Notice how it goes from "we proved Fields-medal level stuff" to "promising leads" when we go to the world of biology. Still awesome, but speaks to the limitations of pure thinking in a more applied subject. No automatic cancer cures today.
I am surprised they end on a gene mutation already known to be involved in a rare disease and a vague hypothesis about the mechanism. Among the many things DCTN1 does... it also directs autophagosomes toward lysosomes for waste degradation. Hm, disruptions in waste degradation.. build up of unwanted proteins and waste in cells... yeah that seems like it would cause neurodegeneration as well! Point being, there are dozens of things, maybe hundreds. Is cholesterol particularly compelling here? Not really. PhD students come up with similar hypotheses every hour. Hypotheses are cheap. I'm sure it can and will do better, but for now... I feel they might be underprepared for curing all disease in 10 years.
The last section of the press release was definitely written by AI. I'm talking about the section that starts with "Finding the next experiment worth doing" all the way to the end.
Ai labs look to be desperate to try and stave off the cooling hype that looks to be snowballing in the industry right now. These next few months are going to be crucial to their potential survival and future.
Its not surprising to me that this press release looks half-baked.
After AI more or less “solved” large parts of mathematics overnight and literally broke the internet by uncovering decades-old PHP vulnerabilities, you still think this is a hype that’s cooling off?
To me, this mostly shows how much our baseline for what counts as “noteworthy” has shifted since ChatGPT came out. I’ve seen more disruptive events in the last couple of years than in the 30 before that.
What would it take to get you excited again? AI discovering that aliens exist?
Isn’t that the point though - they’ve generated such strong results in mathematics, so why now are they delivering something so weak here?
Hype rather than realistic attempts to solve things is actually a valid explanation for that. They want to give the impression it’s a solve-everything machine even if it’s not well suited to areas outside of CS and math (or really anything where the logic of what is being done is not 1:1 with syntax, which is where LLMs are clearly excelling)
The staggering losses anthropic is facing are mostly due to their valuation increasing to just shy of 1T at the beginning of this year. That valuation increase caused the value of what they owe on future stock to triple which is shown as a massive loss on paper but doesn’t represent actual cash leaving their hands. The rules are being rewritten by frontier companies whose revenue growth and proposed spending are outpacing any thing we have seen in the past. Anthropic has committed to spending over 500B over the next 10 years on cloud infrastructure which might be what you are quoting but many project their revenue growth to outpace the 50B a year in spending so that may not even equate to any loss but will certainly be less than 50B/yr. The whole thing is bonkers but and it’s easy to get overwhelmed by the breathtaking numbers on their balance sheet.
If they would be profitable without further spending on R&D they would stop spending, but as you say they are in an R&D race so they can't stop and thus the hypothetical is irrelevant.
Paper loss. It's a sort of debt they have on bonds that is tied to their valuation. Because their valuation has grown significantly, so has their debt. It's standard accounting process to put that debt as a "loss", even though they're not paying anything.
Think of it this way. It's as if a public company gives you a certain amount in RSUs that won't vest for a while. A year later, the company is worth 10x more. So they list the increased value of the RSUs they owe you as a loss.
In reality, the fact that this "debt" has ballooned so much is a positive sign - the valuation is high (it's a separate issue whether that valuation is realistic).
That must be hard to say with a straight face. Over the last 4-6 months I think very few corporate developers have handwritten any code at all and the “intelligence” growth from one model to the next is very tangible.
Odd. I barely perceive the growth, and I use them a ton at work. Not saying the advancements aren’t there, I just wouldn’t describe every model release as having tangible growth.
Also the handwritten code doesn’t remove the work. If anything, it makes the thinking more difficult (for me) because instead of thinking during the coding, I have to before and after (in iterations).
For big projects I actually go back to tradcoding some to make sure I understand the system, since I’m the one responsible.
I actually dont let llms code anything non trivial, I will have it help with the planning a bit usually, but I write most of the code, and wow turns out my brain doesnt rot in the process and I actually understand whats going on.
Im convinced the people letting llms do their jobs are going to get replaced, not by agents but people who use them more reasonably.
This is one of the laziest and nonsensical press releases I have ever seen attached to a scientific paper. Everything in the press release is either misrepresenting what is in the linked paper or is completely obvious and uninteresting.
Evidently if you fan-out data analysis work across 1946 agents instead of just one, the analysis is faster and more thorough than if you instructed a single agent to go through all of the 1946 genes sequentially. And that, in turn, is faster than if you did it yourself. Like, duh.
The linked paper is not exactly transformative, but the impression it leaves is quite a bit better than the nonsense press release.
The problem was never generating leads. The bottleneck is validated results. It’s performing clinical trials proving efficacy. And this is where I think AI will have the biggest impact—-helping scientist, navigate study complexity and regulatory burden
Drug design is hard because it’s a system problem not a molecule-protein interaction problem.
At one point the gold standard for generating leads was using combinatorial chemistry to experimentally screen through millions of compounds.
I’m not sure how wide-spread that is any more; it used a lot of reagents. And it ran into the real bottleneck: lead generation tells you little to nothing about the efficacy, toxicity, and side effects of a drug or biologic in actual humans.
In silicon will never do a better job at lead generation than combinatorial chemistry. It could help with the real bottleneck. But… there just isn’t a lot of high quality training data on this.
They’ve been pushing ML approaches to drug design since at least the early 2010s. The approach has evolved with the trends in ML. Overall, I think the results have been very mixed; much like the traditional pharma pipeline.
> At one point the gold standard for generating leads was using combinatorial chemistry to experimentally screen through millions of compounds.
I don't think this was ever the gold standard. I mean, some companies did employ these approaches, but they were generally viewed as low-quality, brute-force approaches (at least at the handful of companies I was associated with).
That is not true. For many diseases we lack good hypotheses about the underlying causal mechanisms which resulted in decades and billions being pumped into few questionable targets (e.g. amyloid and alzheimers). In other diseases such as many cancers we often lack "actionable" targets, resulting in hundreds me-too companies all chasing the same target with only subtle variations in approach (inefficient capital alocation).
So AI could help a lot in discovery, much more than the engineering like uses in antibody/drug optimization.
"The problem was never generating leads" is an absurd statement without any reference to the quality of those leads. Ideally you want a system able to generate one more lead, the correct one.
I hope OpenAI will prove its usefulness in stealing joy from mathematicians, engineers, and biologists this time. It feels like there's a chance of warmer feedback this time from everyone out there keeping an eye on what they do.
Personally I would forgive all of the joy being stolen by AI if they discover something fundamental and outstanding that would help all of us.
> stealing joy from mathematicians, engineers, and biologists
This is the weakest possible rationalization against the AI takeover. Solving real world problems isn’t “stealing joy”. Those problems shouldn’t be gatekept. It paints our scientists as rent seekers.
This comment totally misunderstands why scientists get into science. Let me tell you, it isn't the money. It is for the joy of exploring the frontier. LLM's are taking this from us. Maybe they will be better than us but I'm not sure how many scientists will get into science for the joy of being an understudy to an LLM.
If you could press a button and obliterate the joy experienced by every professional at their work, in exchange for a 1% boost in marginal productivity for the next 2 quarters, would you do it?
More to the point, what if the button gives a 10% quarterly boost in productivity for the next 100 quarters. But 80% of the professionals get fired and the other 20% have harder jobs that feel more managerial. Then, yeah I would press it.
I understood them to be arguing that AI models are taking away the joy of certain things like mathematics but not producing commensurate value for humanity. We have a lower bound on twin primes, but that doesn't improve life for anybody, and the cost of getting the bound was removing human involvement from a lot of mathematical research, which was a source of joy for some portion of the human race. If all of this leads to curing major diseases without an AI takeover, it will have been worth it. But if it doesn't, then the ends don't necessarily justify the means.
> Solving real world problems isn’t “stealing joy”
Well, pretty good amount of the training data was stolen. Not only from the repositories without a license, but also archives of books, and they even steal training from each other. Funny enough, they still blame each other for stealing. I don't mind if you think your joy is not stolen. But I mind if you insist when your opinion is the only right one and joy has definitely not been stolen. It's quite sensitive territory starts here, and I don't think anyone (incl.yourself) has a right to speak for everyone in that regard.
I keep hearing these analogies to "rent seekers" - particularly with mathematics.
I just don't understand it. No mathematician would be upset if a total amateur solves a centuries old problem - even one they've worked their whole life on. They cheer and celebrate it.
“AI shouldn’t make groundbreaking advancements because the scientists who otherwise wanted to make those advancements feel sad”.
All those scientists can go pound sand as far as I am concerned. I don’t give a shit about “joy” I care about curing diseases and solving the universe’s mysteries. If AI can get us there faster then I’m 100% for it.
Clinical science is inundated with well-reasoned "potential leads". Ideas for targets, and even designing drugs to interact with those targets in the desired way, is not the bottleneck. Hasn't been for decades. The bottleneck is in clinical development i.e. human trials.
There are many many AI drug discovery companies out there, many of them very well funded, and ALL of them with no shortage of "potential leads".
These companies usually start out with the idea that their potential leads are better than the alternatives (generally because they cast a much bigger net/ considered more screening criteria than a human could, thanks to AI) and that big pharma will pay them license fees.
And yet, all of those companies, pretty much without exception, have turned into drug development companies themselves. They are forced to develop their own "potential leads" into actual approved drugs. And they are subject to the same timelines, same costs and - so far - the same probabilities of success as any other drug developer.
PerturbAI & OpenAI's "potential leads" might be better or worse than the next AI company. I would guess worse, given there is zero proprietary data involved and no clarity about why the underlying models are going to generate insightful findings. Either way, not useful.
I just saw a similar post from Anthropic which also landed like a thud. The deep need of OpenAI/Anthropic to churn out flashy announcements today to with upcoming IPO is in serious conflict with the realistic pace of biological or medical research, let alone successful clinical applications. They would produce many cool leads like this which ultimately turn out to be useless before landing on the one that makes it to human clinical trials (and may fail there too).
A previous blog post from Anthropic used the mention of "CRISPR-like" to activate a mini hype cycle by association with a well-known groundbreaking discovery. Without that kind of manipulative hook these announcements are pretty thin gruel and below the bar of thousands of Pubmed papers that never made it into public view.
Well thats the trick. OpenAI publishes things like this, making you feel like you need to do more, you frantically increase your token usage.
You dont need too, I know like 2 people out of 30 devs I know closely who legitmately accomplish anything behaving this way and they were superhuman before AI as well, it probably has little to do with just having access to lots of agents.
OpenAI in sciences: "We have bitten off a bit of each of these apples - you guys finish eating them, because that is what you are best at - finishing half-eaten food. May we have a $2T valuation, please?"
Why waste so much space on a chart on whether "human experts preferred distributed-agent reports over single-agent reports"? I thought we were supposed to be curing alzheimer's and cancer!
I am sorry, but if you still have stupid humans these leads will end up going the same place all the leads go. Nowhere.
So what if DCTN1 is linked to Alzheimer's? All they will do is invent a drug to mess with the enzyme and it might only still help a small percentage of people and no one will be able to afford it.
It is well know that diet plays a large role in Alzheimer's[1] and is largely an issue of personal genetics and environment.
I use LLMs every day but damn... can we please market them and value them as a coding tool and not the savior/destroyer of the world. "new leads" comes off flat after the previous tone
Notice how it goes from "we proved Fields-medal level stuff" to "promising leads" when we go to the world of biology. Still awesome, but speaks to the limitations of pure thinking in a more applied subject. No automatic cancer cures today.
I am surprised they end on a gene mutation already known to be involved in a rare disease and a vague hypothesis about the mechanism. Among the many things DCTN1 does... it also directs autophagosomes toward lysosomes for waste degradation. Hm, disruptions in waste degradation.. build up of unwanted proteins and waste in cells... yeah that seems like it would cause neurodegeneration as well! Point being, there are dozens of things, maybe hundreds. Is cholesterol particularly compelling here? Not really. PhD students come up with similar hypotheses every hour. Hypotheses are cheap. I'm sure it can and will do better, but for now... I feel they might be underprepared for curing all disease in 10 years.
The last section of the press release was definitely written by AI. I'm talking about the section that starts with "Finding the next experiment worth doing" all the way to the end.
Ai labs look to be desperate to try and stave off the cooling hype that looks to be snowballing in the industry right now. These next few months are going to be crucial to their potential survival and future.
Its not surprising to me that this press release looks half-baked.
After AI more or less “solved” large parts of mathematics overnight and literally broke the internet by uncovering decades-old PHP vulnerabilities, you still think this is a hype that’s cooling off?
To me, this mostly shows how much our baseline for what counts as “noteworthy” has shifted since ChatGPT came out. I’ve seen more disruptive events in the last couple of years than in the 30 before that.
What would it take to get you excited again? AI discovering that aliens exist?
> AI discovering that aliens exist?
I wonder if this would turn even the most stalwart opponents around.
I could see discussion about such an announcement focusing on exactly what qualifies as an "alien."
Isn’t that the point though - they’ve generated such strong results in mathematics, so why now are they delivering something so weak here?
Hype rather than realistic attempts to solve things is actually a valid explanation for that. They want to give the impression it’s a solve-everything machine even if it’s not well suited to areas outside of CS and math (or really anything where the logic of what is being done is not 1:1 with syntax, which is where LLMs are clearly excelling)
How is the hype cooling? Opus 5.5 is doing most of my job
Hype and functionality are only loosely correlated.
I assume your still verifying that opus is correct?
In which case congratulations you’ve just been promoted to manager with no pay increase!
Joking aside Ai is doing a large chunk of my job but all it’s done is shifted what I actually spend time on.
It’s not really saved any man hours.
That's not hype - that's the new norm. It's not novelty anymore.
and the company serving it is losing 50B every half year lol
The staggering losses anthropic is facing are mostly due to their valuation increasing to just shy of 1T at the beginning of this year. That valuation increase caused the value of what they owe on future stock to triple which is shown as a massive loss on paper but doesn’t represent actual cash leaving their hands. The rules are being rewritten by frontier companies whose revenue growth and proposed spending are outpacing any thing we have seen in the past. Anthropic has committed to spending over 500B over the next 10 years on cloud infrastructure which might be what you are quoting but many project their revenue growth to outpace the 50B a year in spending so that may not even equate to any loss but will certainly be less than 50B/yr. The whole thing is bonkers but and it’s easy to get overwhelmed by the breathtaking numbers on their balance sheet.
If that company stopped training new models and just continued serving Opus 5.5 they'd be profitable. Being in an R&D race isn't cheap
For how long? If OpenAI or Google (or Chinese companies) decide to keep losing money longer than them they'll pass them pretty soon.
The AI race is going to be won by whoever has enough money to burn to outlast competitors and I'm increasingly skeptical that's going to be Anthropic
If they would be profitable without further spending on R&D they would stop spending, but as you say they are in an R&D race so they can't stop and thus the hypothetical is irrelevant.
Because they are growing aggressively.
Paper loss. It's a sort of debt they have on bonds that is tied to their valuation. Because their valuation has grown significantly, so has their debt. It's standard accounting process to put that debt as a "loss", even though they're not paying anything.
Think of it this way. It's as if a public company gives you a certain amount in RSUs that won't vest for a while. A year later, the company is worth 10x more. So they list the increased value of the RSUs they owe you as a loss.
In reality, the fact that this "debt" has ballooned so much is a positive sign - the valuation is high (it's a separate issue whether that valuation is realistic).
Their "real" losses are about $8B.
Because you have a simple job.
Would love to know what you do that’s more complex than say mathematics research
Not stuffing every part of a number space into a sequence?
Math and Computer Science have low costs to verify answers. Biology is high due to reagents and instrumentation costs.
That must be hard to say with a straight face. Over the last 4-6 months I think very few corporate developers have handwritten any code at all and the “intelligence” growth from one model to the next is very tangible.
Odd. I barely perceive the growth, and I use them a ton at work. Not saying the advancements aren’t there, I just wouldn’t describe every model release as having tangible growth.
Also the handwritten code doesn’t remove the work. If anything, it makes the thinking more difficult (for me) because instead of thinking during the coding, I have to before and after (in iterations).
For big projects I actually go back to tradcoding some to make sure I understand the system, since I’m the one responsible.
Same. People are drunk on "agentic" and reality is coming for them. It's also coming for the holdouts who say it has no effect.
Like a lot of tech before it I think the overriding theme is misapplication during a hype cycle.
I actually dont let llms code anything non trivial, I will have it help with the planning a bit usually, but I write most of the code, and wow turns out my brain doesnt rot in the process and I actually understand whats going on.
Im convinced the people letting llms do their jobs are going to get replaced, not by agents but people who use them more reasonably.
That’s true. But that’s a really small slice of the pie they’ve cut for themselves in hype and valuation.
This is one of the laziest and nonsensical press releases I have ever seen attached to a scientific paper. Everything in the press release is either misrepresenting what is in the linked paper or is completely obvious and uninteresting.
Evidently if you fan-out data analysis work across 1946 agents instead of just one, the analysis is faster and more thorough than if you instructed a single agent to go through all of the 1946 genes sequentially. And that, in turn, is faster than if you did it yourself. Like, duh.
The linked paper is not exactly transformative, but the impression it leaves is quite a bit better than the nonsense press release.
The problem was never generating leads. The bottleneck is validated results. It’s performing clinical trials proving efficacy. And this is where I think AI will have the biggest impact—-helping scientist, navigate study complexity and regulatory burden
If you can increase the fidelity of your research model (in-silico, murine, etc) to real life humans then there are fewer false leads.
Drug design is hard because it’s a system problem not a molecule-protein interaction problem.
At one point the gold standard for generating leads was using combinatorial chemistry to experimentally screen through millions of compounds.
I’m not sure how wide-spread that is any more; it used a lot of reagents. And it ran into the real bottleneck: lead generation tells you little to nothing about the efficacy, toxicity, and side effects of a drug or biologic in actual humans.
In silicon will never do a better job at lead generation than combinatorial chemistry. It could help with the real bottleneck. But… there just isn’t a lot of high quality training data on this.
They’ve been pushing ML approaches to drug design since at least the early 2010s. The approach has evolved with the trends in ML. Overall, I think the results have been very mixed; much like the traditional pharma pipeline.
> At one point the gold standard for generating leads was using combinatorial chemistry to experimentally screen through millions of compounds.
I don't think this was ever the gold standard. I mean, some companies did employ these approaches, but they were generally viewed as low-quality, brute-force approaches (at least at the handful of companies I was associated with).
That is not true. For many diseases we lack good hypotheses about the underlying causal mechanisms which resulted in decades and billions being pumped into few questionable targets (e.g. amyloid and alzheimers). In other diseases such as many cancers we often lack "actionable" targets, resulting in hundreds me-too companies all chasing the same target with only subtle variations in approach (inefficient capital alocation).
So AI could help a lot in discovery, much more than the engineering like uses in antibody/drug optimization.
The real problem is generating leads that are more likely to yield positive results than throwing darts at hypotheses.
That's what top-tier scientists are good at.
"The problem was never generating leads" is an absurd statement without any reference to the quality of those leads. Ideally you want a system able to generate one more lead, the correct one.
I hope OpenAI will prove its usefulness in stealing joy from mathematicians, engineers, and biologists this time. It feels like there's a chance of warmer feedback this time from everyone out there keeping an eye on what they do.
Personally I would forgive all of the joy being stolen by AI if they discover something fundamental and outstanding that would help all of us.
> stealing joy from mathematicians, engineers, and biologists
This is the weakest possible rationalization against the AI takeover. Solving real world problems isn’t “stealing joy”. Those problems shouldn’t be gatekept. It paints our scientists as rent seekers.
This comment totally misunderstands why scientists get into science. Let me tell you, it isn't the money. It is for the joy of exploring the frontier. LLM's are taking this from us. Maybe they will be better than us but I'm not sure how many scientists will get into science for the joy of being an understudy to an LLM.
It doesn’t misunderstand, it just treats that as irrelevant, which it is.
If you could press a button and obliterate the joy experienced by every professional at their work, in exchange for a 1% boost in marginal productivity for the next 2 quarters, would you do it?
More to the point, what if the button gives a 10% quarterly boost in productivity for the next 100 quarters. But 80% of the professionals get fired and the other 20% have harder jobs that feel more managerial. Then, yeah I would press it.
I understood them to be arguing that AI models are taking away the joy of certain things like mathematics but not producing commensurate value for humanity. We have a lower bound on twin primes, but that doesn't improve life for anybody, and the cost of getting the bound was removing human involvement from a lot of mathematical research, which was a source of joy for some portion of the human race. If all of this leads to curing major diseases without an AI takeover, it will have been worth it. But if it doesn't, then the ends don't necessarily justify the means.
> Solving real world problems isn’t “stealing joy”
Well, pretty good amount of the training data was stolen. Not only from the repositories without a license, but also archives of books, and they even steal training from each other. Funny enough, they still blame each other for stealing. I don't mind if you think your joy is not stolen. But I mind if you insist when your opinion is the only right one and joy has definitely not been stolen. It's quite sensitive territory starts here, and I don't think anyone (incl.yourself) has a right to speak for everyone in that regard.
I keep hearing these analogies to "rent seekers" - particularly with mathematics.
I just don't understand it. No mathematician would be upset if a total amateur solves a centuries old problem - even one they've worked their whole life on. They cheer and celebrate it.
Nor do they oppose the spread of math knowledge.
Nor do they have high salaries. Nurses make more.
Where is the "rent seeking"?
“AI shouldn’t make groundbreaking advancements because the scientists who otherwise wanted to make those advancements feel sad”.
All those scientists can go pound sand as far as I am concerned. I don’t give a shit about “joy” I care about curing diseases and solving the universe’s mysteries. If AI can get us there faster then I’m 100% for it.
Until there are no more scientists...
> I don’t give a shit about “joy”
Exactly. Fuck these selfish pieces of shit for delaying me getting administered heroin in a cube until the heat death of the universe.
> I don’t give a shit about “joy”
> solving the universe’s mysteries.
What do you get out of solving the universe's mysteries then? Ennui?
This is very, very weak.
Clinical science is inundated with well-reasoned "potential leads". Ideas for targets, and even designing drugs to interact with those targets in the desired way, is not the bottleneck. Hasn't been for decades. The bottleneck is in clinical development i.e. human trials.
There are many many AI drug discovery companies out there, many of them very well funded, and ALL of them with no shortage of "potential leads".
These companies usually start out with the idea that their potential leads are better than the alternatives (generally because they cast a much bigger net/ considered more screening criteria than a human could, thanks to AI) and that big pharma will pay them license fees.
And yet, all of those companies, pretty much without exception, have turned into drug development companies themselves. They are forced to develop their own "potential leads" into actual approved drugs. And they are subject to the same timelines, same costs and - so far - the same probabilities of success as any other drug developer.
PerturbAI & OpenAI's "potential leads" might be better or worse than the next AI company. I would guess worse, given there is zero proprietary data involved and no clarity about why the underlying models are going to generate insightful findings. Either way, not useful.
I just saw a similar post from Anthropic which also landed like a thud. The deep need of OpenAI/Anthropic to churn out flashy announcements today to with upcoming IPO is in serious conflict with the realistic pace of biological or medical research, let alone successful clinical applications. They would produce many cool leads like this which ultimately turn out to be useless before landing on the one that makes it to human clinical trials (and may fail there too).
A previous blog post from Anthropic used the mention of "CRISPR-like" to activate a mini hype cycle by association with a well-known groundbreaking discovery. Without that kind of manipulative hook these announcements are pretty thin gruel and below the bar of thousands of Pubmed papers that never made it into public view.
So they did a perturb-seq experiment and then launched one agent per gene in the library?
Seems quite inefficient to me. Unless the goal is to spend millions in API credits.
And here is me just trying to keep ~10 agents busy....
Well thats the trick. OpenAI publishes things like this, making you feel like you need to do more, you frantically increase your token usage.
You dont need too, I know like 2 people out of 30 devs I know closely who legitmately accomplish anything behaving this way and they were superhuman before AI as well, it probably has little to do with just having access to lots of agents.
OpenAI in sciences: "We have bitten off a bit of each of these apples - you guys finish eating them, because that is what you are best at - finishing half-eaten food. May we have a $2T valuation, please?"
The valuation bubble pops if the market comes to believe frontier AI will not disrupt other industries at the rate that it did software engineering.
Why waste so much space on a chart on whether "human experts preferred distributed-agent reports over single-agent reports"? I thought we were supposed to be curing alzheimer's and cancer!
I am sorry, but if you still have stupid humans these leads will end up going the same place all the leads go. Nowhere.
So what if DCTN1 is linked to Alzheimer's? All they will do is invent a drug to mess with the enzyme and it might only still help a small percentage of people and no one will be able to afford it.
It is well know that diet plays a large role in Alzheimer's[1] and is largely an issue of personal genetics and environment.
[1] https://news.harvard.edu/gazette/story/2024/07/alzheimers-st...
If anyone cares to comment why I am wrong here I would appreciate it.
More PR bs from unscrupulous people. Until you've cured an actual disease for actual people, please shut up.
"new leads"
I use LLMs every day but damn... can we please market them and value them as a coding tool and not the savior/destroyer of the world. "new leads" comes off flat after the previous tone