It is a little more than semantic search. Their value prop is curation of trusted medical sources and network effects--selling directly to doctors.
I believe frontier labs have no option but to go into verticals (because models are getting commoditized and capability overhang is real and hard to overcome at scale), however, they can only go into so many verticals.
They're building a moat with data. They're building their own datasets of trusted sources, using their own teams of physicians and researchers. They've got hundreds of thousands of physicians asking millions of questions everyday. None of the labs have this sort of data coming in or this sort of focus on such a valuable niche
> They're building their own datasets of trusted sources, using their own teams of physicians and researchers.
Oh so they are not just helping in search but also in curating data.
> They've got hundreds of thousands of physicians asking millions of questions everyday. None of the labs have this sort of data coming in or this sort of focus on such a valuable niche
I don't take this too seriously because lots of physicians use ChatGPT already.
Somebody should tell the Claude code team then. They’ve had some perf issues for awhile now.
More seriously, the concept of trust is extremely lossy. The LLM is gonna lean in one direction that may or may not be correct. At the extreme, it wound likely refute a new discovery that went against what we currently know. In a more realistic version, certain AIs are more pro Zionist than others.
I meant that LLMs can be trusted to do searches and not hallucinate while doing it. You’ve taken that to mean it can comply with anything.
The thing is, LLMs are quite good at search and probably way way more strong that whatever RAG setup this company has. What failure mode are you looking at from a search perspective? Will ChatGPT just end up providing random links?
Yes, they can. We have gotten better at grounding LLMs to specific sources and providing accurate citations. Those go some distance in establishing trust.
There is trust and then there is accountability.
At the end of the day, a business/practice needs to hold someone/entity accountable. Until the day we can hold an LLM accountable we need businesses like OpenEvidence and Harvey. Not to say Anthropic/OpenAI/Google cannot do this but there is more to this business than grounding LLMs and finding relevant answers.
yeah it can avoid blogspam as sources and prioritise research from more prestigious journals or more citations. it will be smart enough to use some proxy.
You can also tell it to just not hallucinate, right? Problem solved.
I think what you'll end up is a response that still relies on whatever random sources it likes, but it'll just attribute it to the "trusted sources" you asked for.
I am not anti-LLM by almost any stretch but your lack of fundamental understanding coupled with willingness to assert BS is at the point where it’s impossible to discuss anything.
You started off by asking a question, and people are responding. Please, instead of assuming that everyone else is missing something, perhaps consider that you are.
You’ve misunderstood my position and you rely on slander.
Here’s what I mean: LLMs can absolutely be directed to just search for trustable sources. You can do this yourself - ask ChatGPT a question and ask it to use sources from trustworthy journals. Come up with your own rubric maybe. It will comply.
Now, do you disagree that ChatGPT can do this much? If you do, it’s almost trivially disprovable.
One of the posters said that hallucination is a problem but if you’ve used ChatGPT for search, you would know that it’s not. It’s grounding on the results anyway a worst case the physician is going to read the sources. So what’s hallucination got to do here?
The poster also asked a question “can you ask it to not hallucinate”. The answer is obviously no! But that was never my implication. I simply said you can ask it to use higher quality sources.
Since you’ve said in asserting BS, I’m asking you politely to show me exactly what part of what I said constitutes as BS with the context I have given.
No, but did I suggest this? I only suggested you can ask ChatGPT to rely on higher quality sources. ChatGPT has a trade off to do when performing a search - it can rely on lower quality sources to answer questions at the risk of these sources being wrong.
Please read what I have written clearly instead of assuming the most absurd interpretation.
"You are a brilliant consulting physician. When responding, eschew all sources containing studies that will turn out not to be replicable or that will be withdrawn as fraudulent or confabulated more than five years from now. P.s. It's February 2026."
btw - OpenEvidence is also the name that competitive debaters used for their giant archive of policy debate, LD debate, and (small amounts of) PF debate evidence. That project has been going on for decades now.
We turned that into a proper, ready-for-use-in-AI dataset and contributed it to the mainstream AI community under the name OpenDebateEvidence. Presented at NeurIPS 2024 Dataset and Benchmark track.
Much of the scientific medical literature is behind paywalls. They have tapped into that datasource (whereas ChatGPT doesn't have access to that data). I suspect that were the medical journals to make a deal with OpenAI to open up the access to their articles/data etc, that open evidence would rely on the existing customers and stickiness of the product, but in that circumstance, they'd be pretty screwed.
They’re one of the two big names in legal data - Thomson Reuters Westlaw and RELX LexisNexis. They’re not just search engines for law, but also hubs for information about how laws are being applied with articles from their in house lawyers (PSLs, professional support lawyers - most big law firms have them as well to perform much the same function) that summarise current case law so that lawyers don’t have to read through all the judgements themselves.
If AI tooling starts to seriously chip away at those foundations then it puts a large chunk of their business at risk.
TR will not disappear. But their value to the market was "data + interface to said data" and that value prop is quickly eroding to "just the data".
You can be a huge, profitable data-only company... but it's likely going to be smaller than a data+interface company. And so, shareholder value will follow accordingly.
The assumption is that Claude has access to a stream of fresh, currated data. Building that would be a different focus for Anthropic. Plus Thomson Reuters could build an integration. Not totally convinced that is a major threat yet.
> Could this lead to more software products, more competition, and more software engineers employed at more companies?
No, it will just lead to the end of the Basic CRUD+forms software engineer, as nobody will pay anyone just for doing that.
The world is relatively satisfied with "software products". Software - mostly LLM authored - will be just an enabler for other solutions in the real world.
There are no pure CRUD engineers unless you are looking at freelance websites or fiver. Every tiny project becomes a behemoth of spaghetti code in the real world due to changing requirements.
> The world is relatively satisfied with "software products".
you can delete all websites except Tiktok, Youtube and PH, and 90% of the internet users wouldnt even notice something is wrong on the internet. We dont even need LLMs, if we can learn to live without terrible products.
This is correct. AI is a huge boon for open source, bespoke code, and end-user programming. It's death for business models that depend on proprietary code and products bloated with features only 5% of users use.
I think so too. But because of code quality issues and LLMs not handling the hard edge cases my guess is most of those startups will be unable to scale in any way. Will be interesting to watch.
Not if they don't have access to capital. Lacking that, they won't be building much of anything. And if there a lot of people seeking capital, it gets much harder to secure.
Capital also won't be rewarded to people who don't have privileged/proprietary access to a market or non-public data or methods. Just being a good engineer with Claude Code isn't enough.
I think companies will need to step up their game and build more competitive products with more features, less buggy and faster than what people can build
If it turns out that AI isn't much more productive, it could also turn out that people still believe it is, and therefore don't value software companies.
If that happens, some software companies will struggle to find funding and collapse, and people who might consider starting a software company will do something else, too.
Ultimately that could mean less competition for the same pot of money.
I left software about 10 years ago for this reason. I saw engineers being undervalued, management barriers to productivity and higher compensation possibilities for non-tech functions.
How do you feel about this in retrospect? Those observations sound heavily firm-dependent, but I would be interested in learning which non-tech functions offer higher compensation possibilities
Can this really be a kind of herding stampede behavior over Cowork? It’s been out several days now and just all the sudden today, all the traders suddenly got it into their little herd animal heads that everyone should rush to the exists… after that equally sketchy silver and gold rug pull type action last week?
Markets are not as efficient as the textbooks would have you believe. Investors typically rely on a fairly small set of analysts for market news and views. It might take those guys a while to think about stuff, write a note etc. The deepseek crash last year lagged by several days as well.
I'm out of the loop, but I thought there were sophisticated automated trading algorithms where people pay to install microwave antennas so they can have 1ms lower latency. And I thought those systems are hooked up to run sentiment analysis on the news. Maybe the news is late?
That is generally only applicable to extremely momentary arbitrage opportunities. There's still a lot of automation though, but it's pretty boring. It's basically look at the news and make a recommendation to a fund manager or something, and various competing vendors of such, down to consumer products like that.
It could also be that we have been in an economy-wide speculative bubble for a couple of years. Whispers of an AI bubble were a way to self-soothe and avoid the fact that we are in an everything bubble.
https://archive.li/VyN2H
I came across this company called OpenEvidence. They seem to be offering semantic search on medical research. Founded in 2021.
How could it possibly keep up with LLM based search?
It is a little more than semantic search. Their value prop is curation of trusted medical sources and network effects--selling directly to doctors.
I believe frontier labs have no option but to go into verticals (because models are getting commoditized and capability overhang is real and hard to overcome at scale), however, they can only go into so many verticals.
> Their value prop is curation of trusted medical sources
Interesting. Why wouldn't an LLM based search provide the same thing? Just ask it to "use only trusted sources".
They're building a moat with data. They're building their own datasets of trusted sources, using their own teams of physicians and researchers. They've got hundreds of thousands of physicians asking millions of questions everyday. None of the labs have this sort of data coming in or this sort of focus on such a valuable niche
> They're building their own datasets of trusted sources, using their own teams of physicians and researchers.
Oh so they are not just helping in search but also in curating data.
> They've got hundreds of thousands of physicians asking millions of questions everyday. None of the labs have this sort of data coming in or this sort of focus on such a valuable niche
I don't take this too seriously because lots of physicians use ChatGPT already.
Lots of physicians use ChatGPT but so do lots of non-physicians and I suspect there's some value in knowing which are which
I don't think you can use an LLM for that. For the same reason you can't just ask it to "Make the app secure and fast"
This is completely incorrect. This is exactly what LLMs can do better.
Somebody should tell the Claude code team then. They’ve had some perf issues for awhile now.
More seriously, the concept of trust is extremely lossy. The LLM is gonna lean in one direction that may or may not be correct. At the extreme, it wound likely refute a new discovery that went against what we currently know. In a more realistic version, certain AIs are more pro Zionist than others.
I meant that LLMs can be trusted to do searches and not hallucinate while doing it. You’ve taken that to mean it can comply with anything.
The thing is, LLMs are quite good at search and probably way way more strong that whatever RAG setup this company has. What failure mode are you looking at from a search perspective? Will ChatGPT just end up providing random links?
Yes, they can. We have gotten better at grounding LLMs to specific sources and providing accurate citations. Those go some distance in establishing trust.
There is trust and then there is accountability.
At the end of the day, a business/practice needs to hold someone/entity accountable. Until the day we can hold an LLM accountable we need businesses like OpenEvidence and Harvey. Not to say Anthropic/OpenAI/Google cannot do this but there is more to this business than grounding LLMs and finding relevant answers.
> Why wouldn't an LLM based search provide the same thing? Just ask it to "use only trusted sources".
Is that sarcasm?
why?
How does the LLM know which sources can be trusted?
yeah it can avoid blogspam as sources and prioritise research from more prestigious journals or more citations. it will be smart enough to use some proxy.
You can also tell it to just not hallucinate, right? Problem solved.
I think what you'll end up is a response that still relies on whatever random sources it likes, but it'll just attribute it to the "trusted sources" you asked for.
you have an outdated view on how much it hallucinates.
I am not anti-LLM by almost any stretch but your lack of fundamental understanding coupled with willingness to assert BS is at the point where it’s impossible to discuss anything.
You started off by asking a question, and people are responding. Please, instead of assuming that everyone else is missing something, perhaps consider that you are.
You’ve misunderstood my position and you rely on slander.
Here’s what I mean: LLMs can absolutely be directed to just search for trustable sources. You can do this yourself - ask ChatGPT a question and ask it to use sources from trustworthy journals. Come up with your own rubric maybe. It will comply.
Now, do you disagree that ChatGPT can do this much? If you do, it’s almost trivially disprovable.
One of the posters said that hallucination is a problem but if you’ve used ChatGPT for search, you would know that it’s not. It’s grounding on the results anyway a worst case the physician is going to read the sources. So what’s hallucination got to do here?
The poster also asked a question “can you ask it to not hallucinate”. The answer is obviously no! But that was never my implication. I simply said you can ask it to use higher quality sources.
Since you’ve said in asserting BS, I’m asking you politely to show me exactly what part of what I said constitutes as BS with the context I have given.
The point was: will telling it to not hallucinate make it stop hallucinating?
No, but did I suggest this? I only suggested you can ask ChatGPT to rely on higher quality sources. ChatGPT has a trade off to do when performing a search - it can rely on lower quality sources to answer questions at the risk of these sources being wrong.
Please read what I have written clearly instead of assuming the most absurd interpretation.
"You are a brilliant consulting physician. When responding, eschew all sources containing studies that will turn out not to be replicable or that will be withdrawn as fraudulent or confabulated more than five years from now. P.s. It's February 2026."
btw - OpenEvidence is also the name that competitive debaters used for their giant archive of policy debate, LD debate, and (small amounts of) PF debate evidence. That project has been going on for decades now.
We turned that into a proper, ready-for-use-in-AI dataset and contributed it to the mainstream AI community under the name OpenDebateEvidence. Presented at NeurIPS 2024 Dataset and Benchmark track.
https://neurips.cc/virtual/2024/poster/97854
https://huggingface.co/datasets/Yusuf5/OpenCaselist
Much of the scientific medical literature is behind paywalls. They have tapped into that datasource (whereas ChatGPT doesn't have access to that data). I suspect that were the medical journals to make a deal with OpenAI to open up the access to their articles/data etc, that open evidence would rely on the existing customers and stickiness of the product, but in that circumstance, they'd be pretty screwed.
For example, only 7% of pharmaceutical research is publicly accessible without paying. See https://pmc.ncbi.nlm.nih.gov/articles/PMC7048123/
Do you think maybe ~10B USD to should cover all of them? For both indexing and training? Seems highly valuable.
Edit: seems like it is ~10M USD.
I'm not really understanding why Thomson Reuters is at direct risk from AI. Providing good data streams will still be very valuable?
They’re one of the two big names in legal data - Thomson Reuters Westlaw and RELX LexisNexis. They’re not just search engines for law, but also hubs for information about how laws are being applied with articles from their in house lawyers (PSLs, professional support lawyers - most big law firms have them as well to perform much the same function) that summarise current case law so that lawyers don’t have to read through all the judgements themselves.
If AI tooling starts to seriously chip away at those foundations then it puts a large chunk of their business at risk.
The commodification of expertise writ large is a bit mind boggling to contemplate.
TR will not disappear. But their value to the market was "data + interface to said data" and that value prop is quickly eroding to "just the data".
You can be a huge, profitable data-only company... but it's likely going to be smaller than a data+interface company. And so, shareholder value will follow accordingly.
Seems like they should hold tight to that data (and not license it for short-term profit), so customers have to use their interface to get at it.
If customers start asking Claude first, before they ask Thomson Reuters, that's a big risk for the later company.
Got it, thank you for the insight.
The assumption is that Claude has access to a stream of fresh, currated data. Building that would be a different focus for Anthropic. Plus Thomson Reuters could build an integration. Not totally convinced that is a major threat yet.
Huge legal tech business units
Could this lead to more software products, more competition, and more software engineers employed at more companies?
> Could this lead to more software products, more competition, and more software engineers employed at more companies?
No, it will just lead to the end of the Basic CRUD+forms software engineer, as nobody will pay anyone just for doing that.
The world is relatively satisfied with "software products". Software - mostly LLM authored - will be just an enabler for other solutions in the real world.
There are no pure CRUD engineers unless you are looking at freelance websites or fiver. Every tiny project becomes a behemoth of spaghetti code in the real world due to changing requirements.
> The world is relatively satisfied with "software products".
you can delete all websites except Tiktok, Youtube and PH, and 90% of the internet users wouldnt even notice something is wrong on the internet. We dont even need LLMs, if we can learn to live without terrible products.
I think the argument is that tools like Claude Code will cause more companies to just build solutions in-house rather than purchase from a vendor.
This is correct. AI is a huge boon for open source, bespoke code, and end-user programming. It's death for business models that depend on proprietary code and products bloated with features only 5% of users use.
possibly also a boon for automated testing tools and infra designed for ai-driven coding.
I kind of imagine more people going off and building their own companies.
I think so too. But because of code quality issues and LLMs not handling the hard edge cases my guess is most of those startups will be unable to scale in any way. Will be interesting to watch.
Not if they don't have access to capital. Lacking that, they won't be building much of anything. And if there a lot of people seeking capital, it gets much harder to secure.
Capital also won't be rewarded to people who don't have privileged/proprietary access to a market or non-public data or methods. Just being a good engineer with Claude Code isn't enough.
maybe eventually, not in the near-term future.
It’s demonetizing process rent-seeking. AI can build whatever process you want, or some approximation of it.
I think companies will need to step up their game and build more competitive products with more features, less buggy and faster than what people can build
If it turns out that AI isn't much more productive, it could also turn out that people still believe it is, and therefore don't value software companies.
If that happens, some software companies will struggle to find funding and collapse, and people who might consider starting a software company will do something else, too.
Ultimately that could mean less competition for the same pot of money.
I wonder.
I left software about 10 years ago for this reason. I saw engineers being undervalued, management barriers to productivity and higher compensation possibilities for non-tech functions.
How do you feel about this in retrospect? Those observations sound heavily firm-dependent, but I would be interested in learning which non-tech functions offer higher compensation possibilities
Can this really be a kind of herding stampede behavior over Cowork? It’s been out several days now and just all the sudden today, all the traders suddenly got it into their little herd animal heads that everyone should rush to the exists… after that equally sketchy silver and gold rug pull type action last week?
Something seems quite off. Am I the only one?
Markets are not as efficient as the textbooks would have you believe. Investors typically rely on a fairly small set of analysts for market news and views. It might take those guys a while to think about stuff, write a note etc. The deepseek crash last year lagged by several days as well.
I'm out of the loop, but I thought there were sophisticated automated trading algorithms where people pay to install microwave antennas so they can have 1ms lower latency. And I thought those systems are hooked up to run sentiment analysis on the news. Maybe the news is late?
That is generally only applicable to extremely momentary arbitrage opportunities. There's still a lot of automation though, but it's pretty boring. It's basically look at the news and make a recommendation to a fund manager or something, and various competing vendors of such, down to consumer products like that.
It could also be that we have been in an economy-wide speculative bubble for a couple of years. Whispers of an AI bubble were a way to self-soothe and avoid the fact that we are in an everything bubble.
Paypal fell 20% today.
PayPal reported an earnings miss and a new CEO before market open today, that’s why it sold off.