With gasoline prices still high – averaging over US$4.50 a gallon in mid-May 2026 – President Donald Trump said he wanted Congress to suspend the federal gas tax, which is 18.4 cents a gallon for gasoline and 24.3 cents a gallon for diesel. A bill has been introduced in the Senate, and one is expected to follow in the House, according to Politico, but their fate is unclear.

States also charge their own taxes, ranging from 70.9 cents a gallon for gas in California to 8.95 cents in Alaska. Indiana, Georgia and Utah have suspended their gas taxes for at least some of 2026, and other states are considering similar measures.

As an energy economist, I have seen how suspending those taxes does reduce prices, but not as much as politicians – or drivers – might hope. Research on past gas tax holidays has found that consumers get about 79% of the reduction in gas taxes. That means oil companies and fuel retailers keep about one-fifth of the tax cut for themselves rather than passing that savings to the public.

Suspending the federal gas tax, which would require Congress to pass a law, wouldn’t help consumers much anyway. Even if oil companies passed on the whole savings to consumers, national average gas and diesel prices would drop only about 4%. The percentage reduction in high-cost states such as California would be even smaller.

Gas taxes are just one part of what drives gas prices. Overall, the price of a retail gallon of gas is the sum of four things: the cost of crude oil, refining, distribution and marketing, and taxes.

In nationwide figures from January 2026, crude oil accounted for about 51% of the pump price, refining roughly 20%, distribution and marketing about 11% and taxes about 18%. That mix shifts with conditions: When crude oil prices spike, that can drive more than 60% of the price; when the price drops, taxes and logistics are larger shares of the cost.

Crude oil is the biggest ingredient

Because the price of crude oil is the largest element, most of the price at the pump is derived from the global oil market.

Usually, big swings in crude prices come mainly from shifts in global demand and expectations – not from supply disruptions, according to widely cited research in 2009 by the economist Lutz Kilian.

But what is happening in early 2026 with the war in Iran is one of the exceptions: a classic supply shock. Severe disruptions to shipping through the Strait of Hormuz and attacks on Middle East oil infrastructure have taken millions of barrels a day off the global market.

Most drivers generally can’t quickly reduce how much they drive or how much gas they use when prices rise, so gasoline demand doesn’t change much in the short run. That means a jump in crude costs tends to result in people paying more rather than driving less.

Refining, regulations and the California puzzle

Refining turns crude into gasoline at industrial scale. The U.S. doesn’t have a single gasoline market, though. Roughly a quarter of U.S. gasoline is a cleaner-burning blend of petroleum-derived chemicals called “reformulated gasoline,” which is required in urban areas across 17 states and the District of Columbia to reduce smog.

California uses an even stricter formulation that few out-of-state refineries make. California is also geographically isolated: No pipelines bring gasoline in from other U.S. refining regions.

California’s gasoline prices have long run above the national average, explained in part by higher state taxes and stricter environmental rules. But since a refinery fire in Torrance, California, in 2015 reduced production capacity, the state’s prices have been about 20 to 30 cents a gallon higher than what those factors would indicate.

Energy economist and University of California, Berkeley, professor Severin Borenstein has called this the “mystery gasoline surcharge” and attributes it to the fact that there isn’t as much competition between refineries or gas stations in California as in other states. California’s own Division of Petroleum Market Oversight says the surcharge cost the state’s drivers about $59 billion from 2015 to 2024. It’s not exactly clear who is getting that money, but it could be gas stations themselves or refineries, through complex contracts with gas stations.

Getting the gas into your car

The distribution and marketing category covers the costs of everything involved in getting the gasoline from the refinery gate to your tank.

Gasoline moves by pipeline, ship, rail and truck to wholesale terminals, and then by local delivery truck to service stations.

At the retailer’s end, the key factors are station rent and labor, the cost to buy gasoline in bulk to be able to sell it, credit card fees of as much as 6 to 10 cents a gallon at current prices, and franchise fees paid to the national brand, such as Sunoco or ExxonMobil, for permission to put their branding on the gas station.

Most gas station operators net only a few cents per gallon on fuel itself – which is why many gas stations are really convenience stores with pumps out front. Borenstein and some of his collaborators have also documented that retail gas prices rise quickly when wholesale costs climb but fall slowly when wholesale costs drop.

The question of gas tax holidays

Gas tax holidays reduce funding for what the taxes are designed to pay for, typically roads and bridges. That pushes road and bridge upkeep costs onto future drivers and general taxpayers.

There is an additional problem, too: Taxes on gasoline are supposed to charge drivers for some of the costs their driving imposes on everyone else – carbon emissions, local air pollution, congestion and crashes. But Borenstein has found that U.S. fuel tax levels are already far below the true cost to society. Removing the tax on drivers effectively raises the costs for everyone else.

The Jones Act: A small number that adds up

The 1920 Jones Act is a federal law that requires cargo moving between U.S. ports to travel on vessels built and registered in the U.S., owned by U.S. citizens, and crewed primarily by U.S. citizens and permanent residents. Of the world’s 7,500 oil tankers, only 54 meet this requirement. Only 43 of these can transport refined fuels such as gasoline.

So, despite significant refining capacity on the Gulf Coast, some U.S. gasoline is exported overseas even as the Northeast imports fuel, in part reflecting the relatively high cost of moving fuel between U.S. ports.

Economists Ryan Kellogg and Rich Sweeney estimate that the law raises East Coast gasoline prices by about a penny and a half per gallon on average, costing drivers roughly $770 million a year. In light of the war’s effect on gas prices, the Trump administration has temporarily suspended the Jones Act requirements – an action more commonly taken when hurricanes knock out Gulf Coast refineries and pipeline networks.

What moves the number

The result of all these factors is that the price that drivers see at the pump mostly reflects the global price of crude, plus a stack of domestic costs, only some of which are inefficient.

Tax holidays give a partial, short-lived rebate. Jones Act waivers trim pennies, though permanent repeal may cause more fundamental changes, such as reduced rail and truck transport of all goods, which could lower costs, emissions and infrastructure damage associated with cargo transportation. Harmonizing fuel blends across states and seasons may lower prices somewhat, but likely at the expense of increased emissions.

Ultimately, the best protection against oil price shocks is a more efficient gas-burning vehicle, or one that doesn’t burn gasoline at all. In the meantime, the best I can offer as an economist is clarity about what that $4.50 actually buys.

This article includes material previously published on May 1, 2026.

Robert I. Harris does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

The coordinated U.S.-Israeli strikes at the outset of the war in Iran killed Supreme Leader Ali Khamenei, along with other key regime figures. In doing so, the United States and Israel crossed what The New York Times and others described as “a new Rubicon”: the deliberate, overt killing of a head of state.

President Donald Trump and Israeli Prime Minister Benjamin Netanyahu framed their war not simply as retaliation or coercion, but as an opening for political collapse. Remove enough of the leadership, the logic ran, and the structure beneath it either breaks apart or becomes vulnerable enough for a public uprising to finish the job.

Yet as a former senior U.S. intelligence officer who held leadership roles at the CIA and National Counterterrorism Center, I believe such triumphalist logic masks the strategic shortcoming of such targeted killings.

Disruption is not the same as collapse

Most scholars, too, have concluded that targeted killings or assassinations, often referred to as leadership decapitation, can disrupt operations and degrade organizational effectiveness. Under some conditions, they can even force the targeted side to capitulate. But they rarely lead to collapse.

The work of Jenna Jordan, a scholar of international relations at Georgia Tech, remains one of the clearest warnings against inflated expectations about anticipated effect of such strikes. Across a large body of cases of targeting killings of non-state militant groups, she found that older, larger, more institutionalized organizations are harder to break down through leadership removal than small, young, weakly structured ones.

Patrick Johnston, a former director of the Counterterrorism Center at West Point who has studied counterinsurgency campaigns, found more evidence that decapitation can help end conflicts than Jordan did. Other research has backed-up Johnston’s conclusion that some terrorist groups are vulnerable to leadership targeting.

But even these more favorable studies point to only conditional gains; they do not treat decapitation as a path to automatic political success or as a substitute for broader strategy.

Targeting heads of state is even more fraught

In counterterrorism efforts, disruption may be a good enough outcome for policymakers. Indeed, if the objective is to delay attacks or degrade operational effectiveness, leadership removal can have value. That was how the U.S. campaign against al-Qaeda was generally understood by American policymakers. Even Osama bin Laden’s death and repeated strikes against senior deputies were treated as major blows, not as proof that the organization had ceased to exist or no longer mattered as an operational threat.

Yet when the target is a state, the political bar is even higher. Tactical disruption is, again, not the same as political collapse. It is also not the same as creating a more favorable bargaining environment for the country relying on assassinations.

That distinction matters because recent scholarship has found that killing or capturing leaders may weaken an adversary on the battlefield but does not necessarily tell us how an adversary will respond politically — whether it becomes more willing to bargain, less able to negotiate, or more determined to keep fighting.

Removing another country’s leaders may weaken it in the short term, while changing who is left to negotiate, compromise or escalate. A strike could therefore succeed operationally while narrowing the political options that follow.

Iran’s response to the initial killing of senior leaders in the opening days of the current conflict illustrates the point. Khamenei’s death staggered the government, but it did not break it. Within little more than a week, Iran’s Assembly of Experts appointed Mojtaba Khamenei, his son, as supreme leader.

The government redistributed authority through institutions built to survive political shock: the clerical establishment, the Islamic Revolutionary Guard Corps, and the broader security bureaucracy.

The assassinations did not create a pathway for coercion, negotiation, or popular uprising. Indeed, as the ongoing lack of a long-term resolution to the conflict shows, the Trump administration is not now dealing with a more pliable Iran. Rather, it is facing a state steered by a successor leadership with an agenda even more hostile to U.S. policy in the Middle East, stronger incentives to prolong the conflict and a demonstrated willingness to absorb the pain of defiance.

Israel has long used targeted killing to disrupt adversaries — most visibly in its recent campaigns against Hamas and Hezbollah — but the Iran case shows the danger of turning the tool into a theory of political transformation.

A broader phenomenon

That same gap between tactical achievement and strategic effect appears in other settings as well.

Recent scholary work on criminal organizations in Latin America finds that state decapitation campaigns are often associated with short-term increases in violence, including clashes with state forces, even when they damage the targeted organization.

For example, in February 2026, Mexican forces killed Nemesio Oseguera Cervantes, better known as El Mencho, the head of the Jalisco New Generation Cartel. Yet, according to reporting, the organization continues to operate, with its core operations and networks largely intact. Meanwhile, reprisals followed quickly: 25 members of Mexico’s National Guard were killed and blockades and arson was seen across several states.

Leadership removal imposed a tactical cost, but it did not translate neatly into strategic collapse.

And yet the appeal persists

So why does decapitation remain so attractive? James Walsh, a scholar of political violence, intelligence and armed conflict at the University of North Carolina in Charlotte, suggests targeted killing gives policymakers a means to measure progress in a conflict where success is otherwise difficult to define. It produces a name and a result — often a photo and, in some cases, footage of the strike that can be shown at a press conference. It may not be less complex than diplomacy in operational terms, but it is often easier to explain politically: a strike can be presented as action, while negotiations require patience, trade-offs and the risk of appearing to compromise with an enemy.

In the case of El Mencho, his death gave Mexican President Claudia Sheinbaum a political trophy as well as a tactical victory. It allowed her to show action against cartel power at a moment of domestic strain over cartel violence and sustained U.S. pressure for Mexico to take a harder line. A named target and a confirmed death are easier to present as progress.

A similar dynamic may be present in the Ukraine-Russian war. Russian President Vladimir Putin is reportedly hunkered down in fear of an assassination attempt, most plausibly from Ukraine. But Putin’s death would not, by itself, end Russia’s war or dissolve the Russian state.

A successful strike against the man most identified with the invasion would, though, have an immeasurable rallying effect for Ukrainians after years of sacrifice. The reverse would also be true if a Russian operation killed Ukrainian President Volodymyr Zelenskyy. The political and symbolic shock would be enormous, but neither country’s war effort would necessarily crumble.

High-level decapitations can impose costs, degrade an organization or state’s capacity and force adversaries to operate under sustained pressure. But they cannot, according to the evidence, translate tactical achievement into the political outcomes that leaders invoke to justify such targeted killings.

That is, I believe, the lesson the war in Iran should have reinforced. Whatever the arguments for or against assassination as a matter of state policy, decapitation is a tool of disruption not of transformation. It becomes a strategic error when leaders treat it as the latter.

Brian O'Neill does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Across corporate earnings calls, investor presentations and marketing pitches, “artificial intelligence” has become the buzzword of choice. Yet a troubling pattern lies under the hype. Many claims vastly overstate actual AI sophistication, misleading people about true capabilities, future outcomes and potential harms.

A case in point is the recent 600% share price surge of Allbirds, after the once-trendy sustainable footwear business issued a vague announcement in April 2026 that it would pivot to AI. In the coming months, the company plans to rename itself NewBird AI and give up its status as a public benefit corporation.

As a scholar who studies corporate sustainability, I see parallels between this “AI washing” phenomenon – when companies oversell the benefits of AI while glossing over the risks – and the greenwashing trend in the recent past, when companies claimed to commit to sustainability but didn’t enact fundamental change. Widespread deception was rampant, with businesses spending far more on green marketing than on actual sustainability improvements. And those efforts often backfired on both the companies and the communities they served. Even more worrisome: AI washing’s rapid rise and widespread adoption will likely eclipse the greenwashing trends.

How we got here

AI washing is thriving because companies and policymakers ignore four important principles. These shortfalls, in the past, also characterized greenwashing.

First, AI guidelines lack standardization. By 2019, 84 sets of AI ethics principles and guidelines had already been published. By 2023, this number had exploded to more than 200 – a mess of voluntary frameworks from companies, research institutions and public organizations.

Making matters worse is that the U.S. currently relies on fragmented AI rules, with most being voluntary. The Trump administration has generally sided with Big Tech to push back efforts at state or federal regulation. At a global level, one of the few exceptions is the European Union AI Act, perhaps one of the most comprehensive frameworks, but its implementation won’t be fully phased in until 2027 or later.

In the early 2000s, corporate sustainability faced a similar credibility crisis. Every company measured sustainability differently, making comparisons impossible and greenwashing easy. The breakthrough came only when standardized, industry-specific metrics allowed meaningful benchmarking. Initiatives such as the Global Reporting Initiative and Sustainability Accounting Standards Board established common metrics for measuring environmental impact, social responsibility and quality of governance, known by the shorthand ESG.

When companies must report carbon emissions using the same methodology, for example, or disclose labor conditions using identical categories, investors can compare performance, identify laggards and allocate capital accordingly. This push made comparisons possible and deception harder, although it still wasn’t foolproof. For example, a 2023 United Nations Environment Programme report on the fast-fashion industry found that many companies continue to make “vague and inflated” sustainability claims.

Second, there are no comprehensive frameworks in the U.S. that require businesses to judge how AI affects them in a material way and publicly disclose those impacts. Examples of AI-driven material impacts include whether algorithmic bias shapes business outcomes, or whether decisions on how to use AI systems carry significance for shareholders and the public.

Instead, AI governance remains dominated by the narrow inner circle of companies that build the AI systems, while affected communities rarely have a say in determining which AI impacts are material enough to warrant public attention. For example, Big Tech companies like Google, Microsoft, Apple, NVIDIA and others adhere to their own AI governance guidelines, with relatively little public input.

The development of sustainability principles offers some examples of how to build these frameworks. The EU’s Corporate Sustainability Reporting Directive requires over 50,000 companies to formally evaluate which sustainability topics are material to their stakeholders, and then disclose that information. These efforts try to ensure that accountability is clear across entire supply chains.

While nowhere nearly as comprehensive, U.S. regulations such as the 2010 Dodd-Frank financial reform and California’s law requiring reporting on statewide greenhouse gas emissions provide a similar blueprint that U.S. policymakers could build on if they chose.

A third problem is the general lack of third-party verification, making AI washing trivially easy. Effective disclosure means reporting all material impacts – not just cherry-picked successes.

In practice, AI audits can vary dramatically in rigor, scope and methodology. One auditor might conduct extensive testing across demographic groups, analyze decision-making and validate the quality of training data. Another might simply review documentation and accept company explanations at face value. Given the variety of AI auditing models out there, different auditors may use incompatible methodologies, making results impossible to compare. If companies adopted third-party accreditation systems to assess how they use AI, they would help ensure the accountability that self-reported claims cannot match.

By contrast, there was reasonable progress in this respect as companies adopted ESG principles. For example, institutions such as the Carbon Disclosure Project and Global Reporting Initiative have a network of partners that offer independent verification. These providers, certified under international standards, verify corporate sustainability data against rigorous criteria. That way, they provide the assurance that lets companies show the progress needed to unlock sustainable finance and mitigate legal risks. Third-party audits are far from perfect, but they offer a clear path for improvement.

The fourth principle is robust enforcement. Early ESG initiatives relied on reputational pressure and stakeholder goodwill – things that corporations routinely ignored when profits were at stake. When change came, it was because regulations established legal liability and financial penalties.

These consequences changed how corporations assess risk and continue to shape sustainability practices today. Volkswagen’s 2015 ‘Dieselgate’ scandal, for example, cost the company over US$30 billion in fines, settlements and criminal charges after U.S regulators found that the carmaker was cheating emissions tests. BP faced billions in penalties and liabilities for the 2010 Deepwater Horizon disaster, the biggest oil spill in the history of marine oil drilling operations.

The current enforcement gap in AI creates a predictable dynamic. The expected value of AI washing – like potential investment gains, competitive advantage, and market valuation increases – far exceeds the expected cost in terms of penalties and risk of detection. Until enforcement imposes consequences that exceed benefits, AI washing will persist as a rational business strategy rather than a risk to a business’s reputation.

Fortunately, investors are beginning to step up. The Federal Trade Commission, for example, launched Operation AI Comply in 2024, targeting deceptive AI claims, although this effort has been partially scaled back by the current Trump administration.

New standards for a new era

Until businesses address these four principles, AI washing will continue. Without standards and audits, even well-intentioned companies can’t know if their work meets adequate rigor. Without assessments of material impact, some groups of consumers or shareholders will be hurt. And without liability, even thorough auditors won’t be able to identify whether a business’s claims about AI are truthful.

These principles, applied broadly, also help explain why greenwashing persists. For example, the lack of universal reporting standards continues to leave some gaps, with data-quality issues persisting even as reporting frameworks emerged. More fundamentally, political buy-in for ESG has diminished significantly, particularly in the U.S., where over 150 bills were introduced across multiple states by 2023 to disincentivize firms from adopting ESG. Major financial institutions – including JP Morgan, State Street, BlackRock and PIMCO – have retreated from their earlier climate commitments amid political pressure as well as antitrust concerns.

This trend shows that even well designed accountability measures require durable political support to succeed. After all, corporate sustainability took more than 25 years to develop from an initial framework to mandatory standards, and it still remains a work in progress. AI, by contrast, is advancing exponentially in terms of its reach and societal impact. There may not be 25 years to catch up – but at least there are lessons from the recent past.

Suvrat Dhanorkar does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

When was the last time you paid attention to your commute? And I don’t mean a couple of feet in front of you, at the car merging into your lane without a blinker. I mean really paid attention to the route you take.

Did you see the landmarks in the distance that make up the city skyline? Did you drive right past the grocery store you promised to stop by at the corner of this Peachtree Street or that Peachtree Street, a struggle Atlanta locals know well?

“Oops! Force of habit,” you might say to yourself as you miss your turn and begin to think about when and where you can turn around.

Relying on familiarity can either facilitate or impede daily navigation. As a researcher studying memory and navigation, I aim to understand how the brain supports spatial navigation and what happens if the cognitive mechanisms for choosing the best route home begin to decline, such as during stress or with aging.

Humans are creatures of habit – at least that’s what people tell themselves when wary of trying something new. But what if a new route is faster or safer than the one you usually take? Would you try it?

Research from my team suggests that people balance between exploration and habit – that is, trying something new or sticking with the familiar – when deciding what route to take. Which navigation strategy someone chooses depends not only on their spatial abilities but on their network of brain regions that support navigation.

A spatial blueprint

Spatial navigation refers to the cognitive ability that helps you travel from one location to another. It may sound simple, but it requires using cognitive functions such as memory, attention, decision-making and assessing potential rewards – never mind the ability to simply perceive the environment itself.

Spatial navigation uses memories of things you consciously experienced. Two types of memory relevant to navigation are what scientists call episodic and semantic.

For example, you might retrieve an episodic memory about a specific event: remembering a detour you took a week ago to drop a package off at the post office, including the traffic and weather that day.

You might also retrieve a semantic memory that’s more factual and knowledge-based: remembering how many blocks away the post office is from the park and the turns you need to make to get there.

Together, these kinds of memory inform your spatial memory, which allows you to retrieve location information. This could be where buildings are in relation to each other or where objects are situated in your house. Spatial memories help form your cognitive map, which is essential for getting around in the world.

Often, these different ways of remembering interact, and you can use one type of memory to inform the other. For example, you’ve become accustomed to your commute to work and know it’s relatively short (semantic memory), but over the past three days you’ve been arriving late due to heavy traffic (episodic memory), so you choose to take a different route next time.

Research from my team has found that disagreements in your brain over possible routes can happen. Different types of memory can come up with different solutions for what route you can take, and this conflict is a big factor in how hard your brain needs to work when navigating an environment.

Responding to new and familiar memories

Habits stem from what researchers call stimulus-response memories. These include the knee-jerk reaction you might have to familiar landmarks – when you perceive these places, your brain signals you to make a turn along your commute without needing to consciously think about it.

Habits are rigid, but they can also be beneficial: By taking care of the navigation for you, habit frees up your brain to have a conversation with someone or plan what to make for dinner when you get home.

When navigating less familiar routes or environments, where habit doesn’t kick in automatically, you rely on brain regions such as the hippocampus to call on detailed memories from recent experiences to help guide the way.

But let’s say you’re shopping at a new grocery store where most things are where you expect them to be, even though you’ve never been in this particular store before. What happens when your brain experiences both something new and something familiar about an environment?

Researchers have shown that when something about an environment is familiar and aligns with your prior experiences, the prefrontal regions of your brain – those responsible for executive functions such as decision-making – become more active. They can bypass or even inhibit your hippocampus’s ability to form new memories about specific events.

In other words, your brain can weave information about a new experience into your database of existing knowledge, rather than storing it as completely new information with little relation to the past. This process may help fast-track your understanding about new experiences.

Updating cognitive maps

Researchers know that cognitive maps of the environment depend on the hippocampus and its database of memories about specific events. However, I and other researchers argue these maps can also function as a schema – a collection of memories made up of associations between environmental details. You can add new information to these collections and use it to infer new relationships.

Say a new pedestrian bridge is built between the park and the post office. Your brain can more easily weave this new route information into your existing memories compared with learning a new environment from scratch. Similarly, if you just moved to a new town and know very little about the spatial layout, you might rely on your past experiences of towns to infer where something is.

Using neuroimaging techniques as well as virtual reality programs designed to test a participant’s ability to navigate different routes, my team found that there is likely an interdependent relationship between the brain areas that store memories of specific events and areas that store related information across memories when planning to navigate less familiar places.

New routes are more difficult to follow when they differ from your prior experiences. Thus, a stronger schema helps integrate your knowledge of the spatial relationships between locations and landmarks (such as the distance between the post office and the park) with more general knowledge (such as prior route difficulty). This all informs how you choose to navigate.

Navigating daily life

These memory principles help explain why inconsistencies with your previous experiences can make it so difficult to navigate many aspects of daily life.

Imagine you woke up tomorrow and the GPS on your smartphone was no longer available. How will you plan your route to get to your destination?

You might be used to navigating north from your home to the grocery store – but have you ever tried to navigate to that grocery story from a different location? It’s much harder!

Factors such as stress, aging and general cognitive decline can affect brain function and human behavior. Imagine how much harder that new route to the grocery store is for an older adult.

Relating new information to your prior experiences may help strengthen your schema and make navigation easier. And understanding what processes the brain needs to go through to solve these navigation problems can help you understand why getting around can be challenging.

This work was supported in part by grants from the National Institute on Aging of the NIH.