• Academic Debate: Recent social science research presents conflicting findings regarding the impact of social media usage on the cognitive development of children.
• Primary Study: An analysis using Adolescent Brain Cognitive Development data suggests that increased social media consumption during tween years correlates with lower verbal and spatial memory scores.
• Methodological Critique: Independent data analysis by Jordan Lasker disputes these findings, asserting that alternative statistical models show little evidence of negative cognitive effects.
• Research Methodology: The original research utilized longitudinal data to track cognitive performance fluctuations alongside changing social media habits across three usage-intensity groups.
• Comparative Analysis: Critics argue for more rigorous evaluation methods, such as sibling comparisons and within-individual longitudinal assessments, to control for familial and home environment variables.
• Scientific Uncertainty: Re-evaluating the data with more granular models renders most previous findings statistically insignificant, highlighting the difficulty in establishing definitive causal links.
• Variable Interpretations: Even within recalculated models, minor negative associations persist, though researchers suggest these may stem from broader family-level trends rather than direct screen-time impact.
• Ongoing Investigation: The lack of consensus among researchers underscores the complexity of utilizing large datasets to measure the long-term cognitive consequences of digital device exposure.

[

](https://images.unsplash.com/photo-1546052725-72b0b8b9f4b6?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wzMDAzMzh8MHwxfHNlYXJjaHw1Mnx8c29jaWFsJTIwbWVkaWElMjB0ZWVuc3xlbnwwfHx8fDE3NzQ2MzAyODB8MA&ixlib=rb-4.1.0&q=80&w=1080)

Courtesy Joel Mott/Unsplash

Kids’ cell-phone use is one of the hotter social-science topics these days, and one we’ve touched on here before. A recent kerfuffle shows why the debate is sure to keep raging for a long time.

The story begins with a study published in The Lancet Regional Health, which got some extra attention thanks to a tweet by Jonathan Haidt—who’s led the intellectual charge against kids’ overuse of tech devices.

In the study’s analysis, the kids who most heavily increase their use of social media during their tween years tend to have weaker cognitive skills, including verbal and spatial memory, than similar kids who stay off social networks. However, the study faced an almost immediate response from the prominent data blogger Jordan Lasker, better known as Crémieux, who ran different models on the same dataset and argued there’s little sign of an effect. His blog post is here and a longer paper is posted here.

The episode is an interesting lesson in how science can move quickly online—if not so much in formal academic journals—and how different ways of analyzing a given dataset can produce wildly different conclusions.

The original paper drew its data from the Adolescent Brain Cognitive Development Study, which began following its thousands of subjects in the mid-to-late 2010s, when the kids were 8 to 11 years old. Thanks to further data collection over the following two years, the authors can sort the kids not just according to their overall social media use, but according to how this use changed over time.

More than half of the sample used very little social media at any point. About 40 percent of the sample is placed in another group: those who started as light users but increased their use over time; the 12-year-olds in this group used social media for close to an hour a day. And the third group, constituting about 6 percent of the sample, comprised the heaviest users. Even nine-year-olds in this group used social media close to an hour a day, and the almost-teenagers used it more than three hours a day.

The authors check to see how these groups fared on cognitive tests at the study’s two-year follow-up. They include a variety of statistical controls, including the kids’ baseline cognitive performance—which should help to address issues of self-selection, where smarter or duller kids may be more likely to take up social media—as well as basic demographics and non-social media screen time.

These models answer the question: if two kids started out with the same cognitive scores, and also share numerous other traits available in the data, and yet they had two different trajectories of social media use, does the heavier social-media user tend to fare worse on the later cognitive test?

The study’s answer was yes. Across four different cognitive tests, there was always a measurable gap between the lightest and heaviest users. And for three of the four, there was also a gap between the lightest users and the medium group, the kids who’d started out light but increased over time. These are not enormous effects—generally falling between a tenth and a quarter of a standard deviation—but they are certainly worrisome given the ubiquity of screens for kids (including those at older ages than the study focused on).

Enter Crémieux. The blogger pointed out that the data in question facilitate a much more rigorous analysis than the authors had conducted.

Instead of comparing totally different kids with each other and relying on statistical adjustments to make that comparison more apples-to-apples, one could look at siblings—who share a family background and home environment—to see if the heavier-using siblings fared worse. One could also study outcomes from both survey waves, instead of focusing on performance at the two-year follow-up. One could even look within individuals, to see if kids’ own cognitive scores fluctuated along with their social-media use.

The results are underwhelming with these approaches, and yet still debatable. Most of Crémieux’s results are statistically insignificant, but two of the within-individual models still suggest negative effects.1 Lasker’s paper suggests that, since these effects weren’t evident in the family-based analysis, they might reflect “family-level time trends” instead of a real effect.

The debate over the impact of kids’ tech use—on cognitive skills and everything else—is far from over. And it’s too bad that even the fanciest statistical tools can’t unambiguously tell us what’s happening.

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From the Manhattan Institute

• Jennifer Weber takes a hard look at math achievement in New York State schools.

Other Work of Note

• When Texas shipped illegal immigrants to blue areas with sanctuary policies, the receiving counties shifted toward Trump.

• Why don’t voters respond more strongly when candidates moderate their positions?

• When various policy changes—such as the 1990s welfare reform—pushed low-income moms to work, they became less likely to vote but more conservative.

• Conservatives and liberals differ in whom they see as victims. And Republicans and Democrats differ in how they dole out pork spending.

• This analysis of austerity measures imposed by the International Monetary Fund has produced some strenuous arguments on X.

• Elite colleges have generally found standardized tests quite helpful in predicting kids’ performance. A new study of a large public university system, though, finds that tests don’t add much beyond high-school GPA.

• Lots of new research on federal student loan policy.

• When Dallas schools started paying teachers based on effectiveness, student math achievement improved.

• Has teacher turnover finally stabilized?

• Another review of the returns to additional education. More bullish on school than the one I wrote up here a while back.

• A calculator from the Joint Economic Committee on the fiscal effects of immigration policy. But don’t forget about MI’s own!

• Options for Social Security reform from the folks who manage the Penn Wharton Budget Model.

• Meanwhile, there’s a new Financial Report of the U.S. Government.

• Is it possible to raise a nation’s overall level of happiness?

• How international college students affect their domestic peers academically.

• What do Americans think about assisted suicide?

• New statistics on abortion.

• Some interesting research based on the “Words Can Harm Scale,” which asks respondents whether they agree with a variety of melodramatic statements about how speech can traumatize.

• Tyler Cowen has his new book online for free with an AI assistant: “Beginning with the 1871 Marginal Revolution and ending with the AI tools transforming research today, this is a book about how ideas are born, why they take so long to arrive, and what happens when machines begin to see around corners that humans cannot.”

• There’s long been debate about how much improvements in trauma care have affected the homicide rate. This paper finds a 4 percent reduction in overall firearm mortality from the opening of a Level 1 trauma center on the South Side of Chicago.

• The (predictable) impact of California’s $20 fast-food minimum wage on prices.

• The Supreme Court has become less deferential to executive agencies’ interpretations of the laws that are supposed to constrain them, and this seems to be affecting how agency rulemakers do their jobs.

• Did AI slow the growth of coding jobs? And what are CEOs saying about how AI affects their businesses? Plus an interesting paper on “new work,” or novel types of jobs that emerge with technological changes.

• Some new results on the impact of food-stamp work requirements on single parents. These are generally seen as relatively weak, requiring a job search, registering with a job-search system, and in some cases participating in an Employment and Training program. The study finds they reduce enrollment but don’t induce parents to work more.

• Building public hospitals was good for babies in the 1950s and 1960s.

• Communist revolution was bad for Cuba.

• “Higher caffeinated coffee intake was significantly associated with lower risk of dementia.”

1

It’s tempting to think that even these results are far smaller than the original study’s, but note that the new models calculate the effect of one hour of social-media use rather than the difference between the high and low trajectories.

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The United States is engaged in a high-stakes, winner-take-all technological cold war with China. But the U.S. cannot lead in artificial intelligence if its largest economic zones function as anchors, not propellers.

Unfortunately, New York State is pushing for more innovation-killing regulation, spinning up red tape and mandating bureaucracy. Just three months into 2026, legislators in Albany have introduced more than 180 AI-related bills, far exceeding the quantity of legislation in any other state and even doubling that of California. There’s a bad idea for everything: national AI lab development rules, disparate-impact paperwork assessments and audits, algorithmic-pricing regulations, “robot taxes,” and AI rules in journalism and hiring.

New York’s everything-and-the-kitchen-sink approach to AI regulation will accelerate a continued business exodus. Consumers will suffer under the state’s micro-managerial, paperwork-first policies. And, because many regulations will have negative spillovers, the damage will extend beyond New York.

The harms aren’t hypothetical. Last year, Governor Kathy Hochul signed the Responsible Artificial Intelligence Safety and Education (RAISE) Act, which, after chapter amendments, regulates potential “catastrophic” risks associated with major AI systems. Governor Hochul boasted that the law sets the “national standard” for AI governance.

But lawmakers in Albany should not be dictating policy for the entire nation, especially on decisions of such magnitude. These issues are best addressed by national security experts with the necessary clearances and information to weigh risks and consequences.

To win the AI race, the United States needs to build more data centers. But New York is floating a three-year moratorium on data-center construction. By the time it elapses, the U.S. will have lost the AI race and forfeited its leadership position to Beijing.

Even when some New York lawmakers seek to boost AI capabilities, others erect new roadblocks. For example, the state promoted a $100 billion Micron Technology chip-making complex in Clay, New York. But opponents quickly filed lawsuits to halt construction until environmental and labor demands were satisfied, including two lawsuits filed on the day the project broke ground. Construction has already suffered serious delays, and the project could be derailed altogether.

It’s not just state lawmakers obstructing AI progress. In 2023, New York City enacted a first-in-the-nation law requiring race- and gender-bias audits of algorithmic hiring and firing tools. After a Cornell study found that only 18 of 391 city employers posted the audits as required, the Society for Human Resource Management (SHRM) declared the law a bust. But the measure had already inspired the sweeping Colorado AI Act, a law that that state’s government has regretted ever since.

Then, in 2024, Governor Hochul signed the Legislative Oversight of Automated Decision-making in Government (LOADinG) Act. The bill was sold to the public as a way of ensuring ethical, transparent, and accountable government AI use. In reality, it baked in layers of union protectionism, paperwork, and impact assessments.

The law stipulates, for example, that automated decision-making systems cannot result in a “transfer of future duties and functions ordinarily performed by employees of the state or any agency or public authority.” Such language is not written to help public employees make effective use of AI. It’s designed to protect their jobs at taxpayer expense.

New York State’s approach to innovation is at odds with the views of the state’s congressional representatives, who recognize the dangers of falling behind China on AI. Last year, Senator Chuck Schumer described the announcement of China’s powerful DeepSeek AI model as “AI’s Sputnik moment for America.” It was a “wakeup call that Congress desperately needs” to get serious about the AI race.

“If America falls behind China on AI, we will fall behind everywhere: economically, militarily, scientifically, educationally, everywhere,” Schumer said.

Schumer is right, and his state needs to listen. America cannot afford to let New York sabotage both itself and the nation’s innovators. This is how you lose a technology race.

Photo: Will Waldron/Albany Times Union via Getty Images

• Relationship Satisfaction Goal: Research indicates that couples who engage in sexual activity at least once per week generally report the highest levels of well-being.
• Optimizing Frequency: Increasing sex beyond a weekly cadence does not consistently yield higher levels of satisfaction and may lead to negative outcomes if the frequency feels forced.
• Responsive Desire: Sexual desire often emerges after physical or emotional engagement has already begun rather than occurring spontaneously in both partners simultaneously.
• Strategic Scheduling: Planning intimacy is a valid organizational tool that prioritizes relationship health and helps create necessary conditions for connection.
• Maintaining Physical Touch: Everyday non-sexual affection, such as holding hands or hugging, is essential to sustain bonding and prevent the cooling of emotional intimacy.
• Domestic Equity: Managing households fairly is a significant factor in relationship dynamics, as shared labor reduces resentment and improves the overall environment for intimacy.
• Micro Novelty: Introducing small variations in routines or settings is a highly effective, low-pressure method to maintain interest and engagement over time.
• Mindful Intention: Successful long-term intimacy is built through consistent, purposeful effort rather than an expectation of spontaneous perfection or perpetual excitement.

Source: Kenny Eliason/Unsplash

Most people don’t notice when their sex life starts to change.

Over time, what was once easy begins to feel effortful. And suddenly you’re left wondering, is this it? Is this just married sex? And then that quiet fear creeps in…” Are we having it ‘enough?’”

What I’ve learned over more than a decade of teaching and researching sexuality is that this experience is not unusual. And that actually, you’re probably more normal than you think. However, a fulfilling sex life in any long-term relationship does take curiosity, intention, and yes, a certain amount of planning. Which brings us to the question: “What should we aim for?”

The surprisingly modest “sweet spot”

Many people assume that a good sex life means having sex extremely frequently. In reality, the data tells a much more reassuring story.

Large-scale studies consistently find that couples who have sex about once a week or more report the highest levels of relationship satisfaction and overall well-being. Interestingly, these same studies show that more sex isn’t always better. In fact, the relationship between sexual frequency and well-being flattens after once a week, suggesting that despite what we may think, the most sexually satisfied couples aren’t having sex daily, or even more than once a week. Though there’s certainly nothing wrong with having sex that often, the data shows that when couples force themselves to have it more than they truly want, that pressure often backfires.

Why waiting for desire doesn’t work

One of the most persistent myths about sex is that you should only have it when you feel fully in the mood. But like many things in life, sometimes that feeling of “readiness” happens only after you’ve started.

This belief is especially problematic in long-term relationships. Research on sexual desire shows that for many people, particularly women, desire is often responsive rather than spontaneous (Nagoski, 2018). It emerges after physical or emotional engagement has already begun.

If you wait for both partners to feel spontaneously aroused at the same time, you may be waiting indefinitely.

This is why many clinicians and researchers now encourage couples to be more intentional about sex. Not in a rigid or transactional way, but in a way that acknowledges that desire often follows action, not the other way around.

Why scheduling sex actually works

Scheduling sex has an unfortunate reputation for being unromantic. But when you step back, it starts to look less like a failure and more like a form of prioritization.

We schedule workouts, social plans, and even downtime. Yet we often expect sex, something deeply tied to both physical and relational well-being, to somehow happen on its own.

Couples who maintain satisfying sex lives over time tend to have one thing in common: they create the conditions for connection. That might mean setting aside time to be together at least once a week without distractions, slowing down at the end of the day, or intentionally shifting from “task mode” to “relationship mode.” In this way, you aren’t just planning sex, you are planning to create the conditions which make it more likely to happen, rather than just expecting either partner to instantly turn on.

Importantly, scheduling does not mean obligation. It means creating an opportunity. If one or both partners genuinely aren’t interested in that moment, the goal is flexibility, not pressure.

The quiet erosion of intimacy

In many relationships, the first thing to disappear is not sex itself, but touch. And if the only time couples touch each other is once a week when they plan for intimacy, it can become a pressure-filled recipe for failure.

THE BASICS

• The Fundamentals of Sex
• Take our Sexual Satisfaction Test
• Find a sex therapist near me

Small gestures, holding hands, a hug, a kiss when you walk in the door, often fade as couples become more focused on logistics. Over time, relationships can begin to feel more like partnerships in managing life than sources of emotional or physical connection.

Research consistently shows that affectionate touch plays a critical role in maintaining closeness. It reinforces bonding, increases feelings of security, and can help sustain sexual desire over time.

What’s happening outside the bedroom matters

Sexual difficulties are often rooted in dynamics that have little to do with sex itself.

One of the most consistent findings in relationship research is the role of fairness in household labor. When one partner carries a disproportionate share of domestic responsibilities, it can lead to exhaustion and resentment. Both are deeply incompatible with desire.

Sex Essential Reads

Want to Increase Your (or Their) Libido?

Is Making Love Different from Just Having Sex?

Studies have found that couples who share household tasks more equitably tend to have more frequent sex and higher levels of satisfaction. Not because chores are inherently sexy, but because fairness fosters goodwill, and goodwill creates the conditions for intimacy.

In other words, planning intimacy once a week is a wonderful goal, but it’s unlikely to happen when resentments are brimming. Never underestimate the power of gratitude and recognition of all that your partner does.

Novelty doesn’t have to be dramatic

Planning intimacy once a week is more likely to happen when there’s something about the experience to look forward to. And the key to making that true is novelty.

But this doesn’t need to mean major changes. Research shows that in reality, small shifts can be surprisingly powerful. Enter what I call “micro-novelty.”

A different time of day. A new setting. Slight variations in familiar routines. These “micro-novelties” can help counteract the predictability that sometimes dampens desire.

No one craves tacos every night for dinner, even if they happen to love tacos. This is why small shifts in how you set the mood, slight changes of technique, or sharing fantasies and turn-ons can make you more present and excited when your intimacy date arrives.

The bigger picture

If there is one takeaway from decades of research on sex and relationships, it is this: Good sex is not about perfection, super high frequency, or constant excitement.

Yes, regular sex is important to relationship well-being, but more isn’t always better. And ultimately, connection, intention, and responsiveness matter more than one special number.

• Service Restrictions: Anthropic implemented unannounced throttling during peak weekday hours, significantly limiting access for paying Claude subscribers.
• Economic Imbalance: The subscription model remains inherently unstable, as inference costs for active users often exceed the flat monthly fees collected.
• Rationing Logic: Capacity limitations effectively function as ticket rationing, restricting service availability during critical West Coast business hours.
• Historical Parallels: The current strategy mirrors past challenges faced by companies like AOL, which struggled when flat-rate pricing models met high consumption demand.
• Pricing Inefficiency: Digital market research suggests that failing to utilize versioned pricing for goods with nonzero marginal costs leads to inverted economics.
• Operational Negligence: Management proceeded with aggressive customer acquisition strategies without scaling capacity or infrastructure to support the resulting surge in usage.
• Lack Of Transparency: The company maintains a history of imposing opaque limitations, creating a disconnect between the advertised service and actual user experience.
• Competitive Consequences: By failing to manage service availability, Anthropic risks alienating its user base and providing an easy marketing opportunity for industry rivals.

On March 23, Anthropic's paying Claude subscribers discovered their sessions had been throttled without notice. Max users handing over $200 a month watched their daily allowance vanish on a single prompt. Developers on the West Coast, opening their laptops at 8 a.m., found themselves locked out before writing a line of code.

Call it "adjusting." Better: call it rationing.

The euphemism arrived on schedule.

Thariq Shihipar, a member of Anthropic's technical team, confirmed on Wednesday that the company now limits session capacity during "peak hours," weekdays from 5 a.m. to 11 a.m. Pacific. Weekly caps stay the same, he assured. You'll just burn through them faster during the only hours that matter.

That is the logic of an airline that overbooks every flight, then explains your ticket is still valid, you simply can't board during business hours. Only 7% of users are affected, Anthropic added. One imagines the airline making the same announcement over the PA system.

The math was never hidden.

The numbers that explain this crisis were always public. Anthropic's own API pricing reveals the gap. A moderate subscriber paying $20 a month generates roughly $58.50 in inference costs. Nearly three dollars consumed for every dollar collected. Power users on the top-tier plan burn multiples more. The company acknowledged last August that some subscribers were consuming "tens of thousands of dollars in model usage" against flat-rate plans.

None of this was secret. The company sold subscriptions it knew were underwater, then flinched when customers took the product seriously.

AOL tried this in 1996.

America Online killed hourly billing that December and promised unlimited access for a flat fee. Users took AOL at its word, lines jammed for weeks, and state attorneys general forced a settlement. The lesson was plain: "unlimited" works as a customer acquisition strategy precisely until customers believe it.

Thirty years later, Anthropic is replaying the same tape at GPU scale. The technology changed. The arithmetic did not.

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Information has rules. Anthropic ignored them.

Carl Shapiro and Hal Varian warned about exactly this failure mode in Information Rules, their 1998 study of pricing in digital markets. Their central argument was deceptively plain: information goods with variable consumption demand versioned pricing, not flat rates.

Flat-rate pricing for goods with nonzero marginal cost is a bet against your own success. The more customers you acquire, the faster the economics invert. Anthropic's revenue has grown by multiples since 2025. That growth, on money-losing unit economics, does not solve the problem. It compounds it.

The deeper dishonesty is structural. A casual user who asks Claude two questions over morning coffee costs pennies to serve. A developer running agentic coding loops all morning, cursor ticking through request after request, burns a hundred times the compute. Anthropic knew this distribution existed. It published the API rates that prove it. Yet it kept selling a single price tier as if usage were uniform.

When the transition from subsidy to metering arrived, Anthropic handled it the worst way possible: without warning. Its own support chatbot went down during the chaos. Google had pulled the same move weeks earlier, restricting AI Ultra subscribers without explanation. The pattern is becoming an industry template: sell the subscription, ration the service, blame the peak.

The timing is the tell.

The throttled window opens at five in the morning, Pacific time, and doesn't close until eleven. That is the West Coast workday. A developer in San Francisco sits down at eight and walks straight into the limit. Users who reorganized their toolchains around Claude, who let competing subscriptions lapse because Anthropic's product was better, discover the product they depend on carries a conditional asterisk they were never shown.

And the opacity is not new. Anthropic has a documented pattern of imposing limits users cannot see coming, creating a persistent gap between what subscribers pay for and what they receive.

Hours after Anthropic's announcement, OpenAI's Codex engineering lead posted on X that the company had lifted all usage limits. The post pulled hundreds of thousands of views. If Anthropic wanted to design a customer acquisition campaign for its chief rival, it could not have built a more effective one.

Subsidy is not a business model.

The defense will sound reasonable. Demand surged after the QuitGPT movement sent users flooding from ChatGPT to Claude. The app hit number one on the U.S. store. GPU capacity doesn't materialize over a weekend. All true. All foreseeable.

But foreseeability is the point. The QuitGPT surge was a windfall Anthropic actively celebrated. It watched the download numbers climb. It updated its marketing. And it did not update its capacity planning or its subscriber communications to match.

A company that accepts a massive surge in sessions without preparing its infrastructure or warning its customers is not a victim of success. It is a beneficiary of attention that refused to pay the operational cost of receiving it.

Customers forgive price increases. They do not forgive bait-and-switch.

[

$250 a Month. No Warning. No Access.

San Francisco | Monday, February 23, 2026 Google restricted AI Ultra accounts, cutting off subscribers who accessed Gemini through OpenClaw's OAuth client. No warning, no explanation, no way to reach

The Implicator

](https://www.implicator.ai/250-a-month-no-warning-no-access/)

[

Google Restricts AI Ultra Subscribers Over OpenClaw OAuth, Days After Anthropic Ban

Google has restricted accounts of AI Ultra subscribers who accessed Gemini models through OpenClaw, a third-party OAuth client, according to a growing thread on the Google AI Developer Forum. The rest

The Implicator

](https://www.implicator.ai/google-restricts-ai-ultra-subscribers-over-openclaw-oauth-days-after-anthropic-ban/)

[

Brave Drops Free Search API Tier, Puts All Developers on Metered Billing

Brave removed its free Search API tier on Thursday, replacing the zero-cost plan available since May 2023 with a credit-based billing system that charges $5 per thousand requests, according to the com

The Implicator

](https://www.implicator.ai/brave-drops-free-search-api-tier-puts-all-developers-on-metered-billing/)

Opinion

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Marcus Schuler

San Francisco

Tech translator with German roots who fled to Silicon Valley chaos. Decodes startup noise from San Francisco. Launched implicator.ai to slice through AI's daily madness—crisp, clear, with Teutonic precision and sarcasm. E-Mail: marcus@implicator.ai

• Routine Eating Habits: Consuming the same meals and snacks consistently is linked to higher weight loss success over several months.
• Cognitive Efficiency: Establishing predictable food routines reduces the decision-making burden and mental effort required to maintain a healthy caloric intake.
• Study Methodology: Researchers analyzed self-reported food logs from 112 overweight or obese participants enrolled in a structured weight-loss program.
• Weight Loss Comparison: Participants adhering to a routine diet experienced an average weight loss of 5.9 percent compared to 4.3 percent for those with varied diets.
• Caloric Consistency: Every hundred-calorie fluctuation in daily intake was associated with a 0.6 percent decrease in total weight loss over 12 weeks.
• Environmental Adaptation: Constant dietary repetition serves as a strategy to counter the challenges posed by modern, high-variety food environments.
• Nutritional Considerations: The observed results do not account for nutritional quality, as the focus was specifically on caloric control and routine adherence.
• Future Research: The findings suggest a need for randomized clinical trials to confirm the causal relationship between repetitive eating patterns and long-term weight management.

Consistency is key to building healthy habits, and our daily meal choices may be no exception.

Researchers at Drexel University in the US have now found evidence that indulging in the same meals and snacks day after day can lead to more successful weight loss over the course of several months.

While diversity in the diet is undoubtedly important for human health, these new results suggest that eating the same meals on repeat can come with perks for those who want to lose weight.

As long as the go-to meals and snacks are well-rounded, they may help with weight loss more than a flexible, varied diet.

"Maintaining a healthy diet in today's food environment requires constant effort and self-control," says lead author and health psychologist Charlotte Hagerman from Drexel University.

"Creating routines around eating may reduce that burden and make healthy choices feel more automatic."

For the study, Hagerman and colleagues analyzed the self-reported food logs of 112 overweight or obese adults who were enrolled in a structured behavioral weight-loss program.

In the first 12 weeks of the program, participants who ate the same meals and snacks, as well as those with day-to-day calorie consistency, tended to lose more body weight than those who chose different foods, or whose calorie intake fluctuated more widely.

Specifically, those who stuck to a more routine weight-loss diet lost 5.9 percent of their body weight on average, whereas those with a more varied diet lost 4.3 percent.

That's a small overall difference, but one that could be significant, especially in the long run if this weight loss is maintained.

The study authors calculate that for every hundred-calorie difference in a participant's day-to-day diet, weight loss decreased by 0.6 percent over the study's 12-week period.

(Johner Images/Johner Images Royalty-Free/Getty Images)

The research is small and insufficient to overturn evidence suggesting that a diverse diet holds health benefits for most people. And, of course, it's important to talk to a doctor before making any major changes to your diet.

However, it is one of the first studies to use real-time food tracking data to explore how routine eating aids weight loss across multiple months.

The findings suggest that the constant variety of food we are surrounded by, day in and day out, may be hampering some weight-loss regimens.

"If we lived in a healthier food environment, we might encourage people to have as much variety in their diet as possible," explains Hagerman.

"However, our modern food environment is too problematic. Instead, people may do best with a more repetitive diet that helps them consistently make healthier choices, even if they might sacrifice some nutritional variety."

The current study did not consider the nutritional quality of the diets participants were eating. This means that they could have been losing weight by eating an unhealthy diet.

However, participants were enrolled in a behavioral weight loss treatment program, in which they worked with coaches to determine their daily calorie intake and weekly weight-loss goals.

There were two ways participants could approach their goals: They could either keep a consistent daily calorie intake, or they could prioritize a weekly average, 'saving' some calories for special occasions.

Those who logged their food choices on the most days, which is highly predictive of weight loss, still lost more weight if they had a more routine diet.

Related: A 30-Year Study May Have Found The Single Best Diet For Healthy Aging

Researchers can't say for sure whether that weight loss is really caused by a more routine diet, but the association has them wanting to know more.

"Even a healthy diet high in variety may increase points of decision-making, making it more cumbersome to calculate calories, versus having go-to meals with pre-calculated calories," hypothesize the study authors.

Sounds like a randomized clinical trial in the making.

The study is published in Health Psychology.

• Management Methodology: A book titled The Algorithm by former Tesla executive Jon McNeill details a five-step process allegedly used to guide operations at Tesla and SpaceX.
• Procedural Steps: The framework consists of questioning requirements, deleting unnecessary steps, simplifying and optimizing, accelerating cycle times, and automating processes.
• Foundational Philosophy: The operational system relies on first principles thinking, which involves breaking complex problems down to their most fundamental atomic components.
• Terafab Initiative: A proposed joint project between Tesla and SpaceX involves constructing a large-scale AI chip manufacturing facility in Texas to address supply shortages.
• Strategic Vertical Integration: The decision to build internal chip production addresses perceived risks regarding supply chain dependency and single points of failure for AI-dependent businesses.
• Space-Based Expansion: Strategic plans include shifting data center operations to outer space to leverage abundant solar power and reduce operational costs.
• Performance Discrepancies: Historical projections, such as the goal to reach 20 million vehicle deliveries annually, have previously fallen short of stated targets.
• Operational Urgency: The management style emphasizes maintaining consistent pressure on existential business issues to establish a competitive advantage over industry peers.

Elon Musk in a livestream last Saturday announcing the Terafab project.

By

Tim Higgins

March 27, 2026 8:00 pm ET

Anyone can tap in to the powerful management techniques behind Elon Musk’s success.

At least that’s the thesis of a just-released book by former Tesla TSLA -2.76%decrease; red down pointing triangle President Jon McNeill. “The Algorithm” argues there are five steps that explain how Musk wants his teams at the electric-car company and rocket-maker SpaceX to operate. 

“Much of the genius in Musk’s companies come from the legions of smart people empowered by the Algorithm,” McNeill writes. “They’re chasing stretch goals with free license to question everything and innovate boldly.”

That philosophy was on my mind as I watched Musk’s most recent event to announce plans for a joint project between Tesla and SpaceX to build the world’s largest AI chip factory.

The so-called Terafab, he said, would far exceed what all of the chip fabrication plants, or fabs, on the planet can currently make. Not the sort of thing a car company or a rocket maker would naturally get involved in doing, especially given the risks of entering a competitive and different industry.

Yet AI chips are at the heart of his vision for billions of robots being made a year globally and space missions to the moon and Mars. The goal is simple, he told an audience in Austin, Texas, recently: “Turn science fiction to science fact.”

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What’s the secret sauce of Elon Musk’s management style? Host Tim Higgins and former Tesla President Jon McNeill deconstruct the operating system that powered Tesla’s massive growth and the high-stakes lessons learned along the way.

So what exactly is the Algorithm? A series of deceptively simple steps: 1) Question every requirement. 2) Delete every possible step in a process (or part). 3) Simplify and optimize. 4) Accelerate cycle time. 5) Automate.

The approach was first detailed in Walter Isaacson’s 2023 biography “Elon Musk.” It was Isaacson who encouraged McNeill to write his own book that goes into depth about how the Algorithm works, the new author said.

McNeill, who left Tesla in 2018, was a key deputy during Tesla’s struggle to develop the game-changing Model 3 sedan and ramp production of the Model X SUV. 

During that time, the framework for solving problems became so routine, by McNeill’s telling, that one executive at Tesla suggested calling it the Algorithm so they could better communicate the approach throughout the company. 

It is rooted in the first principles thinking popular with Musk, McNeill told me for an episode of the “Bold Names” podcast.

“First principles thinking to me is the lowest common denominator of the problem in the elements of the problem—so like I think about breaking the problem down to…atomic level,” McNeill said.

Jon McNeill, former Tesla president. Felix Wong/South China Morning Post/Getty

Pulling it off correctly is beyond basic—even for Musk.

The Terafab, which some have estimated could cost $20 billion or more, has all of the hallmarks of the Algorithm.

Musk and others are investing heavily to build more computing power to fuel AI development. Key hurdles are AI chip supply and energy required to power data centers.

Part of SpaceX’s recent AI strategy is a shift toward building data centers in outer space, where solar power is abundant and, Musk says, will eventually be cheaper than operating on Earth.

But a chip supply shortage is crimping that dream. The world’s suppliers combined are making about 2% of what Musk said his companies need for Tesla’s robotcars and humanoid robots, and SpaceX’s AI data centers, to fuel his AI ambitions with xAI.

Musk said he has been trying to encourage suppliers to expand capacity quickly, but there’s a maximum rate they’re comfortable doing.

Most in business would probably say they’re stuck waiting. Not Musk. 

Musk stepping out of a Tesla Model X SUV at a 2015 launch event. Justin Sullivan/Getty

“That rate is much less than we would like, and so we either build the Terafab or we don’t have the chips, and we need the chips, so we’re gonna build Terafab,” Musk said.

That gets to the Algorithm, McNeill told me: If Musk wants to control his own destiny, there’s no requirement that he buy chips from someone else. 

“Elon has three businesses that all depend on chips, and he understands that dependence as a single point of failure,” McNeill told me in a follow-up email. 

Musk’s next moves are being met with skepticism, especially as he prepares to take SpaceX public this year. Why would these companies want to get into the complicated and expensive business of making chips? 

The case for the Terafab probably isn’t helped by grandiose ideas that Musk has touted in recent years that fizzled out—such as aiming to scale Tesla to build 20 million vehicles a year. (The company delivered 1.6 million vehicles last year.)

But supporters point to his success turning Tesla into an EV powerhouse and SpaceX into the dominant player in the burgeoning space economy as examples of what can happen when Musk succeeds.

An image of the Advanced Technology Fab project, from the livestream announcement last Saturday.

The Algorithm was honed during years of struggle. Supplier bottlenecks have been huge issues for Musk’s manufacturing companies. That’s especially true in dealing with new technologies where everyone isn’t as confident as Musk is about the size of a potential new market.

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Shortly after the success of Tesla’s Model S sedan, for example, Musk began making plans for building a giant battery factory. Similar to now, Musk envisioned requiring more batteries for EVs than the world was producing and he wanted to jump-start things.

Eventually, Tesla would convince battery supplier Panasonic to open a giant factory in Nevada, an important part of making the Model 3 successful.

A key ingredient to the Algorithm, McNeill told me, is the sense of urgency that it injects into everyday work. For Musk, that means latching on to one or two existential issues and riding them week after week.

“I used to sit in those meetings, saying I’m pretty dang sure that our competitors’ CEOs are not sitting in these weekly engineering reviews and not driving their companies as fast,” McNeill said. “Therefore we’re compounding an advantage against them.” 

Today it’s clear that Musk’s new urgency is around AI in space.

Elon Musk Inc.

More coverage of the billionaire entrepreneur and his businesses, selected by editors

Funding Tesla's AI Future

How Musk Wants to Wire the Human Brain

His Quest to Make Men Great Again

Using His Empire to Kickstart xAI

Musk Has Used Illegal Drugs, Worrying Leaders at Tesla and SpaceX

Musk Is Planning a Texas Utopia—His Own Town

SpaceX Wields Power Over Satellite Rivals to Boost Starlink

Behind Musk’s Management Philosophy

Copyright ©2026 Dow Jones & Company, Inc. All Rights Reserved. 87990cbe856818d5eddac44c7b1cdeb8

Appeared in the March 28, 2026, print edition as 'The Playbook That Makes Elon Musk’s Wild Ideas Work'.

Tim Higgins is a business columnist for The Wall Street Journal, frequent contributor to CNBC, and author of books about Apple (“iWar”) and Tesla (“Power Play”). He also co-hosts “Bold Names,” the Journal’s weekly interview podcast with top business leaders.

His weekly column focuses on influential companies and their leaders, such as Elon Musk, Tim Cook and Mark Zuckerberg. Tim became a columnist in 2023 after working for more than two decades as an award-winning reporter, covering everything from the bankruptcy of General Motors to the presidential campaigns of 2016.

A Missouri School of Journalism grad, Tim also earned an M.B.A. from Michigan State University. He lives in San Francisco.