So Daniel sent us a really provocative one today. He writes: In AI workloads, we often focus on shaving off tiny amounts of latency. However, we tend to forget that there is a threshold for human reaction time, and below a certain number of milliseconds, any optimizations are mostly illusory. This threshold varies significantly based on factors like tiredness and inebriation. Provide rough parameters for this variation: what is the baseline reaction time, how much does fatigue or alcohol degrade it, and at what point do sub-threshold latency optimizations stop mattering to the end user?
Herman Poppleberry here, and man, Daniel is hitting on something that drives me absolutely wild in engineering circles. We are living in the era of the "latency arms race," where companies are burning millions of dollars in compute and engineering hours just to shave three milliseconds off an inference call. And the irony is, for the vast majority of human users, that improvement is literally invisible. It’s like tuning a car to go two hundred and one miles per hour instead of two hundred when the speed limit is sixty-five and the driver is half-asleep anyway.
It’s the classic "shaving the mountain" problem, right? You’re working so hard on the peak that you forget the base of the mountain is made of slow, squishy human biology. By the way, quick shout out to our scriptwriter for the day—today's episode is actually being powered by Google Gemini three Flash. It’s fitting, considering we’re talking about the speed of processing. But Herman, you’re the one who’s always looking at benchmarks. Is it really that extreme? Are we genuinely optimizing for things people can't perceive?
Oh, without a doubt. We’ve reached this point where technical benchmarks have become decoupled from human experience. In the AI space specifically, everyone is obsessed with "time to first token." They want that LLM to start spitting out text in fifty milliseconds. But if you look at the biology—which we’re going to dive deep into today—there is a hard floor. There is a "biological latency" that we can’t code our way out of.
It’s funny because you’ll see these GitHub repos where the lead dev is bragging about a two-percent gain in response time, and I’m sitting there thinking, "I haven't even finished blinking by the time that response finished." We’re essentially optimizing for the sake of the graph, not the person using the tool.
It’s the "Illusion of Speed." We treat latency as a linear value where lower is always better, but it’s actually a step function. Once you cross certain biological thresholds, the marginal utility of extra speed drops to zero. And what Daniel pointed out about fatigue and alcohol? That’s the real kicker. We’re building these ultra-fast systems for a user base that is often sleep-deprived, distracted, or, in some contexts, literally impaired.
Right, if you're a developer at two in the morning, your brain is running on a legacy dial-up connection. That ten-millisecond win on the server side is getting swallowed by the three-hundred-millisecond lag in your own prefrontal cortex. I want to really break down these numbers today. Where is the actual line? When does an engineer need to just put the keyboard down and say, "This is fast enough, let’s go work on something that actually changes the user's life"?
That’s the goal. We need to define the "Bio-Floor." If we understand the baseline of human reaction and how it degrades, we can actually set smarter engineering budgets. Instead of "as fast as possible," the goal should be "faster than the human bottleneck."
Well, let’s get into the actual biology of it then. If I’m sitting at my desk, sober and well-rested—which, let's be honest, is a rare state for me—what am I actually capable of perceiving? What is the "gold standard" for a human being?
To understand that, we have to look at how we actually process a stimulus. It's not just one thing; it's a chain of events that starts the moment a pixel changes on your screen or a sound comes out of your speaker.
So it’s a hardware problem, just a biological one.
Well, it’s a wetware problem. You have the sensory input phase, the neural transmission to the brain, the actual cognitive processing where you decide what the stimulus means, and then the motor response if you’re actually clicking a button. Each of those steps has a fixed cost in milliseconds that no amount of fiber optic cable can fix.
I love this. We spend all this time talking about GPU clusters and Infiniband interconnects, and we’re about to spend the next twenty minutes talking about the "interconnect" between your eyes and your thumb.
It’s the most important link in the chain! If you don't understand the receiver, you're just broadcasting into the void.
Alright, let's pull up the data. What are the baselines? Give me the numbers for the "perfect" human specimen before we start talking about how much a beer or a late night ruins everything.
So, for a standard visual stimulus—like a light flashing or a chat box appearing—the average human reaction time is roughly two hundred and fifty milliseconds. If it’s an auditory stimulus, it’s actually faster, around one hundred and seventy milliseconds, because the auditory cortex is a bit of a speed demon compared to the visual system.
Wait, auditory is faster? I always assumed seeing was the quickest way we took in information.
Nope. Sound hits the brain faster. It’s why sprinters start to a gun, not a light. But think about that two hundred and fifty millisecond number for a second. That is one-quarter of a second. If an AI response takes fifty milliseconds, and your brain takes two hundred and fifty to even realize it happened, you’re already in a situation where the system is five times faster than the observer.
That’s wild. So when we see these companies fighting over ten versus twenty milliseconds, they are fighting over a window of time that is literally ten times smaller than the time it takes for a human to blink.
It’s actually even more lopsided than that when you get into the "Instantaneous Perception" threshold, which is usually cited around one hundred milliseconds. That’s the "Doherty Threshold" territory. If a system responds in under a hundred milliseconds, the human brain literally cannot distinguish the delay. It feels "live."
So if I’m an engineer and I’ve already hit ninety milliseconds, and I’m killing myself to get to forty... I’m basically doing it for the "high score" on the benchmark, because no human on earth is going to say "Wow, this feels so much more 'live' than it did yesterday."
You’re optimizing for the placebo effect at that point. Or worse, you’re optimizing for a "well-rested, elite-tier gamer" who might—might—notice a difference in a high-twitch environment. But for an AI chat bot? Or a data analysis tool? It’s complete overkill.
And that’s the "rested" baseline. I want to see how these numbers fall off a cliff when you introduce reality. Because nobody using these tools is a "perfect specimen" in a lab. They’re tired, they’re stressed, maybe they’ve had a glass of wine with dinner. That’s where the "illusion" of these optimizations really starts to fall apart.
Oh, the degradation is massive. We’re talking about increases that make system latency look like a rounding error. When you look at the research on fatigue and alcohol, the "biological lag" doesn’t just go up by ten or twenty percent—it can double or triple.
This is going to be a reality check for a lot of people in DevOps. Let's dig into the actual "Degradation Parameters" and see just how slow we really are once the sun goes down.
Let's start with the baseline because it’s the anchor for everything else. In a perfectly controlled lab setting, a well-rested, sober human has a visual reaction time of about two hundred and fifty milliseconds. That’s the time it takes for light to hit your retina, travel to the visual cortex, get processed by the frontal lobe, and then send a motor command to your finger to click a mouse.
A quarter of a second. That feels fast when you say it, but in the world of computer science, a quarter of a second is an eternity. We measure database queries in single-digit milliseconds.
It is an eternity. And this brings us to the core of the problem. We are aligning technical optimization with human biology, but the biology hasn't had a firmware update in fifty thousand years. There is a physiological floor. If you're an engineer working on an AI interface and you shave your latency down from sixty milliseconds to thirty milliseconds, you are operating entirely beneath the threshold of human perception.
So, you're saying that for the end user, those thirty milliseconds of hard-won engineering are literally invisible? Like, their brain physically cannot register that the "after" is faster than the "before"?
For a standard UI task, yes. It falls within what we call the sensory memory window. If a change happens faster than one hundred milliseconds, the human brain perceives it as instantaneous. This is the Doherty Threshold, named after Walter Doherty’s research at IBM in the early eighties. He found that when a computer responded in less than four hundred milliseconds, user productivity skyrocketed because they stayed in a flow state. But once you drop below one hundred milliseconds, you’ve hit the limit of "instant."
I love that. We’re basically building these incredibly fast Ferraris to drive on a road where the speed limit is set by a biological snail. I mean, if the brain needs a hundred milliseconds to even feel like something is "live," why are we stressing over five milliseconds of network jitter?
Because benchmarks sell. But the reality is that the "human bottleneck" is the only one that truly matters for UX. And that bottleneck isn't just wide—it's incredibly inconsistent. When you move from that two hundred and fifty millisecond lab baseline into the real world, where people are actually using these AI tools, the biology starts to degrade rapidly. This is where the "illusion" of speed becomes a massive waste of resources.
And that inconsistency is the real killer, right? Because we aren't just talking about a static two hundred and fifty millisecond delay. If I'm using a coding assistant at two in the morning, I’m not that same "baseline" human anymore. My internal ping is spiking.
It really is. The variability is staggering. If you look at the data on fatigue, someone who has been awake for twenty-four hours sees their reaction time jump from that two hundred and fifty millisecond baseline up to four hundred or even five hundred milliseconds. That is a two hundred millisecond tax just for being tired. To put that in perspective, that’s twenty times the latency of a decent fiber connection.
So, while the engineer is pulling an all-nighter to shave ten milliseconds off the model's inference time, their own brain has added two hundred milliseconds of lag to the system. The irony is thick enough to cut with a knife.
It gets even worse with alcohol. At a point zero eight blood alcohol concentration, which is the legal limit for driving in many places, you’re looking at an extra one hundred to two hundred milliseconds of delay. Your brain's "processing power" is literally throttled. The signal from the eye to the motor cortex is just hitting more traffic.
I’m curious about the actual mechanism there. Is it just that the neurons are firing slower, or is it a "software" issue in the brain where the decision-making step takes longer?
It’s both, but the decision-making phase is the biggest culprit. You have sensory input, which is lightning fast, and neural transmission, which is also quite quick. But then you hit the "central processing" stage in the frontal lobe where the brain has to decide: "Okay, the AI finished the sentence, now I need to hit tab." That integration of information is what degrades first under stress or exhaustion. It’s like the brain’s operating system is swapping to disk because the RAM is full.
That makes sense. It’s why gamers obsess over one hundred and forty-four hertz monitors, right? They want that sixteen millisecond frame time, but they’re still limited by their own two hundred millisecond nervous system.
Well, not exactly—I mean, the reason high refresh rates feel better is because they reduce "system" latency, which gives the human more of their "biological budget" back. But if you’re a professional gamer who’s exhausted, all that expensive hardware is being negated by your own biological lag.
It really highlights the diminishing returns. If we’re already below that one hundred millisecond "instant" threshold, pushing for ten milliseconds is like trying to make a clock more accurate by measuring nanoseconds when the person looking at it can only see the minute hand.
That’s the "illusion of speed" Daniel was talking about. In a voice assistant, for example, the auditory cortex actually processes signals faster than the visual cortex—about one hundred and fifty milliseconds. But if the AI responds in fifty milliseconds versus one hundred, your brain literally cannot tell the difference. You’ve hit the physiological floor. We’re spending millions of dollars on compute and optimization to beat a clock that the user isn't even capable of reading.
It makes me wonder about the engineering meetings where people are high-fiving over a five millisecond win. We are literally optimizing for a ghost in the machine. If you look at the resource waste involved in that kind of over-optimization, it is staggering. You are talking about more expensive interconnects, more complex pruning of models, and massive engineering hours, all to achieve a result that is mathematically invisible to the person holding the phone.
That is the second-order effect that really bothers me. When you obsess over raw benchmarks instead of human-centric latency, you end up with a system that is incredibly brittle. You might sacrifice model accuracy or safety guardrails just to hit a latency target that provides zero actual utility. There was a fascinating internal study from a major voice AI provider recently. They spent months optimizing their stack to get response times down from one hundred and fifty milliseconds to fifty milliseconds. When they ran A-B tests with real users, the "satisfaction" scores were identical. Not "slightly better," but statistically indistinguishable.
Because to the human ear, one hundred and fifty milliseconds is already "instant." It is like trying to make a light bulb turn on faster than the speed of light. Once you are under that threshold, the brain just checks the "immediate" box and moves on to processing the actual content of the speech.
Contrast that with something like real-time translation tools. If you are in a live conversation and the translation lag is two hundred and fifty milliseconds, the natural flow of human turn-taking breaks down. People start talking over each other because the "silence" they perceive is actually just the AI thinking. In that specific case, getting under two hundred milliseconds is the difference between a tool that works and a tool that is frustrating. But once you hit one hundred and eighty milliseconds? You are probably done. Any further gains are just vanity metrics.
So the real skill for a product manager or an AI engineer isn't just "make it fast," it is "know when to stop." You need a latency budget that accounts for the human at the other end. If your user is a sleep-deprived doctor using a hands-free AI scribe in an emergency room, their internal "lag" is already three hundred milliseconds higher than a well-rested engineer's. Optimizing your API calls by ten milliseconds in that environment is like putting a spoiler on a tractor. It looks cool on the spec sheet, but it does not change the harvest.
We should be prioritizing optimizations that actually impact the "perceived" experience. Things like jitter reduction or streaming tokens at a consistent reading pace. A steady, predictable flow of information is far more valuable to a human brain than a erratic, "ultra-fast" burst that keeps changing its rhythm. We need to stop benchmarking against silicon and start benchmarking against the nervous system.
Which brings us to the actual million-dollar question for the people building these systems. If the goal isn't just "zero latency," how do you actually measure success? Because right now, most engineering teams are just staring at P99 system latency on a dashboard, which is basically like checking the engine temperature while the car is sinking in a lake. It doesn't tell you if the driver is actually getting where they need to go.
That is the first big shift we need. We have to stop measuring system latency in a vacuum and start measuring user-perceived latency. In the industry, we call this "Time to Meaningful Interaction." If you're building a coding assistant, the benchmark shouldn't be how fast the entire block of code generates. It should be how fast the first character appears so the developer’s eye can start tracking it. If you stream that first token in eighty milliseconds, the user's brain registers "instant," even if the rest of the function takes another two seconds to finish. You’ve successfully "hidden" the rest of the latency inside the human reading speed.
It’s a magic trick, basically. You’re distracting the lizard brain with a shiny object while the heavy lifting happens in the background. But to pull that off, you need a specific optimization target based on these biological thresholds we've been talking about. Instead of saying "make it as fast as possible," a product lead should be saying, "Our target is one hundred and twenty milliseconds because our users are primarily mobile and likely distracted."
Defining those "latency budgets" is critical. If you know the human auditory cortex processes signals in about one hundred and seventy milliseconds, and you're building a voice interface, aiming for fifty milliseconds is literally burning money. You could take that extra one hundred milliseconds of "budget" and use it to run a much larger, more sophisticated model that gives a better answer. You're trading invisible speed for visible quality. That is a massive win for the end user that most teams miss because they're chasing a number on a graph.
So if I'm a developer listening to this, how do I actually find that ceiling for my specific product? I'm guessing it’s not as simple as just googling "human reaction time" and calling it a day.
You have to run the "Drunk and Tired" test, or at least the professional equivalent. Conduct user studies where you artificially inject latency in fifty-millisecond increments. Don't tell the users what you're doing. Just ask them to rate the "snappiness" of the tool. What you’ll almost always find is a plateau. There will be a point—maybe at one hundred and eighty milliseconds, maybe at two hundred—where the satisfaction scores flatline. Once you hit that plateau, stop. Do not spend another dime on speed. Put those engineering hours into features, or accuracy, or just lowering your compute costs.
I love that. It’s about finding the "Minimum Viable Speed." Anything beyond that is just vanity engineering. We need to stop treating the human brain as this infinite processing machine and start treating it like the slightly laggy, biological hardware it actually is. It’s much easier to design for a donkey than it is to design for a supercomputer, right Herman?
I'll take that as a compliment, Corn. If we design for the donkey, we actually end up with a tool that works for the human.
Wait, I mean, you’re hitting on the core of user-centric design. If we design for the biological constraints of the donkey, we actually end up with a tool that works for the human.
Well, before we wrap this up, I have to wonder where the ceiling actually is long-term. We’ve spent this whole time talking about the biological hardware we’re born with—the one hundred and seventy millisecond auditory lag, the visual processing delay. But what happens when the hardware changes? I’m looking at things like Neuralink or non-invasive brain-computer interfaces. If we bypass the eyes and ears and pipe data directly into the cortex, does that hundred-millisecond "instantaneous" threshold just collapse?
That is the ultimate edge case. If you bypass the peripheral nervous system, you’re essentially shortening the cable length of the human data bus. You might see a world where "instant" feels like ten milliseconds because you’ve removed the mechanical delay of the eye and the chemical delay of the synapse. But until we’re all cyborgs, the most sophisticated AI in the world is still going to be gated by a biological processor that needs a nap and a sandwich.
And maybe a glass of water if they’ve been hitting the baseline-degrading fluids too hard. The big takeaway for me today is that "faster" isn't a strategy—it's a resource drain if you don't know who’s on the other end of the screen. We need to stop engineering for benchmarks and start engineering for the actual, slightly blurry, often tired human experience.
Well said. This has been a fascinating deep dive into why your lag might not actually be your lag. Thanks as always to our producer, Hilbert Flumingtop, for keeping the gears turning behind the scenes.
And a big thanks to Modal for providing the GPU credits that power this show and help us keep our own latency under control. This has been My Weird Prompts. If you found this useful, search for My Weird Prompts on Telegram to get notified the second a new episode drops.
We'll see you in the next one.
Stay sharp. Or at least stay rested.
