Can artificial intelligence be so transformative as to solve one of the U.S. economy’s biggest problems: its skyrocketing fiscal deficit? According to three economists at the Brookings Institution, the answer is yes — AI could prove a positive “critical shock” for the country’s fiscal health.
A working paper released last month by the Center on Regulation and Markets at Brookings projects that under the most optimistic scenario, AI could reduce the annual U.S. budget deficit by as much as 1.5% of gross domestic product by 2044, or about $900 billion in nominal terms, lowering annual budget deficits by roughly one fifth at the end of the 20-year span.
“The use of AI presents the rare — possibly unique — opportunity to expand access to health care information and services while simultaneously reducing the burden on the conventional health care system,” the paper’s authors, Ben Harris, Neil Mehotra and Eric So, wrote.
While the authors name various channels through which AI can increase productivity, they highlight AI’s potential to dramatically improve health care services and public health.
Not only could AI make American health care more efficient, it might also “democratize” access to the system by giving people more options for preventative medical care — “changing the ‘who’ and ‘where’ of health care,” the economists wrote.
AI could ease deficit pressure
The economic impacts of a more efficient health care system, and giving individuals more paths to manage their own health, could ease pressure on the government’s yawning fiscal deficit, which topped $1.8 trillion in the fiscal year ended Sept. 30. The national debt stands at $36 trillion.
But adopting AI in health care services isn’t a sure thing. Plenty of impediments stand in the way of widely implementing AI, largely tied to regulation and incentives.
Economists’ outlook on AI and health care is “a mix of enthusiasm and despair,” said Ajay Agrawal, a professor at the University of Toronto’s Rotman School of Management ,where he researches the economics of artificial intelligence.
“Enthusiasm because there’s probably no sector that stands to benefit more from AI than health care. … But there’s friction due to regulation, due to incentives — because of the way things are structured and how people are paid for things — and friction due to the associated risks and liabilities,” Agrawal said.
“So yes, there’s lots of implementation challenges, and at the same time, the prize for succeeding at this is very big,” Agrawal said.
Health care and the deficit
The federal government spent an estimated $1.8 trillion on health insurance in 2023, or around 7% of GDP, according to the Congressional Budget Office. From 2024 to 2033, the CBO forecasts federal subsidies for health care will total $25 trillion, or 8.3% of GDP.
The problem is that so much health care spending in the U.S. isn’t tied to treatment or patient outcomes. Instead, about a quarter of all spending, public and private, is estimated to go toward administrative functions.
“Nearly every industry in the U.S. has experienced substantial improvements in productivity over the last 50 years, with 1 major exception: health care,” according to a report by McKinsey analysts.
This is one area where AI could improve operations, according to the Brookings Institution economists. Basic tasks such as appointment scheduling can be automated, while tasks such as patient flow management and preliminary data analysis can also be done by AI programs.
While the three economists acknowledge that the impact of AI on federal spending is still “highly uncertain,” the coauthors believe it could ultimately be more transformative for the economy than past technological leaps, such as the use of personal computers in the 1990s. The current AI shock “feels different. This isn’t your typical technological shock,” Harris told CNBC.
AI is affecting “how people receive health care,” how the drug industry discovers new products and how researchers make medicine more precise, Harris said.
Disease and death rates
In particular, Harris underscored AI’s impact not just on productivity, but also its potential to transform the cost of care and the rates of illness, disease and death.
“Such changes could have profound impacts on Social Security and public health program outlays,” he and his coauthors wrote.
To be sure, there is also the potential that AI advancements could counterintuitively increase federal spending if the average lifespan increases as a result of the technology. Not only could improved technology lead people to seek more medical care, longer lifespans might also result in a larger retired population.
But the Brookings paper takes a more optimistic tack, predicting one of AI’s largest benefits will result from accelerating the efficacy of preventative care and disease detection. This will create a healthier population that will need less medical intervention, the authors wrote — and might also increase labor force participation rates if a healthier workforce stays employed for more years.
“AI’s ability to improve diagnostic accuracy can not only improve patient outcomes but also reduce wasteful spending on inappropriate treatments,” the economists said. “From a more optimistic perspective, existing AI systems may lower expenditures on all health spending, including Medicare, with cost reductions occurring through several channels—with personalized medicine being a prominent example.”
Evaluating whether AI can ultimately translate into a positive or negative shock on fiscal policy will depend on what stage of the age distribution it affects, Agrawal said. Whether AI is “having its bigger impact on retired people, or around working people,” will answer how the numbers play out, Agrawal said.
AI proliferating already
So far, diagnostics has shown the most advances and greatest potential in applying AI in health care. Agrawal cited AI’s influence throughout almost all the steps of diagnostic care, from receiving input data, medical imagery such as X-rays and MRIs, as well as doctor notes, charts.
“In almost every area of diagnosis, AI has, in some cases, already demonstrated what they call ‘superhuman performance’ — better than than most docs,” Agrawal said.
AI has also shown “significant promise” in better optimizing treatment plans for patients through data analysis. Machine intelligence can develop more effective and less costly plans for individual patients, according to the authors of the paper.
Agrawal believes it’s too early to say whether public or private health systems will take better advantage of AI. In the U.S., private insurers have generally been more keen on AI technology associated with preventative treatment, he said. There’s been less interest in using AI in diagnostic applications, possibly that might lead to a rise in cases and more treatment, he said.
“There aren’t clear economic incentives for the private sector to [implement] that,” said Agrawal. “In the public sector, even though there are incentives, there are a lot of frictions associated with privacy on the data side.”
He believes public-private partnerships will be key in driving the rollout of AI across health care.
The public health care sector “will need very strong incentives in order to drive change, because otherwise, everybody is in their routine. There’s a lot of resistance to change,” Agrawal said.
“So to get over that resistance, you need a very strong motivator, and the private sector generally provides a much stronger motivator, either because the users are trying to reduce cost, or the creators of the technology are trying to generate profit,” he continued.
Large tech companies have already pushed forward in developing large language models specifically for health care services. Google’s AI system, Articulate Medical Intelligence Explore (AMIE), mimics diagnostic dialogue. Its Med-Gemini platform uses AI to aid in diagnosis, treatment planning and clinical decision support. Amazon and Microsoft have their own projects underway to expand the application of AI programs in health services.
Outlook under Trump
President-elect Donald Trump’s second term could alter the rollout of AI in health care, and ultimately, its economic impact. Trump has vowed to reduce government spending and formed an outside panel called the Department of Government Efficiency designed to “dismantle Government Bureaucracy, slash excess regulations, cut wasteful expenditures, and restructure Federal Agencies.” Public health funding is one area that could reduced funding, frustrating the ability to roll out AI applications.
“Now, it is possible that if you do see a retreat in the federal government’s role in providing health care to people, that more efficient AI could help compensate for the cost of that retreat,” said Harris. “If AI means that each dollar goes farther, then I think we’ve timed everything in a sort of lucky way.”
There’s also the chance that rolling back regulations under a second Trump administration could expedite the implementation of AI across health care.
“Many people are fearful of reducing regulation because they don’t want technologies that are immature to be brought into the health care system and harm people,” Agrawal said. “And that’s a very legitimate concern. But very often what they fail to also put into their equation is the harm we’re causing people by not bringing” in new technologies, he added.
“Some areas need a lot more technical development, but there are some domains in diagnosis that are already ready to go, and it’s just regulation that’s preventing them from being used,” Agrawal said.
On July 20, Andy Burnham has been chosen to be the next Prime Minister in UK. The appointment of a new Prime Minister in the United Kingdom often raises questions from people outside the country, especially when no nationwide election has taken place. Many wonder how a new national leader can assume office without voters casting ballots. The answer lies in the UK’s parliamentary system, where the Prime Minister is not directly elected by the public but is instead chosen based on who commands the confidence of the House of Commons.
How the UK Selects Its Prime Minister
Unlike presidential systems where citizens vote directly for the head of government, the United Kingdom elects Members of Parliament (MPs) during a general election. The political party that secures a majority of seats in the House of Commons usually forms the government, and that party selects its own leader to serve as Prime Minister.
If the leader resigns, becomes unable to continue, or is replaced by their party, the governing party can choose a new leader without triggering a general election. As long as the new leader is able to maintain the confidence of Parliament, they can immediately become Prime Minister after being formally appointed by the monarch.
Why No Election Was Required
A general election is not automatically required every time the office of Prime Minister changes hands. The governing party retains its parliamentary majority because voters elected MPs rather than an individual Prime Minister. If the ruling party chooses a new leader through its internal leadership process, the government continues to operate without interruption.
This constitutional arrangement provides stability and allows the government to continue functioning during periods of political transition. It also avoids the expense and disruption of holding a nationwide election every time party leadership changes.
The King’s Constitutional Role
After a governing party elects a new leader, the monarch invites that individual to form a government. This constitutional step is largely ceremonial and follows long-established conventions. The King appoints the person most likely to command a majority in the House of Commons, ensuring continuity of government.
Although the monarch formally appoints the Prime Minister, political power rests with Parliament and the elected representatives of the British people.
Could an Election Still Happen?
Yes. A newly appointed Prime Minister has the authority to request a general election if they believe it is politically advantageous or if they seek a stronger public mandate. Parliament can also reach a point where a government loses the confidence of the House of Commons, potentially leading to an election or the formation of a new government.
In many cases, however, a new Prime Minister continues governing until the next scheduled general election.
What This Means for the UK
The UK’s parliamentary democracy is designed to ensure government continuity while respecting the results of the most recent general election. Leadership changes within the governing party do not automatically alter the composition of Parliament, which is why a new Prime Minister can take office without another nationwide vote.
Understanding this process helps explain why political transitions in the United Kingdom can appear different from those in countries with presidential systems. While the Prime Minister may change, the democratic mandate of Parliament remains in place until voters elect a new House of Commons at the next general election.
On July 21, 2026, global economic analysis shifts focus toward a defining structural macroeconomic trend: the massive expansion of public and private capital deployment into high-capacity electrical grid infrastructure. As industrial electrification, automated data center hubs, and renewable energy integration accelerate worldwide, sovereign governments and institutional investors are facing a monumental economic challenge. Updating legacy power grids to meet skyrocketing demand has emerged as a primary driver of long-term capital expenditures and industrial productivity across both developed and emerging market economies.
According to international economic policy updates released this week, grid infrastructure investments are projected to exceed multi-trillion-dollar thresholds over the coming decade. Economic planners caution that without modernized, high-voltage transmission networks, regional manufacturing sectors face severe energy bottlenecks, localized power price volatility, and operational constraints. Consequently, infrastructure spending is rapidly transitioning from passive utility maintenance into a vital component of national economic competitiveness and industrial policy.
The macroeconomic ripple effects of this capital deployment are being felt across global commodity markets and labor networks. High demand for structural industrial inputs—such as copper, aluminum, specialized electrical steel, and high-capacity transformers—has created sustained pricing support for critical material producers. Simultaneously, the specialized technical labor required to manufacture and deploy modern grid hardware is driving wage growth in industrial sectors, adding a complex new layer to central bank disinflation trajectories.
For global policymakers and strategic investors, the economics of energy grid modernization represent a double-edged sword. While massive infrastructure investment boosts short-term gross domestic product (GDP) and strengthens domestic industrial foundations, it requires disciplined fiscal allocation to prevent inflationary crowding-out of private capital. Countries that efficiently streamline grid infrastructure permitting and mobilize private investment will secure lower long-term energy costs, attracting high-tech manufacturing and reinforcing sustainable economic growth.
The international trade architecture entering the second half of 2026 is undergoing a profound structural pivot. As major sovereign economic blocs adjust to the long-term impact of unilateral tariffs and escalating regional subsidies, traditional globalized supply chains are being rapidly replaced by bilateral trade corridors and regional alliance networks. Data released in late July 2026 highlights a significant divergence: while cross-continental freight volumes between non-aligned partners have cooled, intra-regional trade throughout North America, Southeast Asia, and Eastern Europe has surged to record levels. This shift reflects a broader macroeconomic strategy wherein multinational corporations prioritize geopolitical resilience over pure cost minimization.
The primary economic catalyst behind this regionalization is the proliferation of sector-specific tariffs targeting critical industries, notably battery components, clean energy technology, and advanced semiconductor hardware. In response, global manufacturers have adopted multi-tier sourcing models that distribute production across intermediate partner nations before final assembly. While this strategy successfully bypasses primary import duties, it adds structural layers of logistical complexity and administrative oversight. Economists note that while total output remains robust, aggregate production costs have drifted upward, contributing to persistent baseline inflation across major consumer markets.
Simultaneously, currency settlement patterns within these regional blocs are experiencing a notable transformation. Sovereign central banks and commercial institutions are increasingly utilizing localized currency swap lines and digital clearing mechanisms to settle cross-border trade transactions. This transition reduces direct exposure to foreign exchange volatility and mitigates third-party liquidity constraints, further solidifying regional economic cohesion. However, for developing economies situated outside these primary trading alliances, the tightening of international trade networks presents severe challenges, restricting access to key export markets and foreign direct investment.
For corporate strategists and policy analysts navigating late 2026, success requires a thorough understanding of these emerging trade corridors. Organizations must conduct regular risk assessments of their multi-tier supplier networks, model tariff sensitivities under shifting geopolitical scenarios, and invest in real-time supply chain telemetry. As regional economic blocs strengthen their regulatory borders, supply chain agility and compliance fortitude will distinguish market leaders from vulnerable enterprises in the evolving global economy.