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Does creatine make you smarter?

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Is creatine a weird steroid-like hormone or drug?

No. Creatine is a nutrient. Most omnivores eat a gram or two per day from meat. Your body also synthesizes a gram or two per day. You need creatine to deliver energy inside of cells. It is normal and non-weird.

Does creatine increase testosterone?

Unlikely. This concern comes from one study in 2009 on 16 male rugby players.1 But that study is considered extremely suspect. There have been at least twelve other studies that all found no change or physiologically irrelevant changes. Beyond that, it’s implausible that creatine would increase testosterone, because we know what creatine does and it has nothing to do with hormones.

Does creatine make you go bald?

No. Or, rather:

  1. No study ever reported that.
  2. One study reported the opposite.
  3. There is no mechanistic reason to think that would happen.
  4. There are good mechanistic reasons to think that would not happen.

These rumors all trace back to speculation built on top of that same single 2009 study. But that study is contradicted by later research, and anyway didn’t measure hair. Anything is possible, but as far as I can tell, it’s equally plausible that creatine would increase hair growth. And if you’re really worried about this: Are you going to stop eating meat?

Is creatine safe?

Probably. The International Society of Sports Nutrition says:

Available short and long-term studies in healthy and diseased populations, from infants to the elderly, at dosages ranging from 0.3 to 0.8 g/kg/day for up to 5 years have consistently shown that creatine supplementation poses no adverse health risks and may provide a number of health and performance benefits.

It’s been studied extensively, and no risks have been found. The way it works doesn’t suggest any risks. And supplementing a few grams per day doesn’t put you far outside the range that people get from normal food.

Does creatine make you stronger?

Yes. It’s very rare for a supplement to have such strong and consistent evidence. A widely-cited review says that short-term supplementation increases maximal power/strength by 5-15%. This in turn may increase the long-term gainz from strength-training exercise. Creatine also increases sprint performance by 1-5%. Though, there seems to be little if any benefit for endurance exercise like long-distance running.

But how does creatine make you stronger?

Before answering that, can I go on a rant about how muscles work?

…OK?

Great! Here’s how muscles work:

  • All cells have a molecule called ATP floating around inside, which they use for energy.
  • Muscle cells have proteins in them called myosin.
  • When ATP bumps into myosin, the myosin breaks the ATP down into ADP. This releases energy which is physically captured by the myosin as elastic strain.
  • When triggered by neurons, myosin releases that mechanical energy.
  • When you decide to move your arm, your brain triggers many muscle cells, carefully orchestrating the myosin twitches into large-scale movement.

Now, here’s something that’s crucial for our story: Very little energy is stored as ATP. Your body contains ~100 grams of ATP, representing ~10,000 joules of energy.2 But your body at rest burns ~100 watts. So you only store enough ATP to keep yourself alive for ~100 seconds. If you sprint, you could easily burn ~3000 watts, which would use all your stored ATP in ~3 seconds.

Through the magic of eating, you’re always making more ATP. Typically, your mitochondria recycle ~1 gram of ADP back into ATP per second, the same amount you need to stay alive.3 If you start running, your body can ramp that up to ~10 grams per second, though tricks like breathing faster and speeding up your heart.4 But it takes a minute or two for your mitochondria to really get cranking.5

So then why am I able to sprint for longer than three seconds?

Because creatine acts as an additional energy reservoir, coupled to the ATP reservoir. After you eat or synthesize creatine, 60% is converted into phosphocreatine. This is done by an enzyme that grabs a creatine molecule and an ATP molecule and moves a phosphate group between them. This “charges” the creatine into phosphocreatine and “discharges” the ATP into ADP.6

But if your ATP levels drop—e.g. because you’re running away from a tiger—those enzymes will run in reverse, meaning they “discharge” phosphocreatine into creatine and “charge” ADP back into ATP. This happens almost instantly, so that ATP and phosphocreatine deplete at the same rate.7

At rest, your muscles contain around 3-4 times as much phosphocreatine as ATP. So the “extra” energy storage in phosphocreatine is much larger than the “base” storage in ATP itself. That’s why you can sprint for ten seconds rather than just three seconds.

Does supplementing creatine increase creatine levels in muscle cells?

Yes. Typical levels are:

  • Vegetarian: 100 mmol / kg
  • Omnivore: 120 mmol / kg
  • Someone who supplements creatine: 140 mmol / kg

So, everything seems to add up. If you supplement creatine, you increase your levels by ~16.67%, implying ~12.5% more total short-term energy storage.8 That’s in line with the 5-15% increase in strength seen in creatine trials.9 It also seems to make sense that creatine trials find little benefit for endurance exercise. If you don’t have sudden bursts of activity, a larger short-term energy reservoir won’t really help you.

But isn’t this all very strange?

Well, I find it strange. All else equal, more strength is good. The body already knows how to make creatine. If you can just raise creatine levels and get more strength with no downsides, then shouldn’t evolution have done this already? Some variant of the Algernon argument would suggest that the fact that creatine works so well should be impossible.

You might think that higher creatine levels are bad somehow, and that’s why evolution didn’t make them higher. But that seems wrong. Creatine levels vary naturally based on what you eat. If higher levels were bad, evolution could have brought them down. But it doesn’t. It just lets them vary.

Often, evolution makes us “worse” to reduce our energy expenditures, because evolution hates it when we starve to death.10 But the body only spends 1-2 calories per day synthesizing creatine, and more creatine in muscle cells doesn’t have any significant metabolic cost.

I think the boring explanation is that for our evolutionary ancestors, modest increases in short-term strength just weren’t a big deal. We were exhaustion hunters, not 1-rep max deadlift hunters.11 Also, more creatine causes your muscle cells to draw in some extra water, which slightly increases energy usage for long-distance running.12 So, if you happened to get extra creatine from meat, great. If not, whatever. In the range where creatine fluctuates based on diet, I suspect creatine levels just didn’t have much impact on reproductive success.

Still, we must acknowledge that creatine is unusual. I wish we could tell our bodies, “Hey, we have access to unlimited amounts of food. Stop worrying about conserving energy and concentrate on being awesome.” But we have very few ways to do that. As far as I can tell, the list of normal nutrients that have been proven to increase strength is: protein, creatine, beta-alanine, the end.

So creatine is special. And creatine makes you a little stronger. Does it make you a little smarter, too?

Is creatine used by the brain?

Yes. Most parts of the body don’t contain significant creatine. But the brain does, along with muscles, the heart, and testes. Neurons use it to play the same game muscles do with ATP and phosphate groups and so on.

How much creatine is in the brain?

Maybe half as much as in muscle. The number of interest here is the ratio of phosphocreatine to ATP, indicating how much phosphocreatine increases local energy storage. We saw above that in muscle, that ratio is 3 to 4. In the brain, the numbers are a little sketchy, but the ratio seems to be more like 1.5 to 2.13

But why? Why would the brain use creatine?

Good question! The brain doesn’t have bursts of energy usage like muscles do. Yes, the brain uses ~20% of all calories despite only making up ~2% of body mass. But the brain is unusual in that it needs all that energy just for basic housekeeping, and doesn’t ramp up with usage. Contrary to the common myth, thinking hard does not burn significantly more calories. (Demonstration: Start thinking hard, and watch as your heart rate does not increase.)

So muscles use creatine for sprints. But the brain doesn’t have sprints. So what the hell is the brain using creatine for?

The most common theory seems to go like this: Actually, muscles don’t just use creatine as an extra energy reservoir. They also use it to deliver energy inside of cells. You see, creatine diffuses faster than ATP inside of cells. So even with endurance exercise, creatine is still being used: Enzymes near the mitochondria use ATP to “charge” creatine into phosphocreatine and enzymes near myosin use that phosphocreatine to “recharge” ADP back into ATP. Even though the net change in creatine is zero, it helps “shuttle” energy from the mitochondria to the myosin.

Under this theory, what neurons and muscle cells share is that parts of the cell locally use a lot of energy, when they get triggered. So even though your brain doesn’t “sprint”, it still uses creatine to avoid local energy deficits.

There’s also experimental evidence that creatine is important for the brain. We’ve created genetically altered mice with brains that lack the enzymes needed to convert creatine to and from phosphocreatine. They display severely limited spatial learning and somewhat smaller brains.

Some humans also naturally have creatine deficiency. In some variants, people have trouble synthesizing creatine. This leads to lower levels throughout the body, including skeletal muscle where 95% of creatine lives. Nevertheless, the primary symptom is related to the brain, namely intellectual disability. Muscle weakness and seizures are also common. Other people have creatine transporter deficiency, meaning creatine can’t cross the blood-brain barrier. This leads to lower levels in the brain only. This leads again to intellectual disability and also often muscle weakness or seizures. (That muscle weakness is despite the fact that the muscle cells themselves have normal creatine levels.)14

So somehow, creatine is very important for the brain.

Does supplementing creatine increase creatine levels in the brain?

Probably, though likely less than in muscle.

Creatine can definitely cross the blood-brain barrier. However, the protein that helps it cross is not abundant, and there are some suggestions that it’s down-regulated with prolonged creatine consumption. The brain itself can synthesize some creatine, and this too might be down-regulated by prolonged consumption.

Of course, you can just give people creatine and see what happens to their brains. There have been around a dozen such studies. Most report increases between 3% and 10%, although a few report no change. However, because brains are hard to access, these studies rely on magnetic resonance spectroscopy, and some suggest that these measurements are unreliable.

In people who can’t synthesize creatine, oral supplementation seems to normalize levels in the brain. (Some cognitive impairment usually remains. One patient was diagnosed and began supplementing at three weeks of age and had no intellectual disability.) So supplementing can increase brain levels in some circumstances.

My best guess is that supplementing does usually increase levels in the brain, and that an increase of 3% to 10% is plausible. But the evidence isn’t particularly strong.

Why did people get interested in creatine having cognitive benefits?

Because of Rae et al. (2003). They took a group of 45 healthy vegetarian or vegan university students in Australia. They did a cross-over trial where half of people got 5 grams of creatine per day for six weeks, followed by a six-week wash-out period, followed by the other half of people getting creatine. Their results were amazing, with huge improvements on Raven’s matrices (RAPM) and backward digit span (BDS):

In their analysis, creatine increased BDS by 1.19 standard deviations, and RAPM by 1.76 standard deviations. If we convert those numbers to IQ points (where 1 standard deviation ←> 15 IQ points), that would mean increases of 17.85 and 26.4 IQ points, respectively. In both cases, the results were highly significant (p < 0.0001).

Does that replicate?

No. Following that paper various groups tried similar experiments but no one found such a large or statistically significant effect. After twenty years of inconclusive results, Sandkühler et al. (2023) set out to give a definitive reproduction. In my view, this is the highest-quality RCT ever done on the cognitive benefits of creatine.15 They largely borrowed the experimental design of Rae et al., although they did the experiment in Germany, used a larger sample of 123 people, used half non-vegetarians, and they dropped the wash-out period. Here are their main results:

(T1 shows test results at baseline. T2 shows results after six weeks of creatine or placebo. T3 shows the results after another six weeks, where the placebo group crossed over to creatine and vise versa.)

Overall, everyone got better over time, probably from practice. On backwards digit span, during the first six weeks, the group getting placebo actually improved slightly faster than the group getting creatine. But when those groups switched between getting placebo and creatine, that (formerly placebo, now creatine) group improved even faster. Just staring at the graph, this suggests some benefit. On Raven’s matrices, the same thing happened, but with a greatly reduced magnitude.

They fit a statistical model and report an effect size of 0.17 standard deviations for backwards digit span (~2.5 IQ points, not quite statistically significant) and 0.09 standard deviations for Raven’s matrices (~1 IQ point, not even close to significant). They found no extra benefit for vegetarians, not even a non-significant benefit.

As far as I can tell, this discrepancy has never been convincingly explained. Rae et al.’s 2003 experiment seems well done. The results are too large to be explained by p-hacking and too statistically significant to be explained by random noise. Maybe for some reason, Rae et al.’s cohort had lower baseline creatine levels? It’s very odd. But history suggests that when an exciting result is followed by a disappointing replication, we should bet on the disappointing replication.

What about all the other RCTs? Doesn’t this call for a meta-analysis?

In principle, yes. The trouble is, most of the studies don’t report the numbers needed for a good meta-analysis. They do some experiment giving creatine to half of people and placebo to the other half, and measure how those groups do on some cognitive test. Then they fit some statistical model and report p-values or whatever. But they never actually publish the raw means and standard deviations.16

Fortunately for us, Xu et al. (2024) contacted the authors for all those trials and got their raw data. According to their meta-analysis, creatine had the following effects.

Domain Effect size (standard deviations)
Overall cognitive function +0.34
Executive function +0.32
Attention +0.22
Memory +0.31
Processing speed +0.01

Unfortunately for us, that paper is bad. They claim that several of these results are statistically significant, but a 2026 commentary points out that they made an error that amounts to double-counting the same data for several studies.17 For that reason, I haven’t shown their (incorrect) confidence intervals. If computed correctly, I suspect none of the results would be statistically significant. Technically, the above point estimates are also wrong, although the error shouldn’t systematically bias them in either direction.

In general, I have to tell you that I really don’t trust this paper. It’s very sloppy with tons of missing details. But as far as I can tell, no one else has ever assembled the data needed to do a good meta-analysis. So I think those numbers are the best summary we have.

So who can we trust?

I’ll tell you who I trust: The European Food and Safety Authority (EFSA). In 2024, a firm selling creatine applied to the EU to be allowed to advertise cognitive benefits. This led the EFSA to publish Creatine and improvement in cognitive function: Evaluation of a health claim pursuant to article 13(5) of regulation (EC) No 1924/2006.

Here’s what they have to say (I’ve cut references for readability):

The Panel considers that, overall, the 10 human intervention studies […] do not show a consistent effect of creatine supplementation on cognitive function. The Panel notes that the acute effect of creatine on working memory reported in some studies […] was not observed at lower creatine doses […] or with continuous consumption of creatine. The Panel also notes that the effect of creatine […] reported in one study is an isolated finding across the body of evidence, where no effect of creatine supplementation was observed on other cognitive domains, including different facets of memory (episodic, short‐term, visual), verbal fluency, attention, alertness, processing speed, psychomotor speed, executive function and general cognitive ability/flexibility and fluid intelligence. Finally, the Panel notes that the three intervention studies conducted in diseased individuals do not support an effect of creatine supplementation on cognition.

I think we should consider this definitive. I’d go so far as to say this document probably represents the greatest effort our civilization has ever made to understand if creatine has cognitive benefits.

But we need to remember the ESFA’s role. They’re asking if creatine has been proven to have cognitive benefits, because they’re deciding if it should be legal to advertise cognitive benefits. They say no and I believe them. But that doesn’t mean there are no cognitive benefits.

Are there other reviews of the RCTs?

Yes. Here are all the recent reviews I could find, with a few representative quotes from each:

Review Quotes
Avgerinos et al. (2018) “There was evidence short term memory and intelligence/reasoning may be improved by creatine administration.”

“Performance on cognitive tasks stayed unchanged in young individuals.”

“Vegetarians responded better than meat-eaters in memory tasks”
Dolan et al. (2019) “the blood–brain barrier is an obstacle for circulating creatine”

“may improve the performance in some cognitive tasks, particularly in stressful conditions (e.g. mental fatigue, exhaustive exercise).”
Roschel et al. (2021) “potential for creatine supplementation to improve cognitive
processing, especially in conditions characterized by brain creatine deficits”

“supplementation studies concomitantly assessing brain creatine levels and cognitive function are needed”
Prokopidis et al. (2023) “After correction, our overall analysis showed that creatine monohydrate does not improve overall memory performance (standardized mean difference 0.19; 95% confidence interval, –0.07, 0.46)”
Xu et al. (2024) “Creatine supplementation showed significant positive effects on memory and attention time, as well as significantly improving processing speed time. However, no significant improvements were found on overall cognitive function or executive function.”
McMorris et al. (2024) “Creatine supplementation has no significant effect on young healthy participants in unstressed situations. Moreover, the review show mixed results for stressed groups.”

“Vegans do not intake sufficient […] creatine to ensure the levels necessary for maintaining optimal cognitive output.”

“Closer examination of [the evidence] suggests that there may be more positive outcomes of supplementation than the research so far provides.”
UK NHCC (2024) “A cause-and-effect relationship has not been established between the consumption of ≤3g per day creatine and improved cognitive function.”

On average, the RCTs do find a small positive effect, just not a statistically significant positive effect. As I so often point out, that’s exactly what we would expect if the true effect were positive but small. But it’s also entirely possible that this is due to random chance or p-hacking or publication bias. Gwern contacted one author and found that publication bias did in fact occur.

Overall, I think the RCTs provide very weak evidence in favor of a small benefit for healthy adults. (Perhaps 0.1 to 0.3 standard deviations, depending on the measure.) I also think they provide moderate evidence against a larger effect for healthy adults (above, say, 0.5 standard deviations) and weak evidence for a small benefit for adults that are “stressed” in some way that might diminish creatine, such as being older, vegan, or physically exhausted.

Can you summarize the evidence in favor of creatine making you smarter?

I would love to do that:

  • Creatine is special. Very few nutrients really make you stronger, but creatine does.
  • Few parts of the body other than muscles use significant creatine, but the brain does.
  • Creatine can cross the blood-brain barrier.
  • Creatine is vital for the brain to function correctly.
  • Supplementing creatine probably increases creatine levels in the brain, at least a little.
  • Some RCTs suggest a cognitive benefit.

Can you summarize the evidence against creatine making you smarter?

Yes:

  • We don’t fully understand how the brain uses creatine. There is no clear mechanistic story for why supplementing creatine should make you smarter.
  • The best analogy for how the brain uses creatine is how your muscles use creatine for endurance exercise. But creatine has little benefit for endurance exercise.
  • It hasn’t been firmly established how much (or if) supplementing creatine increases creatine levels in the brain.
  • The RCTs suggest a benefit that is quite small, on the order of 1 to 3 IQ points.
  • The RCTs are not statistically significant.

Does creatine make you smarter?

I don’t know. Maybe a little.

You could make an argument like this: Creatine is crucial for the brain (somehow) so it’s safest to keep levels high, just in case. But I’m not sure I buy that. Creatine is crucial for the brain but there are several hints that evolution knows that, and so regulates levels in the brain more tightly than in muscles.

I might buy that argument for vegetarians or vegans. But there is scant experimental evidence for extra cognitive benefits in those groups, and even some evidence that vegetarians may not have much lower brain creatine levels, despite vastly lower consumption.

And if creatine is helpful, the likely benefit is probably quite small. Say you think there’s a 50% chance creatine increases IQ by 1 point and a 50% chance it’s useless. Is it actually worth the trouble of taking 5 grams of creatine every day for an expected increase of 0.5 IQ points? I’m not sure.

  1. Technically, they found an increase in dihydrotestosterone (DHT) but not testosterone. 

  2. Conveniently, in typical cellular conditions, the body can extract around 100 J of energy from 1 gram of ATP. So we can convert 1 gram ≈ 100 J and 1 gram per second ≈ 100 J / second. You may recall from high school that a watt is defined as 1 watt = Joule per second. 

  3. Wikipedia quotes a paper saying people make / recycle around 50 kilograms per day. That would imply that people make around 0.5787 grams per second. But this is in tension with the idea that people use 100 watts at rest. Since that 100 watt number seems to be more strongly established, I think 1 gram per second is a better estimate. 

  4. Why do you breathe? You breathe because your mitochondria need oxygen to make ATP. When you exercise, you breathe faster so that your mitochondria can make more ATP. You can actually calculate how much ATP your mitochondria make using your VO₂ max score: For each liter of oxygen you use, you make ~21,000 joules of energy, corresponding to ~210 grams of ATP. If you have a typical VO₂ max score of 40 mL/kg/min and you weigh 70 kg, that means you are using 2.8 liters of oxygen per minute, which corresponds to ~588 grams of ATP per minute or ~9.8 grams per second. 

  5. A Tour de France cyclist might burn 1500 or even 2000 watts for hours, meaning they are producing ~20 grams of ATP. They can do this because they’ve trained their bodies to have more mitochondria and better oxygen delivery to those mitochondria. But it takes a few seconds for the body to ramp up and start producing this much power. 

  6. The “T” in “ATP” is for “triple”, meaning there are three phosphate groups. The “D” is for “di”, meaning there are two phosphate groups. 

  7. There’s also stored energy in the form of glycogen. This takes a few seconds to come online, and lasts a few minutes. In a sprint, you aren’t limited by glycogen stores running out, but by having too much acid buildup.

    So, effectively, the body has five levels of cached energy:

    1. The mechanical energy stored in the elastic strain of the myosin.
    2. The chemical energy stored in ATP molecules. (Recharges myosin)
    3. The chemical energy stored in (phosph)ocreatine molecules. (Recharges ATP)
    4. The chemical energy stored in glycogen. (Recharges ATP and thus creatine.)
    5. The chemical energy stored in food and fat. (Used to recharge glycogen (food) and ATP (food or fat) and thus creatine.)

  8. It’s 12.5% rather than 16.67% because your short-term energy storage is ~75% phosphocreatine and ~25% ATP, and supplementing creatine does not increase ATP. 

  9. I’m not sure to what degree this math actually explains why we see a 5-15% increase in strength in creatine studies versus just being a coincidence. It’s a jump from “X% more short-term energy storage” to “X% increase in max bench press”. 

  10. Compared to our evolutionary ancestors, we have long helpless childhoods, low muscle mass, and smaller brains

  11. I do wonder about this given how many people died violent deaths in non-state societies. But how often would 10% more strength tip the outcome? 

  12. I find it amusing that lots of sources refer to extra water retention as a “common side effect” and even report statistics, when a far as I can tell it’s essentially guaranteed by physics. 

  13. Tsuji et al. report a phosphocreatine to ATP ratio of 0.77 in grey matter and 2.18 in white matter, Lu et al. reports ~1.45 in entire brains, and Hetherington et al. report 1.0 in white matter, 1.6 in gray matter, and 2.1 in the cerebellum. 

  14. Difficulty synthesizing creatine is treated by supplementing creatine. Creatine transporter defect currently has no effective treatment. 

  15. After writing this sentence, I later noticed that this experiment had apparently been funded by the Effective Altruism Foundation, Effective Ventures, and personally by (well-known AI alignment researcher) Paul Christiano

  16. I know this sounds odd, but it’s very common. Everyone wants to establish truth, not just create data so someone else can establish truth. It’s hard to blame them, given their incentives. 

  17. Another 2022 meta-analysis by Prokopidis et al. found similar results but apparently has a similar problem. Prokopidis et al. deserve credit for acknowledging the issue and issuing a correction. However, Prokopidis et al. only look at memory, and they seem to be working with before-after scores on the same people, rather than comparisons between the placebo and creatine groups. 



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emrox
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The Men That Don't Fit In

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There's a race of men that don't fit in,

    A race that can't stay still;

So they break the hearts of kith and kin,

    And they roam the world at will.

They range the field and they rove the flood,

    And they climb the mountain's crest;

Theirs is the curse of the gypsy blood,

    And they don't know how to rest.

If they just went straight they might go far;

    They are strong and brave and true;

But they're always tired of the things that are,

    And they want the strange and new.

They say: "Could I find my proper groove,

    What a deep mark I would make!"

So they chop and change, and each fresh move

    Is only a fresh mistake.

And each forgets, as he strips and runs

    With a brilliant, fitful pace,

It's the steady, quiet, plodding ones

    Who win in the lifelong race.

And each forgets that his youth has fled,

    Forgets that his prime is past,

Till he stands one day, with a hope that's dead,

    In the glare of the truth at last.

He has failed, he has failed; he has missed his chance;

    He has just done things by half.

Life's been a jolly good joke on him,

    And now is the time to laugh.

Ha, ha! He is one of the Legion Lost;

    He was never meant to win;

He's a rolling stone, and it's bred in the bone;

    He's a man who won't fit in.

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emrox
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The Human-in-the-Loop is Tired

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Yet another thought piece about LLMs. I know. Bear with me.

This is an attempt to put words around something I think most developers are experiencing right now but haven't had time to make sense of. Programming with LLMs is genuinely useful and genuinely destabilizing. These two things coexist. If we pretend the second one isn't happening, we will all burn out.

At Pydantic, we build tools that developers use to validate data, build AI agents, and observe what their systems are doing in production. We are, quite literally, in the business of making LLM-powered software more reliable. And we are also having a weird time.

This isn't a thinkpiece about whether AI will replace programmers. It's not a doomer essay and it's not a hype piece. It's an honest account of what it feels like to be a developer right now, from someone inside it, and some thoughts on what might actually help.

Hands in the fabric

When I was first learning to code in my early twenties, I remember having this distinct sensation that programming let me dip my hands into the fabric of the universe and shape it to my will. This was, of course, before I'd hit too many compile errors. But that feeling of touching some deep fundamental layer of abstraction, of being able to make things from nothing but logic, has always stuck with me.

I'm not a Computer Science graduate. I'm a designer and a programmer — formally trained in the first, self-taught in the second. I came to the formalisms of software engineering through painful experience rather than academic instruction. If anything, that made me take those principles more seriously once I understood them. When you've earned your opinions about architecture and code quality the hard way, they feel less like textbook rules and more like scar tissue.

That primal feeling of creation? It's the same promise that the low-code and no-code tools of the 2010s kept making but never quite delivered on. I'm old enough to remember building web pages in Dreamweaver, watching Adobe spruik zero-code design tools that generated absolute spaghetti under the hood. It was always almost there, just good enough to hint at a future that was just around the corner (if only you were smart enough to grasp it).

If you're cynical about the current wave of AI tools, I get it. We've been promised this before. But this time the gap between promise and reality has actually, finally, narrowed to something meaningful. And that's exactly what makes it so unsettling.

What "the code writes itself" actually feels like

Yes the code (sorta) writes itself, but the human reviewing, directing, and course-correcting feels worse, not better.

I recently had a conversation with my colleague Douwe, who maintains the Pydantic AI framework and has been one of the most thoughtful people I know about integrating LLMs into open source workflows. He described waking up to thirty PRs every morning, each one pulled overnight by someone's AI, and needing to make snap judgment calls on every single one. The temptation to delegate the review itself to an AI was enormous. But, as he put it: "at that point, what am I still doing here?".

The honest truth is that in the last few months, there have been days when I have spent close to two full days writing a plan for an LLM to execute: obsessively clarifying, specifying, re-specifying, only to have it still do something inexplicably stupid. Port a React hook into a Storybook story file. Read from the wrong plan. Invent components that don't exist. And these aren't errors of capability; they're errors of coherence. The models are smart enough to produce plausible code, but not always smart enough to maintain a coherent intent across a complex change.

This creates a peculiar new kind of fatigue, the fatigue of supervision: of holding the intent in your head while the machine generates volumes of mostly-correct output that still needs your eyes, your judgment, and your taste. Douwe put it well: he used to get a dopamine hit from collaborating with a real person on a cool feature in open source. Helping someone become better at their craft. Now, he said, "everything I write goes into some AI black hole. There's no person on the other side actually learning anything." That loss is real and it's worth naming.

The intensity trap

Simon Willison recently highlighted a Berkeley Haas study which describes how AI usage increases the intensity of work. The constant pull of "one more prompt at the end of the day, one more feature that could make this perfect." I felt that one in my bones. I was up until nearly 2am recently, prompting, because I was so close to getting a plan right. Or so I thought.

It's all a part of the plan

Marcelo, another Pydantic colleague, when asked about his Claude Code session freezing said: "just open 5 claude sessions. You'll never notice because you're busy giving feedback to the others." He was joking. I think. But it captures something true about the current moment. The parallelism is exhilarating and kind of feral. The number of things you can start has dramatically increased. The number of things you can thoughtfully finish hasn't changed at all, because that part still requires the one resource we can't parallelise: your brain.

Here's a term for what I think is happening: the human reward function problem. In machine learning, a reward function tells an agent what good looks like. Writing code by hand was never easy, but it was full of small rewards. Solving a problem in your head. Understanding a gnarly bit of logic. Watching the code compile. The feeling of control. LLM-assisted programming has automated much of the work that generated those dopamine hits and replaced it with the cognitive load of review and supervision. The satisfying part shrank. The exhausting part grew. And there are no new rewards to fill the gap.

If you're feeling like your work is simultaneously more productive and less satisfying, you're not broken. The feedback loop is broken. And I think we need to start treating that as an engineering problem in its own right, not a personal failure.

It's also, frankly, quite lonely. Programming with an LLM is an intensely solitary activity.

You and the machine, going back and forth, refining and prompting and reviewing. The natural moments where you'd turn to a colleague to ask a question, to rubber-duck a problem, to share the small victory of something finally clicking. Those moments get quietly replaced by another prompt. In a team without a strong existing culture of collaboration, this has a tendency to further separate people, to chill communication at precisely the moment when you most need the reassurance that other humans are finding this hard too.

And it's addictive in a way that makes the isolation worse. Sometimes you get something brilliant, sometimes garbage, and you never quite know which. Textbook Skinner Box. It can be genuinely hard to step back and remember that you're allowed to just... write code. But switching between LLM-assisted and manual work is jarring and uncomfortable, two very different modes of thinking, and it takes a kind of maturity and confidence to give yourself permission to switch.

Breakpoints

This moment brings to mind the fear and angst caused by responsive design. I was working as a designer and frontend developer at the time, following Ethan Marcotte and the Zeldman / A Book Apart crowd like everyone else, and I remember how unsettling it felt to be told that the fixed-width layouts we'd all mastered were basically over.

For the younger devs: there was a genuine cultural moment around 2009 when websites moved from fixed, pixel-perfect, magazine-style layouts to fluid, responsive ones. And designers hated it. The loss of control was existential for people whose entire identity was built around precise layouts and perfect grids. You're telling me the user might see my design at any width? On any device? That the layout I crafted would... flow?

Responsive design animation

Image design by Jyotika Sofia Lindqvist

The resistance was intense. And it was understandable. People had built real expertise in a paradigm that was being fundamentally disrupted. The designers who thrived through that transition were the ones who reframed their skills. The eye for proportion still mattered. The understanding of hierarchy still mattered. The craft didn't die, it evolved. What became less relevant was the obsession with pixel-level control. What became more relevant was understanding systems, adaptability, and designing for uncertainty.

I don't want to oversell this parallel. Responsive design played out over years. The current shift is measured in months. Agencies lost clients and designers lost gigs over the responsive transition, but it didn't carry the same existential dread. The stakes are materially different, and the pace is genuinely exhausting in a way that the responsive transition never was. But the underlying pattern, of craft evolving rather than dying, of the core skills mattering more not less, I think that holds.

Working with LLMs on code feels like a similar inflection point. The skill isn't gone, it's shifting. You're not less of an engineer because you didn't hand-write every line. But you do still need to know what good looks like, arguably more than ever, because you're now the quality gate for a much higher volume of output.

What survives

In an era when anyone can produce reasonable-looking UI and code that compiles, the distinguishing markers become: taste, nuance, mature architectural opinions, and the contrarian calls that come from genuine expertise rather than pattern-matching.

It's noticeable to me that we are most successful guiding LLMs in the domains where we understand the code, the decisions, and the trade-offs most deeply. As we venture into the shallow ends of our skill sets, the outputs become markedly more impressionistic. Further from production-ready. More plausible-looking, less actually correct. The model doesn't know what it doesn't know, so it fills the gaps with confidence. Sound familiar? It's a very human failure mode, too.

But new skills are also emerging. I've started running what I call pre-mortems on complex plans: asking a fresh LLM session to assume the plan has catastrophically failed and diagnose why. It catches specification gaps that I miss after two days of being too deep in the details. One of our engineers built a tool that extracts rules from thousands of his past code review comments to seed an AGENTS.md file, essentially encoding years of implicit engineering judgment into instructions an LLM can follow. That's not the death of expertise. That's expertise being distilled.

The people who are finding their footing right now seem to share a few traits: they have strong opinions earned through practice, they can distinguish between principles that still apply and habits that were just bandwidth constraints, and they're willing to evolve their workflow without abandoning their standards.

A view from inside the loop

I don't think the current wave of AI represents the end of software engineering as a profession. I do think it represents a serious contraction and a fundamental reshaping of what the work is. The fear of obsolescence is legitimate. The fear of skill rot is legitimate. And the fear that if you don't go fast enough you'll be left behind is — while often overstated — not entirely unfounded.

But the bottleneck was never the code. It was always the human attention, the engineering judgment, the ability to hold a coherent vision for a system. We just didn't notice because writing code felt like the hard part. Now that it's being automated, those human capacities are revealed as the actual scarce resource. And scarce resources are valuable.

So if you're feeling overwhelmed, destabilized, simultaneously more productive and less happy, know that you're not alone. The team building the tools you're probably using to navigate this moment is feeling it too. We're debugging our reward functions in real time, same as you.

The code is changing. What we do with it is changing. How it feels is... a work in progress.

But the humans are still in the loop. We're just tired. And that's worth talking about.


We're building tools to make this less chaotic: Pydantic AI and Logfire. We're also hiring.

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emrox
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No, People Don’t Want More AI In Their Life

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Many companies silently assume that everybody wants more AI in their lives. That people are craving new AI features, new AI products, new AI workflows — that would all magically replace all existing outdated practices and broken ways of working.

But in reality, it seems like people don’t want more AI at all — at least not in the way most AI leaders envision it. Unsurprisingly, many AI features have low adoption and retention — at a very high cost of delivery, and a high risk of reputation damage.

The AI People Don’t Need

It’s remarkably difficult to make a strong argument with senior leadership, but AI is not a value proposition. New AI features don’t magically make for happy or excited customers. Because AI features are often bolt-ons and separate tools for employees to use, they typically take people out of their regular way of working.

AI is pretty good at amplifying shortcuts and shortcomings in organizations — from data quality to decision making. It can’t magically fix years of accumulated quick patches, technical debt, broken culture and internal politics. If anything, they become more visible with AI as inconsistencies or conflicting priorities and get handed directly to users, who are then left to make sense of the mess themselves.

Because in most organizations, work typically requires hopping on and off between plenty of disconnected and fragmented systems, with a new AI tool, they now have yet another system that they also need to hop on and off. Often it produces more work, and typically it’s not particularly rewarding work either.

On top of that, people are very much aware of the cost of finding and fixing AI hallucinations. Asking AI to generate a response might feel easier than writing from scratch, but it has a cost:

  • Skim through the entire AI output,
  • Spot key points to focus attention on,
  • Review/verify key points, one-by-one,
  • Check rationale for what follows next,
  • Articulate corrections + regenerate,
  • Review the response (a number of times).

For many people, AI isn’t something they can proactively choose and explore on their own — it arrives uninvited, at someone else’s pace. On top of that, plenty of messages amplify fears and worries about AI replacing work — so it’s hardly surprising that the perception of AI isn’t excitement. It’s resistance to change and deep anxiety about one’s place in a world that seems to be changing without them.

At best, AI features might be silently accepted or nodded away. At worst, AI raises concerns, doubts, caution — and calls for a healthy dose of skepticism. And sometimes it’s perceived as a threat or liability — because unlike other features, AI is neither predictable nor reliable.

People don’t dream of AI art museums or AI fridges or AI hotel reception or AI-narrated children’s books. They don’t want their children to have romantic AI partners. Most people don’t want to actively manage (and clean up after) a swarm of AI agents roaming in their bank accounts and acting on their behalf in the real world. And most notably, people don’t really want a magical box to speak to or type into all the time.

The AI People Actually Need

I’m always puzzled by the comparison of AI features with how unreliable humans are. But people don’t compare software with other people. They compare features with features — and if one feature in one product is unreliable, while a similar feature works flawlessly in another, they choose the latter. It’s not about AI or not AI, but rather what works consistently and reliably, and what doesn’t.

Many conversations about AI are conversations about the speed of delivery. But to many people, there is little value in increasing the speed of delivery. They want to do things well, with enough time to think and make good decisions. They also want to enjoy the time they spend working on things, rather than just ship faster. There is an enormous feeling of reward and achievement that slowly disappears, one vibe-coded change at a time.

People don’t change much. And after all these years, they (still) want features that are fast, accessible, reliable, predictable and useful — every single time. And ideally not the ones that replace their entire workflow, but that augment their way of working — and that take over the most mundane, annoying, and boring tasks that they find no pleasure in.

Many jobs are exposed to AI automation, but in many of them there is a rewarding, unique, creative part that requires taste, point of view, and perhaps even human intuition. And if AI automates boring parts of it, that’s an advantage for everyone. That’s also what enhances productivity and brings more joy in daily life.

When AI automates tedious and mentally exhausting tasks, its value is much easier to grasp. But for that, AI shouldn’t feel like a bolt-on. It should be deeply integrated into people’s existing workflows. It must also match existing mental models that they have developed and fine-tuned for years or decades. AI should adapt to how people think and make decisions, not the other way around.

And it doesn’t really matter if these features are branded as “AI”, “smart” or “automation”. However, they must work well for people using them. And that means that people must be aware of use cases where it actually helps them, and be inspired to find more use cases on their own.

Ironically, tools that work well there aren’t “AI-first” — they are “AI-second”. Subtle, humble, calm, ambient, taking a supportive role in the background for work that otherwise is remarkably dull and unnecessary.

I don’t want to read books written by AI. I don’t want to gaze upon paintings by AI. I don’t want AI to teach my children. I don’t want to have an AI therapist. I don’t want AI making my medical decisions. I want AI to do all the physical and mental labor that taxes me so I can read books written by humans and go to art galleries to engage with art made by humans. I want AI that makes my life easier rather than forces me to change myself.

Bo Young Lee
Wrapping Up

Perhaps I’m missing a bigger picture, and perhaps I’m just old school — but I really do like people. Their stories, their thinking, their emotions, their enthusiasm, their laughing. AI can be remarkably helpful in many situations, but so are people. And between the two, I would favor spending time with a human — however imperfect they are — every single time.

No, people don’t need more AI in their lives — they need AI to automate all the boring stuff they have to deal with every day, so they have more time and headspace to do things that they actually love and enjoy doing. That doesn’t mean spending more time with AI — but spending more time with people they love.

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emrox
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July 2026 Security Release

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The July 2026 security release for Next.js is now available
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Beyond the Prompt: The Social Costs of Generative Artificial Intelligence

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The Business, Entrepreneurship & Tax Law Review

Abstract

The rapid commercialization of generative artificial intelligence has produced extraordinary value for technology companies while systematically externalizing costs onto society. This Article provides a review of these externalities across three domains: environmental, human health, and digital infrastructure. The environmental burden includes massive energy and water consumption alongside ecologically destructive mining for rare earth elements and accelerating electronic waste. The human toll encompasses a documented pattern of AI-induced mental health crises, including suicide and self-harm linked to anthropomorphic chatbot design, raising novel questions of product liability in cases like Garcia v. Character.AI. The digital commons face degradation through industrialized data scraping enabled by the Ninth Circuit’s hiQ Labs v. LinkedIn decision, which effectively stripped platforms of their primary defense under the Computer Fraud and Abuse Act. This Article argues that the AI industry’s business model is predicated on a fundamental market failure: the privatization of benefit and socialization of cost. Until the costs of extraction, defense, and human harm are borne by those who generate the risk, the AI revolution will continue to levy an uncompensated tax on the broader economy.

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emrox
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