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How AI Is Changing What Work Means

October 21, 20260 comments♥ 0
00:00Pretty much no one at this point needs to be convinced that AI is changing how work works.Certainly it changes the speed and efficiency with which we do existing tasks,and the magnitude and kind of those changes seems to grow over time.For example, while some of us may have used AI to write emails a couple of years ago,now many of us have personal agents sitting in our email perpetually,notifying us when there's something we actually have to respond to,often after having drafted the response itself.And yet, as I've always argued, that is just the tip of the iceberg.
00:28when it comes to how AI will actually change work.A great example of that comes in today's story,where a well-known financial research firmturned itself temporarily into a bio-research laband collaborated with Anthropicto come up with some potential answers to Scorpion Venom.More than a novelty and more than a clickbait headline,I actually think that this can tell us quite a bit
00:47about where AI at work is headed next.The AI Daily Brief is a daily podcast and videoabout the most important news and discussions in AI.All right, friends,announcements before we dive in. First of all, thank you to today's sponsors, KPMG, C data,granola, and Blitzy. To get an ad-free version of the show, go to patreon.com slash AI Daily Brief,or you can subscribe at Apple Podcasts. If you want to learn more about sponsoring the show,send us a note at Sponsors at Aideleaf.A.I.D.D.D.E.D.Brife. And by the way,
01:19if you are just looking for all the links that I talk over on any given day, there are two waysyou can do it. First, you can go into the episode companion page on the website for any episode.You're going to see everything split up by key numbers, key topics, key quotes.But you can also just subscribe to our newsletter, which is also linked from the website,which is going to have summaries of everything I discuss, as well as the key links.Check it all out at AIDailybrief.aI.But with that out of the way, let's dive in.We kick off today with an update on the personal assistant theme,
01:47where meta's Muse has reached 10 million U.S. downloads in just 40 days.That is a very quick pace.In fact, it makes Muse one of only about a dozen apps to have achieved that milestone,in a similar period of time. The other 11 are almost exclusively mobile games, specificallythe smash hits of mobile gaming like Pokemon Go, Fortnite, and PubG. Only two non-gaming appshave grown this quickly, which is Disney Plus and Metazone threads. Curiously, none of chat-chip-t,Clod, or Gemini grew that quickly as a mobile app, even though they eventually made it to the topof the download charts. Each chatbot was introduced as a web app first, with the mobile app
02:21coming later and missing the early phases of explosive growth. With its first month in the books,Censor Tower had several other interesting data points about the Muse rollout as well.Meta has allocated 39% of their total advertising spend for October to Muse, which is up from23% in September.That has boosted Muse to become the 21st largest brand by impressions for this month in the UnitedStates.Censor Tower data showed that Muse has a 30% retention rate after seven days and a 23% retentionrate after 14 days, which compares to chat GPT's 7 and 14-day retention rates at around 25%
02:52and the average retention rates for an iOS app at around 5% after 30 days.Interestingly, there has been some criticism around Muses' retention rate.However, that analysis compares Muse unfavorably against Instagram, Facebook, and the Googleapp, which are some of the stickiest consumer apps ever created.It also looks like Muse is still early in its growth phase, with daily active usersreaching 2.5 million last week, which was up 15% week over week.TLDR is, yes, they are spending a ton of advertising to push it, but meta does seem to have a hiton their hands. The next question isn't going to be about short-term retention, but about where people
03:25ultimately find the app fitting into their life if it does. Signal on X writes, most consumer productstypically have one of two types of retention. One is desire-driven, which is just pure dopamine,like Instagram, TikTok, and games. They often don't even need notifications because you knowthere is a reward for your desire always waiting. The second is dependency driven, like emails,calendars, even credit cards and banking. Most people have to do this. It's not something theynecessarily desire. The challenge with personal AI agents right now is that they occupy a weird thirdspace where they aren't emotionally rewarding most of the time to become entertainment like the firstcategory, and they're not reliable or indispensable enough to become pure infrastructure,
04:02i.e. they fail, and there's a lot of friction. Outside of pure research-driven tasks,even one failure is often enough to not come back for the utility value proposition.Now, he goes on to speculate a bunch about what opportunities that opens up, but I think even thoughwe're seeing early positive signals, it is worth remembering that we are still very early daysin terms of understanding the behavior surrounding personal agents.Muse also is not without its challenges.Earlier this week, researchers reportedthat the feature by which Muse allows users to share searches, conversations, and images
04:29had a particular challenge that were allowing other people to see those conversations.In general, those types of share features usually involve creating a unique URLthat anyone who has that URL can use to load a page hosting the content.The way that sensitive information is kept from being accessed is the complicatedness of the URLsthat theoretically can't easily be guessed. However, a particular design decision that Meta'sengineers made around the construction of those URLs has made them easy to discover. As a result,wrote Ars Technica, researchers from security firm Clareock said they were able to enumerate 6,800links muse users created for sharing conversations with each other. After opening a few hundred
05:05of them, they continue, the researchers found information that was clearly not intended to be publiclyavailable, including things like a small business owner's loan application, a real estate investor'sinternal performance report, and a national marketing company's internal revenue review.Meta has said that they've temporarily taken down the feature and are working to harden it tomake sure that doesn't happen.Next up, is another place that you're going to interact with agents?Your car?For almost a year, a basic version of the Gemini assistant has been available in Android Auto.
05:34The agent mostly functioned as a voice control for maps and a stereo system, but could alsosend text messages while you were driving and find information on the web. It was not, however,connected to your personal context, as users have begun to expect from a personal agent.User feedback also suggested that it didn't really work that well and often failed at basictasks. The new version adds the ability to access data from the rest of your Google-based services.Google says you can now do things like asking the assistant whether you're going to miss yourflight while en route, and the agent can rifle through your inbox to find the necessary detailswhile also checking traffic conditions in Google Maps. Another example was the ability to ask for
06:06directions based solely on a photo. Google said Gemini could now direct you to a coffee place yourfriend took you to last week, based only on a picture of you enjoying your espresso in a booth.Now, I'm still quite unsure about how important these features are ultimately going to be.On the one hand, I could see this type of interaction with your car, becoming something that isso common that we have a hard time imagining that we didn't always have those features.And yet at the same time, I can see a lot of people deciding that the setup and integration intoanother device isn't worth all the hassle when they can already just pick up their phonethat's probably already sitting right there on their console and get the same information.
06:37Whatever the case, it seems that companies are going to continue to explore that,as Tesla has also integrated Grockbot into their vehicles,and you have to imagine everyone else is going to follow.Moving over to markets,an updated financial disclosure from Anthropic has leaked,sparking another round of hand-wringing in the financial press.The Wall Street Journal got their hands on Anthropics' updated prospectus draft,which included fully audited financials for the first half of the year.
07:00Their reporting focused on two big, scary numbers,an $80 billion net loss for the first half of the year and up to $750 billion in compute spendingover the coming years. The $80 billion net loss is substantially larger than the $42 billion lossfor 2025 reported from the previous edition of their prospectus. But both numbers stemmed fromthe same accounting treatment of stock grants. Around $72 billion of the loss was an on-paper accountingchange, leaving $8.7 billion as the actual operating loss for the first half.Responding to the claim that this is the biggest loss of any company seeking to go public,Financial podcaster Jack Farley commented,
07:34This would be worrisome if the $80 billion accounting loss by Anthropic were economically real in any way.Good news, it isn't.When a company issues convertible notes and warrants and the market cap 1,000 X's,the on-paper loss from being short calls is massive, not fundamental.Now, for others, the bigger deal is the $750 billion in compute spending.The reporting suggested that of that, firm uncancellable commitments were around $450 billionand come due over the next seven to 10 years.In other words, it's a very high level of spending, but one that's far more tenable spread out
08:06over a decade.There also could be an element of double ordering in the larger number, with Anthropic makingsoft commitments to neoclouds in hopes they can actually complete new facilities.The journal commented that, quote, Anthropic needs more computing power than the market canprovide.The one other number that people are laser focused on is the revenue number of 16.3 billionfor the first half.Anthropic had claimed a revenue run rate of 65 billion for the year up to July,
08:29and while both of those things can be true, actual booked revenue yields a lower number thanmight have been expected. At last week's Investor Day, Anthropics said that they added another$16.8 billion in revenue in Q3 for a 45% quarter-over-quarter growth rate, but that unauditedwhisper number won't be included in the prospectus when they IPO. The rest of the reportinglargely covers risk disclosures that were already in the press, things like alignment issues,the data center backlash, and customer concentration. Realistically, these leaks mostly haveserved to confirm pre-existing biases. The $80 billion net loss gives AI critics some new materialfor their next podcast, but it strikes me as fairly difficult to imagine that anyone is surprised
09:07that Anthropic issued a ton of stock options and will spend more than a half trillion oncompute when all is said and done. Like the SpaceX IPO, this stock offering is not going to beabout the past few quarters. It's going to be about what people believe about the next decade.Still, market skittishness matters and perhaps explains why some investors are pushing Anthropicto moderate their targets. Last week,Reuters reported that some investors are pushing back on the rumored $2 trillion valuation,and we'd be more comfortable at $1.5 trillion. Keep in mind, both numbers are coming from investors,and we've heard nothing attributed to Anthropic or their investment bankers. Meanwhile,
09:39OpenAI is rushing to complete their latest round of fundraising ahead of the Anthropic IPO.Reports of the round first circulated towards the end of last month, suggesting OpenAIwas seeking $30 billion at a valuation of $1.4 trillion. Now, sources tell Reuters thatOpenAI is trying to close the deal in weeks to get ahead of the Anthropic IPO in whateverconditions that brings to market. The sources said that OpenAI is also keen to capitalize on recent growthafter the release of GPT6 Astra, but this is an unusually fast deal even for this crazy sector.The acceleration rights Reuters underscores how closely the two AI developers are competing for capitalas both pursue trillion-dollar price tags and growing concern about rising interest rates,
10:16significant volatility in technology stocks, and rising scrutiny of AI valuations. These moneyconversations are going to get nothing but more frenetic to end the year. However, for now,that's going to do it for today's AI Daily Brief.Appreciate you listening or watching, as always.And until next time, peace.If you're leading AI inside an enterprise,you already know that the gap right nowisn't capability but execution.
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13:35Welcome back to the AI Daily Brief.Today we're discussing what Scorpion Anti-Venem tells us about the future of AI.Seriously, that's actually the subject of the show.Or at least a set of stories that relate to that attention-grabbing headlineand the themes that they represent.are the broader subject of the show.AI news aggregator Andrew Curran posted earlier this week,drug discovery, new disease cures,
13:58advancements in pharma and biomedicine,along with direct dividends or shares,as is currently being discussed,these are the only viable ways to turn public sentiment around.And this is an idea that you see bandied about a lot,that to actually get people to stop disliking AI,we got to hop to and give them some of those disease curesthat they've been promised.
14:16I actually think that the new activities around biology and AIbring up a much larger set of questionsthan just that one, although that one certainly matters as well.So first, let's talk about a couple of the stories at the intersection of bio and AI,particularly this crazy one with financial firms Citrini Research working on Scorpion Sting Cures,but the first actually comes from OpenAI.The company has announced that they have acquired an AI-powered drug discovery startupto build out their biotech offering.
14:41OpenAI announced on Tuesday that they'd acquired four-year-old AI biotech startup,Orchestra Bio.Terms of the deal were not disclosed,and Orchestra last raised 20 million in venture funding last year.Orchestra develops AI agents designed to assist in drug discovery workflows.Rather than exploring new candidate compounds,Orchestra has largely focused on streamlining lab tasks like monitoring the compound manufacturingprocess and tracking drug trials as they progress.
15:06The orchestra team will work on GPT Rosalind, OpenAI's model variant designed for life sciences work.The model was first released in April as a research preview, but has now been releasedas a full commercial product.Alongside the model, OpenAI offers Rosalind Workbench, which is a suite of agents to helpthe model connect to scientific instruments and analyze data. In their press release, Open AI wrote,Our vision is for AI to help scientists across the full process of discovering and developing newmedicines, exploring ideas, designing experiments, interpreting results, and coordinating the workneeded to advance promising treatments. Bringing orchestra bio together with Rosalind is a step
15:41toward that vision. Alongside researchers and our partners, we aim to make more discoveriespossible and help new medicines reach patients sooner. Now to be clear, in contrast to some things we're going todiscuss a little bit later, this is not about OpenAI building their own biotech firm. It's aboutbuilding better tools to help the existing pharma industry. Also from the press release,they wrote, realizing the potential of AI and life sciences requires close collaboration with theorganizations working at the forefront of drug discovery and development. Our partnership with Novo Nordiskaims to translate its scale and scientific expertise into faster R&D and commercial execution whilemaintaining strong governance. At Moderna, AI is helping teams translate insights from complex data
16:21into experimental workflows with the potential to accelerate R&D.In other words, open AI is giving you the idea that they want to better support thoseindustries use cases of AI, not do that particular work with AI.Many were enthusiastic about this type of work.Ran longevity on X-Rights, I really hope the big labs keep focusing on drug development.That is where we can really change the world.And Presti Genetics, Permita Mistra thinks this is part of a trend.Sharing the news with the added caption, The Biohype begins.
16:48However, some think that this is just an incremental step.Today, Vasista writes,The only path to profitability for the frontier labs is becoming a biotech company eventually.And that position got a bit more credence last monthwhen Reuters reported that Anthropic had quietly set up its own biology lab.So to be clear, in contrast to OpenAI, building better systems for existing labs to use,this is Anthropics setting up their own lab to do physical biology work.After the Reuters news broke, the lab's existence was confirmed by Anthropics' head of life sciences.
17:19Eric Kuder-Abram said,We believe that to do biology, the final test is still and will be for a while in real lab work.We absolutely are doing that today,and I would describe our approach as being typical of what you would see in most biotech companies,where there's some amount of that that we're doing in our own facilities,and some amount of that that we're working with external partners on.Now, this news broke right at the time that the X-risk discussion was surging once again,leaving many to wonder if it was exactly the right time for Anthropic to be building a wet lab.
17:46However, with further reporting,it became clear that it had a relatively limited remit that had pretty specific limitations in thetypes of research it could do. Still, this was also the time that there were reports that leaders insideAnthropic believed that at some point in the future, they would be the only company left. And given howmuch this felt like a move into a totally different area and a fundamentally different model for AI,i.e. not just providing the intelligence, but using that intelligence to do things, there was a lotof interest, let's say, to see how it would evolve. That brings us, however, to the story of SatriBack in July, Anthropic writes,
18:20A team from Citrini Researchhad approached us with an interesting question.Could Claude and AI drug discovery more generallyenable a group of motivated non-expertsto tackle a genuine problem in human health?Now, Satrini Research, you might be saying to yourself,haven't I heard of them in the context of newslettersabout financial issues?
18:39And the short answer is yes.When you go to citrini Research.com,it links directly to their substack,which it sums up as providing insightson thematic equity investing in global macro trading. So what the heck are they doing trying todesign new drugs? According to Satrini's long blog post about this work, the origins of theexperiment came from, as they put it, searching for opportunities outside of AI infrastructure,where they were concerned about markets crowding. One area that seemed to them to have
19:05asymmetric opportunity was life sciences and health care. They wrote, the sector had endured thereversal of the pandemic boom in a difficult funding environment as investors found more excitingplaces for their money. Over the past half decade, it has been a graveyard for returns. While everyonefocused on which Japanese semi-dudad producer would sell out next, the cycle had seemingly turned.However, the stocks had yet to climb in any meaningful way. And we kept hearing about AI's promiseand drug discovery. A neglected sector with the chance for AI upside, it had the markings of ourfavorite type of setup, a secular story at cyclical lows. And also, they said, given that their lastbig AI post had been effectively about how AI doing too well even could also destroy
19:44the economy, the opportunity to potentially tell an optimistic story also seemed exciting to them as well.With tongue firmly in cheek, they wrote. Finally, it was our chance to write an article offering a reprievefrom the threats of a dystopian AI future dominating the news. Not that we have ever been guiltyof fueling fears about the future of AI, of course. The one problem they continued, none of uscould claim to know anything about AI as it related to biology. Our usual process, they wrote,would involve deep research in dozens of calls with experts. But serving a dozen experts in biology,and you're going to hear both,AI has not created a single important drug
20:16and it won't in the future either.Two, AI will cure all diseases in the next five years.And everything in between,depending on each individual's degree of AI-pilledness.We weren't going to get a clear answer by simply asking,we needed to try it for ourselves.So we did.We used the model to design proteins,
20:32contracted a lab to synthesize them,and conduct experimental validationand again using the model to interpret the results.Now, this Gonzo approach to biotechseems to have indeed yielded some results.The Citrini folks were able to use Claude to design protein binders for use against scorpion neurotoxins.The group started with a 35-page prompt that Anthropic had already tested in a previous study,modified it to taste and set Claude on its drug discovery work.
20:55300 subagents worked on the protein design, generating 318,000 total candidates,and selecting 211 promising options for further testing.17 were found to bind to four different scorpion toxins after independent testing in another lab.None of the proteins has yet been shown to stop the neurotoxin from working,but it's a promising initial result. Also, all this work came in under a couple hundred thousanddollars, which, while not cheap, also isn't prohibitively expensive for trying to go after anunder-focused on issue that affects a million people a year and actually kills 3,000.Now, what was interesting about the way that Satrini did this is that almost immediately after
21:28their rag-tag group of stock analysts downloaded Claude for Science, they ran headfirstinto the biological guardrails. Rather than trying to do some extensive application process,they just reached out to Anthropic and asked if they could do it sitting in the company's offices.Anthropic was interested right away because this is exactly the type of neglected diseasethat they think AI has a particular opportunity to benefit. Scorpion's sting about 1.2 million peopleper year, which leads to around 3,200 deaths, and most of those come from children in parts ofthe world that have extremely limited access to quality medical infrastructure. The problemAnthropic writes is that new treatments are expensive to make and also unlikely to earn back
22:04the cost of developing them. And this is where the potential step change in the cost of developingnew treatments that AI enables could come in.There was, of course, plenty of skepticism when Satrini dropped the report.Wisplum on X writes,So you achieved something demonstrated in the literature 15-plus years ago,proteins that bind scorpion toxins,but didn't demonstrate the thing that actually matters,neutralizing the venom.
22:25Maybe make that the headline instead of burying it in 10,000 words of Claude Pros.Satrini responded to that one saying,LMAO, everyone involved in this said,well, as soon as we publish this,there will be a handful of haters no matter what we do,we weren't going to wait another three months to publish.still want to pursue the neutralization testing, though.Google DeepMind's director of AGI economics, Alex Emos, wrote,
22:46Satrini has an incredible talent for getting the public to pay attention to overlooked but important issues.Last winter, it was the famous essay on how AI-driven productivity could lead to a miseration.I disagreed with parts, but thought it did an excellent job of starting a much-needed conversationabout economic risks.Today, Citrini got everyone talking about the promise of AI for science.Huge speedups in hypothesis generation and computation, but also illustrates the bottlenecksverification and testing. We need to be clear-eyed about both in order to know where to invest.And in fact, one of the Trini's major conclusions was just how much physical world inertia there is,
23:19even when AI speeds up part of the process. They wrote,Our virtual biologists may have compressed a year's worth of work into a couple days,but biology works at the speed of life. DNA had to be synthesized, proteins produced andpurified, toxins obtained in usable form, and appropriate controls established.Our digital invention was now set to create work for human scientists.Former Open AI researcher Wildapu put two of the charts from Satrini research next to one anotherand said, God, these two charts really hurt my soul.64 hours to dry lab design a multi-binding anti-venom, 39 days for results, and four to seven years to release.
23:52I think that the combination of the Satrini Anthropic collaboration and this new open AI dealbring up three really interesting questions.The first is whether AI labs will start internalizing scientific results rather than building technology for others to use and what it means if they do.This is the question I said really picked up steam a couple of months ago,first when reports came out, which by the way are unverified,that Anthropic leadership thought that they would be ultimately the only private company left in the world,implying that at some point they do everything,including all the things that their customers do currently,
24:23and which picked up more steam a month later when the Anthropic Wet Lab was announced.Yet what the Satrini research shows is that there are so many parts of this processthat even adding a wet lab to take theoretical AI-initiated experiments a little bit fartherdoesn't change the fact that there is an immense number of other steps in people and institutions involvedin actually bringing something real into the world. I don't think this at all settles the question ofwhether at some point AI customers will find themselves competing with the AI labs,in fact that is going to be absolutely inevitable, but thinking it's going to happen all at onceor across entire industries probably doesn't reflect the reality. The second question is this one
24:59about discoveries in public opinion. If AI actually does cure cancer, does that change people'sperspective on AI. First of all, at least when it comes to anthropic experiments right now,they seem to be a lot less about these holy grails like curing cancer, and more about addressingthese medical projects which previously fell through the cracks of market incentives, but whichdon't have to anymore thanks to AI. Anthropic might absolutely be correct that there is a lotmore leverage there than the big flashy stuff, but a news headline like Anthropic cures scorpionstings, which to be clear this isn't even really close to, it's just the very, very first stepin some solutions that could help this, still hits very different to Anthropic Cures Cancer.
25:36Second, even if AI is totally integral to curing major diseases, it seems unlikely that the waythat that works is it just discovers a miracle compound that all of a sudden fixes everything in onefell swoop. In fact, it seems far more likely that AI accelerated discoveries just kind of becomethe norm, everyone gets used to them, and they don't really have all that much bearing on whetherthey like AI or not. Will Manetus writes,If the AI industry thinks curing cancer is enough to turn public sentiment around, they should look at opinion polling on big pharma.To be clear, this does not in any way mean that I think that AI labs shouldn't be working on these issues.In fact, to me, my belief that very little they can do can, in the short term at least, actually really impact public opinion,
26:17means they should be focused on working on the areas that they think are going to have the biggest impact,regardless of the headlines they create.As much as I would like there to be, I just simply don't believe that there is any magic bullet to the perception problems that AI hasand is going to continue to have for some time.The third and most subtle, but in some ways, most interesting question to me, is how theSatrini experiment specifically demonstrates the way in which AI will change not only how we work,but what we work on.Remember, the Satrini experiment started from them doing what they normally do, which is look
26:49for under-recognized opportunities in the markets from an investment thesis perspective.They went through their normal work process of examining different sectors in the context ofreturns, public perception, current market values, and basically came away believing thatlife sciences were undervalued, especially in the context of how AI could change those areas.The problem they ran into is that they didn't think that they could get a robust enoughpicture about the real potential of AI in life sciences just by asking a bunch of experts.And it was in that context that it became economically viable for them to just experiment forthemselves.
27:24Now, Satrini was very clear at the end of this that they are not about to become a biolab.that they are and remain a financial research firm.But I think that you're going to see a lot more of this type of orthogonal work, let's call it,where people in firms can do things that they couldn't do beforethat actually change how well they do their core work stream.This is another version of the Efficiency versus Opportunity AI divide that I always talk about,to go to original hands-on research deep in the biology and life science process.If nothing else, it is a very entertaining story,
27:53and I suggest you go read the full Satrini Therapeutics Research Report.For now, I'm excited to keep exploring.these themes with you, but that's going to do it for today's AI Daily Brief. Appreciate you listeningor watching, as always, and until next time, peace.