorithms do not use just one quantum circuit—they typically require running a quantum circuit many times to sufficiently sample from a distribution of possible solutions. Furthermore, most programs incorporating quantum computation require a combination of both quantum and classical processing. Therefore, partitioning problems between quantum and classical processing hardware requires frequent data exchange. This strategy breaks down if the software is too slow. Therefore, software cannot simply be powerful enough to run quantum workloads efficiently on quantum computers. It must also be designed so that it can perform quickly and efficiently when quantum and classical processing are working together.
To make all of this more practical to potential users inside and outside of the field, developers are building and maintaining high-performing quantum software development kits (SDKs) such as IBM’s Qiskit, Quantinuum’s TKET, Google’s Cirq, and others. At the same time, developers have created universal circuits to use as benchmarks in order for these SDKs to track their performance—their ability to run these circuits quickly and efficiently. These benchmarks include QASMBench circuits, Feynman circuits, and Hamiltonian circuits. IBM maintains an open-source package that adapts these circuits to more than 1000 tests for benchmarking the performance of quantum SDKs in order to compare Qiskit to its competitors.
It is important that quantum SDKs remain open and transparent so that users can continue to measure their ability to run these and other circuits. Furthermore, maintainers of these quantum SDKs should use open-source tools for performance comparisons and publish their results publicly, not only so that developers can monitor the continuing development of quantum SDKs, but also so that the quantum community can work together to keep benchmarks relevant.
Software must be more than fast and efficient. In classical computing, software developers don’t have to reach down into the code and program individual bits. They can use a higher-level, more abstract language to harness the computer to carry out the desired tasks. In the same way, maintainers of quantum software development kits must create tools that “abstract away” the details of quantum circuits so that users don’t need to learn the intricacies of quantum computing hardware to write quantum code. These kinds of higher-level abstractions are beginning to emerge in today’s quantum SDK.
Over time, quantum software development kits should broaden their areas of application, moving toward domain-specific libraries of functions akin to those now used in fields such as chemistry simulation, machine learning, and optimization. Again, this will allow domain experts to integrate quantum computing without requiring deep quantum computing knowledge.
All of these requirements are critical in the search for quantum advantage, the point where quantum computers can provide substantial improvement to some problems now only feasible with slower classical computation. It will be then that useful quantum computing is brought to the world.
No single company or individual will bring about this new era. This is a global endeavor requiring a collaboration of physicists, engineers, developers, entrepreneurs, government officials, and more. It’s time to get started.
Amazon Web Services today announced Ocelot, its first-generation quantum computing chip. While the chip has only rudimentary computing capability, the company says it is a proof-of-principle demonstration—a step on the path to creating a larger machine that can deliver on the industry’s promised killer applications, such as fast and accurate simulations of new battery materials.
“This is a first prototype that demonstrates that this architecture is scalable and hardware-efficient,” says Oskar Painter, the head of quantum hardware at AWS, Amazon’s cloud computing unit. In particular, the company says its approach makes it simpler to perform error correction, a key technical challenge in the development of quantum computing.
Ocelot consists of nine quantum bits, or qubits, on a chip about a centimeter square, which, like some forms of quantum hardware, must be cryogenically cooled to near absolute zero in order to operate. Five of the nine qubits are a type of hardware that the field calls a “cat qubit,” named for Schrödinger’s cat, the famous 20th-century thought experiment in which an unseen cat in a box may be considered both dead and alive. Such a superposition of states is a key concept in quantum computing.
The cat qubits AWS has made are tiny hollow structures of tantalum that contain microwave radiation, attached to a silicon chip. The remaining four qubits are transmons—each an electric circuit made of superconducting material. In this architecture, AWS uses cat qubits to store the information, while the transmon qubits monitor the information in the cat qubits. This distinguishes its technology from Google’s and IBM’s quantum computers, whose computational parts are all transmons.
Notably, AWS researchers used Ocelot to implement a more efficient form of quantum error correction. Like any computer, quantum computers make mistakes. Without correction, these errors add up, with the result that current machines cannot accurately execute the long algorithms required for useful applications. “The only way you’re going to get a useful quantum computer is to implement quantum error correction,” says Painter.
Unfortunately, the algorithms required for quantum error correction usually have heavy hardware requirements. Last year, Google encoded a single error-corrected bit of quantum information using 105 qubits.
Amazon’s design strategy requires only a 10th as many qubits per bit of information, says Painter. In work published in Nature on Wednesday, the team encoded a single error-corrected bit of information in Ocelot’s nine qubits. Theoretically, this hardware design should be easier to scale up to a larger machine than a design made only of transmons, says Painter.
This design combining cat qubits and transmons makes error correction simpler, reducing the number of qubits needed, says Shruti Puri, a physicist at Yale University who was not involved in the work. (Puri works part-time for another company that develops quantum computers but spoke to MIT Technology Review in her capacity as an academic.)
The company says it is on track to build a new kind of machine based on topological qubits.
“Basically, you can decompose all quantum errors into two kinds—bit flips and phase flips,” says Puri. Quantum computers represent information as 1s, 0s, and probabilities, or superpositions, of both. A bit flip, which also occurs in conventional computing, takes place when the computer mistakenly encodes a 1 that should be a 0, or vice versa. In the case of quantum computing, the bit flip occurs when the computer encodes the probability of a 0 as the probability of a 1, or vice versa. A phase flip is a type of error unique to quantum computing, having to do with the wavelike properties of the qubit.
The cat-transmon design allowed Amazon to engineer the quantum computer so that any errors were predominantly phase-flip errors. This meant the company could use a much simpler error correction algorithm than Google’s—one that did not require as many qubits. “Your savings in hardware is coming from the fact that you need to mostly correct for one type of error,” says Puri. “The other error is happening very rarely.”
The hardware savings also stem from AWS’s careful implementation of an operation known as a C-NOT gate, which is performed during error correction. Amazon’s researchers showed that the C-NOT operation did not disproportionately introduce bit-flip errors. This meant that after each round of error correction, the quantum computer still predominantly made phase-flip errors, so the simple, hardware-efficient error correction code could continue to be used.
AWS began working on designs for Ocelot as early as 2021, says Painter. Its development was a “full-stack problem.” To create high-performing qubits that could ultimately execute error correction, the researchers had to figure out a new way to grow tantalum, which is what their cat qubits are made of, on a silicon chip with as few atomic-scale defects as possible.
It’s a significant advance that AWS can now fabricate and control multiple cat qubits in a single device, says Puri. “Any work that goes toward scaling up new kinds of qubits, I think, is interesting,” she says. Still, there are years of development to go. Other experts have predicted that quantum computers will require thousands, if not millions, of qubits to perform a useful task. Amazon’s work “is a first step,” says Puri.
She adds that the researchers will need to further reduce the fraction of errors due to bit flips as they scale up the number of qubits.
Still, this announcement marks Amazon’s way forward. “This is an architecture we believe in,” says Painter. Previously, the company’s main strategy was to pursue conventional transmon qubits like Google’s and IBM’s, and they treated this cat qubit project as “skunkworks,” he says. Now, they’ve decided to prioritize cat qubits. “We really became convinced that this needed to be our mainline engineering effort, and we’ll still do some exploratory things, but this is the direction we’re going.” (The startup Alice & Bob, based in France, is also building a quantum computer made of cat qubits.)
As is, Ocelot basically is a demonstration of quantum memory, says Painter. The next step is to add more qubits to the chip, encode more information, and perform actual computations. But they have many challenges ahead, from how to attach all the wires to how to link multiple chips together. “Scaling is not trivial,” he says.
It was a stranger who first brought home for me how big this year’s vibe shift was going to be. As we waited for a stuck elevator together in March, she told me she had just used ChatGPT to help her write a report for her marketing job. She hated writing reports because she didn’t think she was very good at it. But this time her manager had praised her. Did it feel like cheating? Hell no, she said. You do what you can to keep up.
That stranger’s experience of generative AI is one among millions. People in the street (and in elevators) are now figuring out what this radical new technology is for and wondering what it can do for them. In many ways the buzz around generative AI right now recalls the early days of the internet: there’s a sense of excitement and expectancy—and a feeling that we’re making it up as we go.
That is to say, we’re in the dot-com boom, circa 2000. Many companies will go bust. It may take years before we see this era’s Facebook (now Meta), Twitter (now X), or TikTok emerge. “People are reluctant to imagine what could be the future in 10 years, because no one wants to look foolish,” says Alison Smith, head of generative AI at Booz Allen Hamilton, a technology consulting firm. “But I think it’s going to be something wildly beyond our expectations.”
“Here’s the catch: it is impossible to know all the ways a technology will be misused until it is used.”
The internet changed everything—how we work and play, how we spend time with friends and family, how we learn, how we consume, how we fall in love, and so much more. But it also brought us cyber-bullying, revenge porn, and troll factories. It facilitated genocide, fueled mental-health crises, and made surveillance capitalism—with its addictive algorithms and predatory advertising—the dominant market force of our time. These downsides became clear only when people started using it in vast numbers and killer apps like social media arrived.
Generative AI is likely to be the same. With the infrastructure in place—the base generative models from OpenAI, Google, Meta, and a handful of others—people other than the ones who built it will start using and misusing it in ways its makers never dreamed of. “We’re not going to fully understand the potential and the risks without having individual users really play around with it,” says Smith.
Generative AI was trained on the internet and so has inherited many of its unsolved issues, including those related to bias, misinformation, copyright infringement, human rights abuses, and all-round economic upheaval. But we’re not going in blind.
Here are six unresolved questions to bear in mind as we watch the generative-AI revolution unfold. This time around, we have a chance to do better.
1
Will we ever mitigate the bias problem?
Bias has become a byword for AI-related harms, for good reason. Real-world data, especially text and images scraped from the internet, is riddled with it, from gender stereotypes to racial discrimination. Models trained on that data encode those biases and then reinforce them wherever they are used.
Without new data sets or a new way to train models (both of which could take years of work), the root cause of the bias problem is here to stay. But that hasn’t stopped it from being a hot topic of research. OpenAI has worked to make its large language models less biased using techniques such as reinforcement learning from human feedback (RLHF). This steers the output of a model toward the kind of text that human testers say they prefer.
Other techniques involve using synthetic data sets. For example,Runway, a startup that makes generative models for video production, has trained a version of the popular image-making model Stable Diffusion on synthetic data such as AI-generated images of people who vary in ethnicity, gender, profession, and age. The company reports that models trained on this data set generate more images of people with darker skin and more images of women. Request an image of a businessperson, and outputs now include women in headscarves; images of doctors will depict people who are diverse in skin color and gender; and so on.
Critics dismiss these solutions as Band-Aids on broken base models, hiding rather than fixing the problem. But Geoff Schaefer, a colleague of Smith’s at Booz Allen Hamilton who is head of responsible AI at the firm, argues that such algorithmic biases can expose societal biases in a way that’s useful in the long run.
As an example, he notes that even when explicit information about race is removed from a data set, racial bias can still skew data-driven decision-making because race can be inferred from people’s addresses—revealing patterns of segregation and housing discrimination. “We got a bunch of data together in one place, and that correlation became really clear,” he says.
Schaefer thinks something similar could happen with this generation of AI: “These biases across society are going to pop out.” And that will lead to more targeted policymaking, he says.
But many would balk at such optimism. Just because a problem is out in the open doesn’t guarantee it’s going to get fixed. Policymakers are still trying to address social biases that were exposed years ago—in housing, hiring, loans, policing, and more. In the meantime, individuals live with the consequences.
Prediction: Bias will continue to be an inherent feature of most generative AI models. But workarounds and rising awareness could help policymakers address the most obvious examples.
2
How will AI change the way we apply copyright?
Outraged that tech companies should profit from their work without consent, artists and writers (and coders) have launched class action lawsuits against OpenAI, Microsoft, and others, claiming copyright infringement. Getty is suing Stability AI, the firm behind the image maker Stable Diffusion.
These cases are a big deal. Celebrity claimants such as Sarah Silverman and George R.R. Martin have drawn media attention. And the cases are set to rewrite the rules around what does and does not count as fair use of another’s work, at least in the US.
But don’t hold your breath. It will be years before the courts make their final decisions, says Katie Gardner, a partner specializing in intellectual-property licensing at the law firm Gunderson Dettmer, which represents more than 280 AI companies. By that point, she says, “the technology will be so entrenched in the economy that it’s not going to be undone.”
In the meantime, the tech industry is building on these alleged infringements at breakneck pace. “I don’t expect companies will wait and see,” says Gardner. “There may be some legal risks, but there are so many other risks with not keeping up.”
Some companies have taken steps to limit the possibility of infringement. OpenAI and Meta claim to have introduced ways for creators to remove their work from future data sets. OpenAI now prevents users of DALL-E from requesting images in the style of living artists. But, Gardner says, “these are all actions to bolster their arguments in the litigation.”
Google, Microsoft, and OpenAI now offer to protect users of their models from potential legal action. Microsoft’s indemnification policyfor its generative coding assistant GitHub Copilot, which is the subject of a class action lawsuit on behalf of software developers whose code it was trained on, would in principle protect those who use it while the courts shake things out. “We’ll take that burden on so the users of our products don’t have to worry about it,” Microsoft CEO Satya Nadella told MIT Technology Review.
At the same time, new kinds of licensing deals are popping up. Shutterstock has signed a six-year deal with OpenAI for the use of its images. And Adobe claims its own image-making model, called Firefly, was trained only on licensed images, images from its Adobe Stock data set, or images no longer under copyright. Some contributors to Adobe Stock, however, say they weren’t consulted and aren’t happy about it.
Resentment is fierce. Now artists are fighting back with technology of their own. One tool, called Nightshade, lets users alter images in ways that are imperceptible to humans but devastating to machine-learning models, making them miscategorize images during training. Expect a big realignment of norms around sharing and repurposing media online.
Prediction: High-profile lawsuits will continue to draw attention, but that’s unlikely to stop companies from building on generative models. New marketplaces will spring up around ethical data sets, and a cat-and-mouse game between companies and creators will develop.
3
How will it change our jobs?
We’ve long heard that AI is coming for our jobs. One difference this time is that white-collar workers—data analysts, doctors, lawyers, and (gulp) journalists—look to be at risk too. Chatbots can ace high school tests, professional medical licensing examinations, and the bar exam. They can summarize meetings and even write basic news articles. What’s left for the rest of us? The truth is far from straightforward.
Last summer, Ethan Mollick, who studies innovation at the Wharton School of the University of Pennsylvania, helped run an experiment with the Boston Consulting Group to look at the impact of ChatGPT on consultants. The team gave hundreds of consultants 18 tasks related to a fictional shoe company, such as “Propose at least 10 ideas for a new shoe targeting an underserved market or sport” and “Segment the footwear industry market based on users.” Some of the group used ChatGPT to help them; some didn’t.
The results were striking: “Consultants using ChatGPT-4 outperformed those who did not, by a lot. On every dimension. Every way we measured performance,” Mollick writes in a blog post about the study.
Many businesses are already using large language models to find and fetch information, says Nathan Benaich, founder of the VC firm Air Street Capital and leader of the team behind the State of AI Report, a comprehensive annual summary of research and industry trends. He finds that welcome: “Hopefully, analysts will just become an AI model,” he says. “This stuff’s mostly a big pain in the ass.”
His point is that handing over grunt work to machines lets people focus on more fulfilling parts of their jobs. The tech also seems to level out skills across a workforce: early studies, like Mollick’s with consultants and otherswith coders, suggest that less experienced people get a bigger boost from using AI. (There are caveats, though. Mollick found that people who relied too much on GPT-4 got careless and were less likely to catch errors when the model made them.)
Generative AI won’t just change desk jobs. Image- and video-making models could make it possible to produce endless streams of pictures and film without human illustrators, camera operators, or actors. The strikes by writers and actors in the US in 2023 made it clear that this will be a flashpoint for years to come.
Even so, many researchers see this technology as empowering, not replacing, workers overall. Technology has been coming for jobs since the industrial revolution, after all. New jobs get created as old ones die out. “I feel really strongly that it is a net positive,” says Smith.
But change is always painful, and net gains can hide individual losses. Technological upheaval also tends to concentrate wealth and power, fueling inequality.
“In my mind, the question is no longer about whether AI is going to reshape work, but what we want that to mean,” writes Mollick.
Prediction: Fears of mass job losses will prove exaggerated. But generative tools will continue to proliferate in the workplace. Roles may change; new skills may need to be learned.
Using generative models to create fake text or images is easier than ever. Many warn of a misinformation overload. OpenAI has collaborated on research that highlights many potential misuses of its own tech for fake-news campaigns. In a 2023 report it warned that large language models could be used to produce more persuasive propaganda—harder to detect as such—at massive scales. Experts in the US and the EU are already saying that elections are at risk.
It was no surprise that the Biden administration made labeling and detection of AI-generated content a focus of its executive order on artificial intelligence in October. But the order fell short of legally requiring tool makers to label text or images as the creations of an AI. And the best detection tools don’t yet work well enough to be trusted.
The European Union’s AI Act, agreed this month, goes further. Part of the sweeping legislation requires companies to watermark AI-generated text, images, or video, and to make it clear to people when they are interacting with a chatbot. And the AI Act has teeth: the rules will be binding and come with steep fines for noncompliance.
These are three of the most viral images of 2023. All fake; all seen and shared by millions of people.
The US has also said it will audit any AI that might pose threats to national security, including election interference. It’s a great step, says Benaich. But even the developers of these models don’t know their full capabilities: “The idea that governments or other independent bodies could force companies to fully test their models before they’re released seems unrealistic.”
Here’s the catch: it’s impossible to know all the ways a technology will be misused until it is used. “In 2023 there was a lot of discussion about slowing down the development of AI,” says Schaefer. “But we take the opposite view.”
Unless these tools get used by as many people in as many different ways as possible, we’re not going to make them better, he says: “We’re not going to understand the nuanced ways that these weird risks will manifest or what events will trigger them.”
Prediction: New forms of misuse will continue to surface as use ramps up. There will be a few standout examples, possibly involving electoral manipulation.
5
Will we come to grips with its costs?
The development costs of generative AI, both human and environmental, are also to be reckoned with. The invisible-worker problem is an open secret: we are spared the worst of what generative models can produce thanks in part to crowds of hidden (often poorly paid) laborers who tag training data and weed out toxic, sometimes traumatic, output during testing. These are the sweatshops of the data age.
In 2023, OpenAI’s use of workers in Kenya came under scrutiny by popular media outlets such as Timeand the Wall Street Journal. OpenAI wanted to improve its generative models by building a filter that would hide hateful, obscene, and otherwise offensive content from users. But to do that it needed people to find and label a large number of examples of such toxic content so that its automatic filter could learn to spot them. OpenAI had hired the outsourcing firm Sama, which in turn is alleged to have used low-paid workers in Kenya who were given little support.
With generative AI now a mainstream concern, the human costs will come into sharper focus, putting pressure on companies building these models to address the labor conditions of workers around the world who are contracted to help improve their tech.
The other great cost, the amount of energy required to train large generative models, is set to climb before the situation gets better. In August, Nvidia announced Q2 2024 earnings of more than $13.5 billion, twice as much as the same period the year before. The bulk of that revenue ($10.3 billion) comes from data centers—in other words, other firms using Nvidia’s hardware to train AI models.
“The demand is pretty extraordinary,” says Nvidia CEO Jensen Huang. “We’re at liftoff for generative AI.” He acknowledges the energy problem and predicts that the boom could even drive a change in the type of computing hardware deployed. “The vast majority of the world’s computing infrastructure will have to be energy efficient,” he says.
Prediction: Greater public awareness of the labor and environmental costs of AI will put pressure on tech companies. But don’t expect significant improvement on either front soon.
6
Will doomerism continue to dominate policymaking?
Doomerism—the fear that the creation of smart machines could have disastrous, even apocalyptic consequences—has long been an undercurrent in AI. But peak hype, plus a high-profile announcement from AI pioneer Geoffrey Hinton in May that he was now scared of the tech he helped build, brought it to the surface.
Few issues in 2023 were as divisive. AI luminaries like Hinton and fellow Turing Award winner Yann LeCun, who founded Meta’s AI lab and who finds doomerism preposterous, engage in public spats, throwing shadeat each other on social media.
Hinton, OpenAI CEO Sam Altman, and others have suggested that (future) AI systems should have safeguards similar to those used for nuclear weapons. Such talk gets people’s attention. But in an article he co-wrote in Vox in July, Matt Korda, project manager for the Nuclear Information Project at the Federation of American Scientists, decried these “muddled analogies” and the “calorie-free media panic” they provoke.
It’s hard to understand what’s real and what’s not because we don’t know the incentives of the people raising alarms, says Benaich: “It does seem bizarre that many people are getting extremely wealthy off the back of this stuff, and a lot of the people are the same ones who are mandating for greater control. It’s like, ‘Hey, I’ve invented something that’s really powerful! It has a lot of risks, but I have the antidote.’”
Some worry about the impact of all this fearmongering. On X, deep-learning pioneer Andrew Ng wrote: “My greatest fear for the future of AI is if overhyped risks (such as human extinction) lets tech lobbyists get enacted stifling regulations that suppress open-source and crush innovation.” The debate also channels resources and researchers away from more immediate risks, such as bias, job upheavals, and misinformation (see above).
“Some people push existential risk because they think it will benefit their own company,” says François Chollet, an influential AI researcher at Google. “Talking about existential risk both highlights how ethically aware and responsible you are and distracts from more realistic and pressing issues.”
Benaich points out that some of the people ringing the alarm with one hand are raising $100 million for their companies with the other. “You could say that doomerism is a fundraising strategy,” he says.
Prediction: The fearmongering will die down, but the influence on policymakers’ agendas may be felt for some time. Calls to refocus on more immediate harms will continue.
Still missing: AI’s killer app
It’s strange to think that ChatGPT almost didn’t happen. Before its launch in November 2022, Ilya Sutskever, cofounder and chief scientist at OpenAI, wasn’t impressed by its accuracy. Others in the company worried it wasn’t much of an advance. Under the hood, ChatGPT was more remix than revolution. It was driven by GPT-3.5, a large language model that OpenAI had developed several months earlier. But the chatbot rolled a handful of engaging tweaks—in particular, responses that were more conversational and more on point—into one accessible package. “It was capable and convenient,” says Sutskever. “It was the first time AI progress became visible to people outside of AI.”
The hype kicked off by ChatGPT hasn’t yet run its course. “AI is the only game in town,” says Sutskever. “It’s the biggest thing in tech, and tech is the biggest thing in the economy. And I think that we will continue to be surprised by what AI can do.”
But now that we’ve seen what AI can do, maybe the immediate question is what it’s for. OpenAI built this technology without a real use in mind. Here’s a thing, the researchers seemed to say when they released ChatGPT. Do what you want with it. Everyone has been scrambling to figure out what that is since.
“I find ChatGPT useful,” says Sutskever. “I use it quite regularly for all kinds of random things.” He says he uses it to look up certain words, or to help him express himself more clearly. Sometimes he uses it to look up facts (even though it’s not always factual). Other people at OpenAI use it for vacation planning (“What are the top three diving spots in the world?”) or coding tips or IT support.
Useful, but not game-changing. Most of those examples can be done with existing tools, like search. Meanwhile, staff inside Google are said to be having doubts about the usefulness of the company’s own chatbot, Bard (now powered by Google’s GPT-4 rival, Gemini, launched last month). “The biggest challenge I’m still thinking of: what are LLMs truly useful for, in terms of helpfulness?” Cathy Pearl, a user experience lead for Bard, wrote on Discord in August, according to Bloomberg. “Like really making a difference. TBD!”
Without a killer app, the “wow” effect ebbs away. Stats from the investment firm Sequoia Capital show that despite viral launches, AI apps like ChatGPT, Character.ai, and Lensa, which lets users create stylized (and sexist) avatars of themselves, lose users faster than existing popular services like YouTube and Instagram and TikTok.
“The laws of consumer tech still apply,” says Benaich. “There will be a lot of experimentation, a lot of things dead in the water after a couple of months of hype.”
Of course, the early days of the internet were also littered with false starts. Before it changed the world, the dot-com boom ended in bust. There’s always the chance that today’s generative AI will fizzle out and be eclipsed by the next big thing to come along.
Whatever happens, now that AI is fully in the mainstream, niche concerns have become everyone’s problem. As Schaefer says, “We’re going to be forced to grapple with these issues in ways that we haven’t before.”
RISC-V is one of MIT Technology Review’s 10 Breakthrough Technologies of 2023. Explore the rest of the list here.
Python, Java, C++, R. In the seven decades or so since the computer was invented, humans have devised many programming languages—largely mishmashes of English words and mathematical symbols—to command transistors to do our bidding.
But the silicon switches in your laptop’s central processor don’t inherently understand the word “for” or the symbol “=.” For a chip to execute your Python code, software must translate these words and symbols into instructions a chip can use.
Engineers designate specific binary sequences to prompt the hardware to perform certain actions. The code “100000,” for example, could order a chip to add two numbers, while the code “100100” could ask it to copy a piece of data. These binary sequences form the chip’s fundamental vocabulary, known as the computer’s instruction set.
For years, the chip industry has relied on a variety of proprietary instruction sets. Two major types dominate the market today: x86, which is used by Intel and AMD, and Arm, made by the company of the same name. Companies must license these instruction sets—which can cost millions of dollars for a single design. And because x86 and Arm chips speak different languages, software developers must make a version of the same app to suit each instruction set.
Lately, though, many hardware and software companies worldwide have begun to converge around a publicly available instruction set known as RISC-V. It’s a shift that could radically change the chip industry. RISC-V proponents say that this instruction set makes computer chip design more accessible to smaller companies and budding entrepreneurs by liberating them from costly licensing fees.
“There are already billions of RISC-V-based cores out there, in everything from earbuds all the way up to cloud servers,” says Mark Himelstein, the CTO of RISC-V International, a nonprofit supporting the technology.
In February 2022, Intel itself pledged $1 billion to develop the RISC-V ecosystem, along with other priorities. While Himelstein predicts it will take a few years before RISC-V chips are widespread among personal computers, the first laptop with a RISC-V chip, the Roma by Xcalibyte and DeepComputing, became available in June for pre-order.
What is RISC-V?
You can think of RISC-V (pronounced “risk five”) as a set of design norms, like Bluetooth, for computer chips. It’s known as an “open standard.” That means anyone—you, me, Intel—can participate in the development of those standards. In addition, anyone can design a computer chip based on RISC-V’s instruction set. Those chips would then be able to execute any software designed for RISC-V. (Note that technology based on an “open standard” differs from “open-source” technology. An open standard typically designates technology specifications, whereas “open source” generally refers to software whose source code is freely available for reference and use.)
A group of computer scientists at UC Berkeley developed the basis for RISC-V in 2010 as a teaching tool for chip design. Proprietary central processing units (CPUs) were too complicated and opaque for students to learn from. RISC-V’s creators made the instruction set public and soon found themselves fielding questions about it. By 2015, a group of academic institutions and companies, including Google and IBM, founded RISC-V International to standardize the instruction set.
The most basic version of RISC-V consists of just 47 instructions, such as commands to load a number from memory and to add numbers together. However, RISC-V also offers more instructions, known as extensions, making it possible to add features such as vector math for running AI algorithms.
With RISC-V, you can design a chip’s instruction set to fit your needs, which “gives the freedom to do custom, application-driven hardware,” says Eric Mejdrich of Imec, a research institute in Belgium that focuses on nanoelectronics.
Previously, companies seeking CPUs generally bought off-the-shelf chips because it was too expensive and time-consuming to design them from scratch. Particularly for simpler devices such as alarms or kitchen appliances, these chips often had extra features, which could slow the appliance’s function or waste power.
Himelstein touts Bluetrum, an earbud company based in China, as a RISC-V success story. Earbuds don’t require much computing capability, and the company found it could design simple chips that use RISC-V instructions. “If they had not used RISC-V, either they would have had to buy a commercial chip with a lot more [capability] than they wanted, or they would have had to design their own chip or instruction set,” says Himelstein. “They didn’t want either of those.”
RISC-V helps to “lower the barrier of entry” to chip design, says Mejdrich. RISC-V proponents offer public workshops on how to build a CPU based on RISC-V. And people who design their own RISC-V chips can now submit those designs to be manufactured free of cost via a partnership between Google, semiconductor manufacturer SkyWater, and chip design platform Efabless.
What’s next for RISC-V
Balaji Baktha, the CEO of Bay Area–based startup Ventana Micro Systems, designs chips based on RISC-V for data centers. He says design improvements they’ve made—possible only because of the flexibility that an open standard affords—have allowed these chips to perform calculations more quickly with less energy. In 2021, data centers accounted for about 1% of total electricity consumed worldwide, and that figure has been rising over the past several years, according to the International Energy Agency. RISC-V chips could help lower that footprint significantly, according to Baktha.
However, Intel and Arm’s chips remain popular, and it’s not yet clear whether RISC-V designs will supersede them. Companies need to convert existing software to be RISC-V compatible (the Romasupports most versions of Linux, the operating system released in the 1990s that helped drive the open-source revolution). And RISC-V users will need to watch out for developments that “bifurcate the ecosystem,” says Mejdrich—for example, if somebody develops a version of RISC-V that becomes popular but is incompatible with software designed for the original.
RISC-V International must also contend with geopolitical tensionsthat are at odds with the nonprofit’s open philosophy. Originally based in the US, they faced criticism from lawmakers that RISC-V could cause the US to lose its edge in the semiconductor industry and make Chinese companies more competitive. To dodge these tensions, the nonprofit relocated to Switzerland in 2020.
Looking ahead, Himelstein says the movement will draw inspiration from Linux. The hope is that RISC-V will make it possible for more people to bring their ideas for novel technologies to life. “In the end, you’re going to see much more innovative products,” he says.
Sophia Chen is a science journalist based in Columbus, Ohio, who covers physics and computing. In 2022, she was the science communicator in residence at the Simons Institute for the Theory of Computing at the University of California, Berkeley.
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