The United States is at risk of paralysis.

Time and time again, in one sector after another, we articulate strategies and set objectives, but we usually fall short, because we’ve failed to take into account the crucial element of time. Tasks we once accomplished swiftly now drag on for years. Problems we once resolved efficiently now prove interminable. Without incorporating time into our strategic calculations, we will always be too late.  

I saw this firsthand while I worked at the White House. In 2017, as deputy national security adviser for strategy, I helped write the U.S. National Security Strategy. I knew that actually implementing the initiatives it detailed would be harder than writing it. Presidential executive orders and White House strategies are merely aspirational until they are linked to budgets, and until tasks are assigned to the organizations that must implement them. But even that is not enough. The NSS outlines priorities, but it does not specify when they must be achieved or provide a mechanism to track how long they are taking. The result is a problem that plagues Democratic and Republican administrations alike, as their initiatives aren’t executed swiftly enough to accomplish what they are intended to achieve. Examples abound:

American leaders consistently emphasize the need to reduce dependence on China for crucial minerals, and although we have abundant domestic resources, it still takes well over a decade, on average, to open a new mine in the United States.

[Eric Schmidt and Robert O. Work: How to stop the next world war]

Top military leaders lament that we have lost the art of moving fast and that unless we accelerate required changes, we won’t be prepared to deter and win wars. China is acquiring high-end weapons systems and equipment five to six times faster than the United States. As we continue to provide Ukraine with ammunition and other military equipment, we struggle with replenishing our stockpiles of arms and munitions. For some weapons systems, replenishment will take at least five years, just to restore stocks that were already inadequate to sustain a major conflict. This is particularly problematic, because the United States might run out of certain munitions in less than a week should conflict with China erupt over Taiwan.

We desperately need more ships, but maintenance delays for naval vessels result in, as one retired admiral put it, “the equivalent of losing half an aircraft carrier and three submarines each year.” Those numbers continue to move in the wrong direction. And many of our weapons systems depend on software upgrades; any delays can render them obsolescent.

The problems are not limited to national security. Climate change has been deemed an existential threat for decades, but we have made little progress in mitigating the effects of global warming. It is hard to reconcile an existential threat with glacial progress. The U.S. Citizenship and Immigration Services struggles to process immigrants in a timely fashion, even with staffing increases and technological improvements. Similarly, U.S. officials have complained about the lack of qualified STEM workers for years, without succeeding in addressing the problem.

The United States finds itself unable to get important things done at what military leaders have called the “speed of relevance.” New technology is disrupting existing political, economic, and regulatory architectures faster than they can be rebuilt, exposing a growing gap between the promises of leaders and their ability to deliver. This gap between promises and outcome creates cynicism at home and abroad. Americans are skeptical that government can adapt and reform, given the speed of technological change. Internationally, friends and allies are questioning U.S. competence. Our rivals, in turn, register our inability to deliver, weakening deterrence.

Time wasn’t always a problem as the Pentagon itself proves. The construction of what is still by far the largest office building on Earth took just 16 months. The construction supervisor, Leslie Groves, was known as “the biggest S.O.B around,” a man who “had the guts to make difficult decisions,” as one subordinate later recalled. He demanded decisions in 24 hours or less, or an explanation. In 1942, President Franklin D. Roosevelt’s scientific adviser Vannevar Bush said that nothing should stand in the way of a program to build an atomic bomb “at the maximum speed possible.” The resulting Manhattan Project, directed by Groves, took about three years.

Later, President John F. Kennedy’s Apollo program put humans on the moon within a decade, spinning off thousands of new innovations, such as integrated circuits and solar panels. It required building a pair of major facilities—the Johnson Space Center and the Kennedy Space Center—each within a few years. In the 1950s, the Air Force fielded six new fighters from five different manufacturers in only five years, and during the early years of the Cold War, generations of ICBMs came and went within a decade. The revolutionary Minuteman missile was conceived in the late ’50s and deployed in the early ’60s.

[Read: Your smart toasted can’t hold a candle to the Apollo computer]

In the diplomatic realm, the Marshall Plan was announced in 1948, and within three years, it had provided nearly $15 billion to rebuild Western Europe. Inspired by his World War II experiences—having seen the “superlative system of the German autobahn”—President Dwight D. Eisenhower signed legislation funding the U.S. interstate highway system in June 1956, and within 10 years, it was substantially completed.   

These successes shared a common absence. Sprawling bureaucracies and stifling regulations had not yet materialized. The Defense Department could act quickly to buy the tens of thousands of microchips from Texas Instruments that it needed for the Minuteman missile. In the fall of 1962, the Air Force started looking for a new computer to guide the Minuteman II, and by the end of 1964 it had found one and test-fired the first missile.

Consistency in funding was crucial too. For instance, the overall cost of the Apollo space program actually declined as work shifted from research and development into production and operations. The program had the money it needed when it needed it, and the funding was sustained for years.

Moreover, in many of these cases, individuals were given the power to make decisions and to build strong teams quickly. The Marshall Plan’s administrator, Paul Hoffman, could hire qualified experts within weeks, recruiting the best from government, academia, and industry to staff each mission area. The Marshall Plan was a “business plan to be carried out by businessmen,” as one media executive put it. The Apollo program assembled young engineers and scientists with similar speed. Today, most of these hiring practices would be illegal.

We can no longer live off the accomplishments of past generations. To compete in today’s world, and to protect American interests, we must take time into account.

Calls for bureaucratic reform are nothing new. The Defense Department alone, for instance, has seen well over 150 efforts to upgrade acquisition systems, speed up processes, and buy what it needs when it’s needed. Empowering leaders to take greater risks can speed up processes. And experts have long warned of the dangers of compliance cultures that cripple organizations, as individuals spend more time checking boxes than actually getting results.

Such reform efforts will continue. Some may even succeed. But there is an additional approach that we might pursue: incorporating time as a foundational element of strategy.

In practical policy terms, this would mean including time as an input into policy promises and initiatives, just as we do other factors, such as funding and personnel. Given the abundance and granularity of data today, and the availability of new analytical tools such as large language models, this input is possible in a way that it was not in the past.  

[Alec Ross: The Pentagon’s army of nerds]

What we might call time-sensitive strategies have several benefits. They force policy makers to account for time, which in turn helps them define objectives realistically. These strategies could impose a sense of discipline too—like the Gantt charts of the early 1900s, which helped managers track the time required for each stage of a complicated process. Policy makers would need to account not just for what needs to be done, but also for when it needs to be done.

Time-sensitive strategies would also force greater transparency. Americans deserve to know whether a promise can be fulfilled tomorrow, or whether it will likely take many years. This could in turn reduce cynicism and might appeal to the best of Americans’ desire to improve and innovate and fix things, allowing them to ask why certain tasks take so long and advocate for improvements.

And finally, such strategies might reveal flawed assumptions, and drive decision makers toward more creative approaches. If leaders are forced to ask why things are taking so long, they in turn will identify persistent regulatory, legislative, and funding obstacles, and explain how they will be removed or overcome. Often, we need new ideas less than new ways of accomplishing long-standing goals.

If we remain naive or willfully ignorant about the role that time plays in success, the promises of our political leaders will remain merely performative.

But we need more than performative promises. The major technological and geopolitical challenges we face demand that the United States act faster. Advances in quantum technologies will render obsolete entire communication networks that depend on encryption. Precision-guided weapons and autonomous systems like drones have made American bases around the world vulnerable. The ability to shift supply chains to improve U.S. resilience and reduce our vulnerabilities depends on the ability of the United States to build mining and manufacturing facilities more quickly.

To meet its needs at home, and protect our interests around the world, Washington needs to deliver on time.

Base personnel had been ordered to evacuate the base’s 374th Airlift Wing headquarters, a residential tower and other buildings at around 10:20 a.m.

In early 2020, when the coronavirus was still a distant concern, my wife and I booked an AirAsia flight to Bali. Big mistake. At the start of lockdown, we scrambled to secure a refund. We called the airline’s customer-support line: no dice. We pleaded with its online chatbot, a lobotomized character named AVA. We sent a Twitter message to the brand on March 17 and received a response seven weeks later that read, in full, “Twitter Feedback.”

Those were dark days in airline customer service, with so many travelers desperate to figure out alternative plans. The present is not much brighter. In recent months, airlines around the world have changed how they engage with customers who need help. Frontier will no longer take your call, encouraging fliers to make contact via chatbot. Alaska Airlines is removing check-in kiosks at certain airports, driving people to its app. Air France, KLM, and Ryanair have all suspended customer service on Twitter, which for a time may have been the quickest way to summon a living, breathing employee.   

As Twitter melts down and people flee Facebook, social media just isn’t as useful as it once was for airline customer service. At the same time, airlines are leaning into AI, betting that the latest wave of chatbots will be the most cost-effective way to support customers. The long-standing truth is that companies don’t want to talk to you. First they didn’t want to do it in person, then they didn’t want to do it by phone, now they don’t want to do it online, and soon they won’t want to do it at all. It’s not personal—it just costs money. But hype-fueled AI products have yet to pick up the slack. “A chatbot being able to talk and to learn and to suggest and to persuade and do all of these things that humans do? I haven’t seen it in action, personally,” Eva Ascarza, a co-founder of the Customer Intelligence Lab at Harvard Business School, told me. Airline customer service is caught between two eras of the internet: one built on social media, the other on machine learning. The transition promises to be rocky. If you’re traveling this summer, you better hope that you don’t need help from an airline.

Airlines belong to a category of consumer-facing businesses that marketers call “high-touch”; they deal with customers whose needs are constantly evolving. Flights are delayed, bags get lost, people have to change their plans. And customers feel they deserve a certain level of care: After all, despite its gradual democratization, air travel remains quite expensive, especially in this period of high inflation. Multiply passenger expectations by the total number of seats—Delta flies something like the population of Sacramento every day, on average—and you start to appreciate the sector’s complexities.

These daunting customer-service demands have pushed airlines to automate since the dawn of mainframes. In 1960, IBM and American Airlines launched the first computerized reservation tool, based on a program developed for the Air Force. Customers would call a travel agent, who would then call an airline ticketing agent, who would then input the trip particulars. By 1964, the system could process some 7,000 bookings an hour, at a time when ticketing agents working manually could process one or two. The problem is, we’re still using it. “The basic systems which said ‘Box A talks to Box B via telex’ have largely remained unchanged since the 1950s,” Timothy O’Neil-Dunne, an airline-industry consultant, told me. (He paused to make sure I knew what a telex was. It’s a fax for text messages.) “So we are dealing with very, very old tech,” he added.

That old tech speaks in short codes: confirmation numbers, airport initials, seat numbers, passenger types. Customers rarely know all of the data that apply to their itinerary, which meant that until the advent of more advanced AI in recent years, changing a flight or locating a bag required a human intermediary, someone fluent in airline and English who could translate a question and input it as DL754, ATL, 19B, and Y. But call centers are expensive—even in Manila. Mindsay, a company that develops conversational AI for the industry, estimates that each support call costs airlines $2.20; in 2017, Harvard Business Review pegged the average cost of a live customer-service interaction at three times that amount.

Over the past decade, Facebook and Twitter emerged as efficient alternatives, allowing airlines to automate their response to certain posts and messages while paying special attention to the most urgent issues (or in some cases, the highest-profile users). In some ways, airlines demonstrated the viability of extending customer service over social media—if they could do it, any brand could. A study last year by the customer-experience company Emplifi found that among 23 industries, airlines had the second-fastest average customer-response time. In many cases, tweeting at an airline can really result in shorter wait times than sternly repeating “representative” on the phone or running a gantlet of scripted if-then scenarios with an online textbox.

Until recently, that kind of automated sorting was the best that chatbots—which many airlines offered early versions of—could muster. The predominant use case for AI in customer service was “the prioritization of calls, the prioritization of requests,” Ascarza said: software that decided how long you could wait for human assistance before the big vein in your head popped. On the other side of Twitter sat a flesh-and-blood airline agent whose voice was never heard but keenly felt. You could tweet something salty, tag the airline, and soon get an invitation to DM from an agent, who invariably signs their name.

But customer service through social media has become strained. “Can you calm down and allow me some time to work please ??” Delta tweeted to an inquiring customer last year. During the pandemic, airlines struggled to handle the unprecedented volume of passengers upset by endless rescheduling, and they doubled down on their automation efforts. In 2020, Delta temporarily suspended its customer service on Twitter and Facebook amid agent shortages and increased wait times. KLM, which was fielding 50,000 Facebook messages a day that March, enhanced its chatbot with machine learning; the discount carriers WestJet and AirAsia leaned into their existing ones.

Not all bots were created equal: AirAsia CEO Tony Fernandes recently called AVA, my erstwhile nemesis, “the most hated AI chatbot” in Southeast Asia. Nevertheless, it worked in the aggregate—at least from the brand’s point of view. “During COVID, AVA helped to clear millions of cases that would not have been possible given the amount of requests,” Fernandes said in an emailed statement. That was all before the implosion of Twitter under Elon Musk: Twitter has begun charging companies $1,000 a month to integrate their customer service into the app, prompting Air France and other companies to reconsider the site altogether. Meanwhile, the rise of platforms like TikTok, which aren’t as conducive to customer engagement, have undermined social-media support even further.

Those trends, along with recent strides in generative AI, have emboldened airline executives. Air India has committed $200 million to update its digital systems, which will include ChatGPT-driven features. Frontier claims that its self-service model requires less labor and delivers better customer-service experiences (although in a recent investor report, the airline baldly states its interest in limiting “avenue[s] for customer negotiation”). In February, AirAsia replaced AVA with a new bot called Ask Bo, which it promises will be “more proactive and attentive” thanks to “enhanced” AI. Technical details are scant, although a spokesperson for the airline claims that since Bo’s launch, 95 percent of all customer queries have flowed through it, and 73 percent of those queries were resolved with no follow-up. Considering how eagerly airlines have leaned into automation, expect other carriers to soon follow suit: “Airlines look on the face of it to be an ideal place to start to deploy an AI-based chatbot” powered by the latest large language models, O’Neil-Dunne said.

These days, even when you appeal to airlines over social media, you’re likely triggering some kind of machine-learning program. Both Twitter and Meta have invested in automation features for brands fielding customer messages. “I just get a link, and then I’m starting this WhatsApp conversation,” Ascarza said. “Sometimes I know it’s a bot, sometimes not.” She pointed out that although research suggests consumers prefer human interaction, it’s often because they lack compelling alternatives. That’s starting to change—and the ancillary features of AI support may suit us just fine. On a text-based interface, “it’s okay to be short,” Ascarza said. “And it’s okay not to be polite. And it’s okay to just get to the point.”

But the freedom to jettison social niceties is a shallow benefit. The simple reality is that customer service has eroded on social media, while the AI programs meant to replace it don’t yet meet the burdens of air travel. The last wave of chatbots, such as SWISS’s Nelly and WestJet’s Juliet, could clear the most straightforward cases with brute force, but they could also be blundering and ineffective. Airlines have iterated on those models and introduced new and improved versions, like Ask Bo, looking to capitalize on the fresh interest in AI as so many other companies are. Still, sophisticated bots on the level of ChatGPT don’t widely exist in air travel, and the way that airlines will actually deploy them—however many months or years from now—is an open question. For now, as the social web recedes from view and AI stumbles into an uncertain growth spurt, consumers everywhere are falling through the cracks.

In the long run, AI might improve customer experiences more than it degrades them. As airlines build smarter and smoother chatbots, they may free up their dwindling labor force to deal with the smaller percentage of more complicated requests. If chatbots are already capable of so much, why couldn’t they help us deal with a canceled flight or a lost bag? But AI could reshape customer service in more insidious ways. O’Neil-Dunne noted that as their customer-support tools become more nuanced, airline offerings are going the other way—giving rise to unbundled amenities and pared-down services, like some basic-economy tickets that don’t let you pick your seat without a surcharge. “If the product is simpler, the servicing is easier,” he said.

On the back end, AI could assess the value of every request, including if and when customers should receive help at all. “The decision of who not to serve is as important as who to serve,” Ascarza said. One logical outcome for an airline with millions of customers might be to simply deny or ignore a percentage of all complaints, which already happens with maddening frequency. The only thing worse than a feckless chatbot is a chatbot telling you, with perfect cogence and clarity, to get lost.

A virtual experiment was described by a senior official at a conference, but he now says he "mis-spoke".