The problem is… When (if) we pick up the phone today it’s because we want to speak to a human.
Most people, avoid phone calls if possible.
If I get a call and it’s an AI, I, like everybody else, is putting down.
If I’m picking up the phone to call a company, it’s because I can’t achieve what I want to on their website.
These AI phone calls are as or more limited than the website.
There is a use-case for voice AI - most of these demoes really miss the mark with “we’re going to replace your call center”.
If founders had any idea how much performance matters in a call center, and how hard it is to achieve, they’d focus on a use case better served by voice AI.
I had deep-access to this industry in a past career - the way online sportbooks talk about their customers in private is all you need to know to know that this isn’t business, it’s predation.
“I followed up by asking GPT-4o to draw “Explain who the enemy is”, and it generated an image with nothing except the word “SELF” in bold letters on a beige background. Odd.”
While this might have just been 4o guessing what the user wants given the prior thread obsessed with self… “Self” as the enemy is a beautiful idea common in eastern philosophy and the unexpected and odd way it’s delivered is like an allegory of a Zen master’s sudden lesson to a student.
Great experiment! It’s important to highlight that even in rigorously blinded studies which find a drug ineffective on average, there can still be genuine responders—this is known scientifically as “heterogeneous treatment effects.” Essentially, individuals vary in genetics, metabolism, and neurochemistry, which can cause meaningful differences in drug responsiveness. Thus, an N=1 trial, like yours, might indeed reveal real personal benefits that wouldn’t appear in a population-level analysis.
However, systematically performing robust N=1 trials individually across multiple substances can be impractical—too laborious and time-consuming for most people. An interesting business model might be a startup that facilitates personalized N=1 experiments at scale: providing users with high-quality compounds, matched placebos, structured dosing schedules, and data-tracking tools. This could empower more individuals to accurately assess personal efficacy across a wide variety of substances, potentially offering valuable, personalized insights that large-scale clinical trials can’t capture.
I made an app for this called Reflect [1]. You can run self guided experiments for anything you can model as a metric in the app. I just wrote about an experiment I did with nootropic coffee [2].
I like this idea! To build on it, the schedules could be non-random (systematic with random offset) and test combinations of substances in a way that maximises information both in the cases where substances are independent and when they have suspected interactions.
There’s a few reasons for this. There’s a few ways to make graphene. You can use CVD or you can use mechanical exfoliation. Mechanical exfoliation requires scotch tape and scales to maybe a flake per hour per grad student. CVD is quite scalable but makes shitty graphene. A lot of graphene breakthroughs (superconductivity for instance) needs mechanically exfoliated graphene.
Secondly, process fab is VERY conservative. There’s numerous amazing ferroelectrics that you can grow tons of that would absolutely spank NAND flash. However, they’re not silicon fab, so nobody makes them.
> There’s numerous amazing ferroelectrics that you can grow tons of that would absolutely spank NAND flash. However, they’re not silicon fab, so nobody makes them.
So why doesn't somebody new start making them and put all the current flash producers out of business?
Is there actually a special property of scotch tape that makes it the ideal candidate over some more specialized industrial adhesive? Or are these references to scotch tape generally just references to the fact that you _can_ use scotch tape like the original graphene experiment?
It happens to have a good level of stickiness. People also use blue nitto tape, and tape used for fixturing on dicing saws. I think basically anything could work, it's just that people use what's lying around.
It's because it's just about impossible to handle: the number one thing a sheet of graphene wants to do is stick another sheet of graphene on top of it and become...regular graphite.
It takes a long time to go from lab bench and physics papers to practical use to mass produced and generally available practical use.
Graphene has incredible properties as a structural material too but so far producing it at that scale and making it behave properly in things like composites has been very hard. But the physics says once we get it to work we have composites many times stronger than steel or materials like Kevlar.
The kids these days are so spoiled. Silicon doping was discovered like when? And how long did it take to make a practical transistor?
Seriously through, it's not every new discovered phenomena owes you something.
Lol I'm obviously joking, I'm probably younger than both OP and 70% of people out here. But my point that the nature doesn't owe us anything still stands. University press releases are really to blame for building up unrealistic expectations, but then you can't expect them to honestly tell you "we spend millions on things with zero practical applicability just because it's awesome".
It’s okay. Next year we will defeat and reverse aging with one simple trick so you can wait longer, at least according to the latest health science click bait.
I will not rest until I have you immortal, flying your fusion powered car, using augmented reality VR controls, to your very own immersive shopping experience with AI assistant android sexbots catering to your every whim and I will not REM enhanced super-sleep until that happens!
Most people, avoid phone calls if possible.
If I get a call and it’s an AI, I, like everybody else, is putting down.
If I’m picking up the phone to call a company, it’s because I can’t achieve what I want to on their website.
These AI phone calls are as or more limited than the website.
There is a use-case for voice AI - most of these demoes really miss the mark with “we’re going to replace your call center”.
If founders had any idea how much performance matters in a call center, and how hard it is to achieve, they’d focus on a use case better served by voice AI.