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Patrick Collison’s reading warning and a founder’s agent demo
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2 min read
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88 docs
A high-signal, two-item resource list: Patrick Collison’s link to Ed West’s critique of declining literacy, and Cliff Whitesman’s specific recommendation of Brett Taylor’s Sierra product demonstration.
Strongest recommendation
Doomscrolling ourselves to death
- Content type / creator: Article by Ed West. Patrick Collison shared it as “Ed West on the decline of reading.”
- Key takeaway: West uses James Marriott’s newly published book The New Dark Ages as the frame for a broader argument about literacy’s decline. The piece reports falling reading comprehension and reading for pleasure, then connects television’s bias toward triviality—and the later intensification from smartphones and TikTok—to the erosion of concentrated attention and reasoned public discourse.
- The useful positive case: This is not only an anti-screen argument. The review presents reading as an empathy engine: books let readers enter the inner lives of people separated from them by class, sex, or race, weakening rigid social categories.
- Why it matters: It is the strongest recommendation because it treats reading as a civic and intellectual capability—not merely a productivity habit—and gives a more consequential reason to protect time for serious learning. The review connects literacy to the quality of public reasoning as well as to empathy.
- Caveat: West discloses that he and Marriott have become friends. He calls the book entertaining, informative, and coherent, but the relationship is a relevant qualification to this favorable review.
A practical video recommendation
Brett Taylor’s Sierra Shopify screen-share
- Content type / creator: Presentation/video by Brett Taylor. The interview does not supply a direct URL or formal title; Whitesman tells listeners who have not seen it to search for it.
- Recommended by: Cliff Whitesman, founder and CEO of Speechify, during a 20VC conversation. He calls the presentation “extremely impressive.”
- Key takeaway: Taylor’s screen-share shows him building a guitar store on Shopify and using Sierra for customer support, sales, and related work. Whitesman describes voice as Sierra’s current wedge but identifies tool calling as the more important underlying capability.
- Why it matters: This is a concrete founder-recommended example for readers evaluating agent products: the value is not just conversational fluency, but carrying out work across a customer-facing workflow.
Patrick Collison’s reading warning and a founder’s agent demo
Research extraction
Direct answer:Doomscrolling ourselves to death — Ed West. The article reviews James Marriott’s book The New Dark Ages.
Central argument: As West presents Marriott’s case, declining literacy is a civilizational shift rather than merely a change in taste: reading comprehension and reading for pleasure are falling; television began the transformation, while smartphones and short-form video have intensified the loss of concentration and reasoned public discourse. The consequences extend to cultural simplification, less innovative science, poorer political debate, and a possible weakening of democracy’s analytical foundations.
- Why reading matters: Marriott treats literacy as an engine of empathy and egalitarianism: novels and memoirs let readers enter the inner lives of people separated from them by class, race, or status, thereby undermining rigid hierarchies.
- Historical arc and evidence: He identifies peak literacy in the early-to-mid twentieth century; a 1944 survey reportedly found that nearly two-thirds of skilled workers and almost half of unskilled workers grew up in homes with substantial libraries. The subsequent spread of television coincided with a major reorganization of domestic life and, in Dutch data, a fall in weekly reading from five to three and a half hours between the 1950s and 1970s.
- Most concrete quasi-experiment: The article reports Øystein Hernæs’s study of Norway’s staggered cable-TV rollout, using military conscription intelligence tests; it found that each additional year of full local coverage was associated with a 0.08-point IQ decline, and concluded that greater exposure to commercial television reduced cognitive ability.
- Current attention economy: The average person reportedly checks a smartphone 144 times per day, while an average TikTok user may scroll through roughly 260 videos in half an hour. West uses these figures to explain why contemporary media favors rapid, repetitive, low-concentration consumption.
- Cultural and political symptoms: Marriott points to simpler, more repetitive pop songs; less complex television dialogue; scientific work becoming less disruptive; and parliamentary speech becoming shorter and less allusive. A vivid illustrative contrast is library-reading Harry Truman versus Donald Trump, whom the book describes as unable to get through one-page memos and as an almost “post-literate” leader.
- Normative and social-stakes claim: The book explicitly defends judgments of cultural quality—such as preferring War and Peace to TikTok—and links the decline of reading to widening social gaps: the article says children with heavy iPad exposure tend to come from poorer backgrounds, while technology elites often restrict their own children’s screen use. It also reports that college graduates born after 1969 read less than less-educated people born before 1950.
- Recommendation and caveat: West calls the book entertaining, informative, amusing, and coherent at roughly 50,000 words, but says its implications are disturbing. He also discloses that he and Marriott are friends, so the recommendation comes with an explicit personal-relationship caveat.
Aaron Levie
- “Applied AI Doesn’t Work” — an X article by Vas, who identifies himself as CEO of @varickagents. Aaron Levie explicitly called it a “Good post” on enterprise AI-agent process redesign, highlighting that real efficiency requires reengineering workflows around what agents can do differently and faster, especially across functions where an empowered owner must redesign how work happens.
- The article’s practical framework is to map workflows at the department level and combine process mining with operator interviews. Classify each step as deterministic, agentic, or human-in-the-loop, then baseline KPIs and prioritize workflows with the most handoffs.
20VC with Harry Stebbings
- Brett Taylor’s Sierra Shopify screen-share presentation (presentation/video; exact title and URL not provided): Cliff Whitesman, Speechify’s founder and CEO, recommended a presentation showing Taylor building a guitar store on Shopify with Sierra and using it for customer support, sales, and related work. Whitesman called it “extremely impressive” and told listeners who had not seen it to search for it.
How to Build Your Own Data Center & Why Every Startup Should Do It
20VC with Harry Stebbings
YouTube channel
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highlights
0:00You said 11 raps kind of leaprogged you.
0:03Is that on you?
0:04Yeah, 100% it's on me. It's the biggest strategic mistake I made in the history of Speedify.
0:07How do you reflect on that?
0:09So today is a real freaking discussion.
0:11Cliff Whitesman, founder and CEO at Speechify, one of the fastest growing text to speech startups in the world on the show.
0:18The best way to lose is not to be in the race. Be in the race. You don't want to be a fat manager who is like a general sitting in the back saying, "Take that hill." You want to be the warrior who runs up with their sword and engages the enemy first.
0:29Ready to go.
0:42Cliff, it is so good to have you back in the studio, dude. I I was looking forward to this one cuz when I was writing it up, it's a very different thread of conversation to how I'd normally go. And so, thank you so much for joining me again today, dude.
0:54My pleasure. Glad to be here as always. Now, I wanted to start with you're spending tens of millions of dollars on Nvidia GPUs and you're paying an additional $100,000 per GPU to receive them 4 months early. Why? Like, what do you know that the market doesn't know? So, in 2022, we bought a huge rack of GPUs from Nvidia.
1:17And the reason we bought them is for training, right? We have a bunch of models. The newest Speedify Simba 3.2 2 model is ranked number one in the world for quality um above all the frontier labs 10x more affordable and stuff like 11 labs and we used to rent GPUs and we found that engineers at speechify would be parsimmonious with how they use the GPUs cuz they were like oh my god I'm costing the company tens of thousands of dollars like I don't want to do that and the analogy my brother and I came up with is imagine you're Michael Jordan and you want to be in the NBA it's the only thing you care about and you need
1:46to pay $20 an hour just to train in a basketball center well that sucks you want one that you can go to whenever ever you want to. In fact, you want a hoop in your house. And so our initial idea was we want a hoop in our house.
1:58And so we bought a bunch of our own GPUs. And that deal ended up being really good for us. And we ended up training really good models. So with time we invested more and more and more and more. So that's the first part. The second part is actually how the economics work out. So if you look at it, the transformer was invented inside of Google in 2017. Nvidia came out with A100 GPUs in 2019. Shortly after they came out with H100 GPUs, right? The original Chetchup PT was trained on A100s. And then they came out with
2:27Blackwells. So then B200s, B300's, and now they came out with Reubins, which is the GPUs that Elon is sending to space. And they're like liquid cool cooled.
2:35They're very, very cool. And we're like, okay, huh. One, every class of GPU is more affordable per one trillion flops, right? So, a flop is addition, subtraction, multiplication, any mathematical operation, and you measure them in how many trillion of operations happen per second in a GPU. And so, they're more affordable as it relates to this. Um, if I was to buy an H100 for, let's say, $30,000, that's how much the kind of a single card would cost. If I wanted to rent an
3:04H100 for one hour spot instance from GCP, it could cost me $5. If I rented it from like, you know, Azure or AWS, maybe it'll cost me $3.5 per hour. So, if I multiply that times 24 hours and then times 365 days in a year, I'm actually going to end up paying $35,000 to $50,000 to rent that GPU for one year, but I could buy it for $30,000. So, it's 1.5x the cost of owning the hardware to rent the hardware for a year. Now, the
3:32hardware is typically um warrantied for 3 years to work properly, but it'll keep working up to the warranty for I imagine, I don't know, 10 years. So the math just maths where it makes way more sense to buy them. The other big part is if you want to do large scale training like we do, you need the memory to be colllocated with a large cluster of GPUs. I can't just rent from Google or Microsoft or even B 10 and run the size of training that I want because I need a gigantic memory card next to it with all
4:02of my data that all the GPUs are accessing. So that's why we first started buying them. The next thing that we found is actually if you run open- source models for coding, you could pay anthropic and then you know you're paying for all the tokens and the fact that you're doing the the the branded right fable one or you can run an open source model and instead of running it on a spot instance from Azure or anyone else you run it on your own hardware and then you're paying a fraction of a fraction of a cent per token and so for all those reasons it made a ton of sense but we
4:32can go into all the depth that you want.
4:33I just want to dig in. The first thought that I have is I completely understand the rationale there but chips depreciate. You have chip cycles and they are accelerating. We are seeing newer and newer chips being created.
4:46We're seeing specialization within chips by buying your locking yourself in so to speak to one chip architecture. How do you think about that? At speechify, we still use K80s for a lot of specific operations for inference and we use older models of GPUs constantly. Um, and there's essentially a difference between when you do inference and when you do training. For training, I'm like, okay, I have this hypothesis. I want to know
5:15the answer to this hypothesis as soon as possible. Like every minute that it doesn't come out, I'm in competition with everybody else. And so having a GPU architecture that is much faster by orders of magnitude is a huge advantage.
5:27But if you go speech to text or text to speech with speechify, I can afford to give you a lower quality GPU and it'll give you what you need still in, you know, 100 milliseconds. So it's like totally good. And so I can always use these older GPU models for inference.
5:42That's number one. Number two, we have so many experiments that were running at every single point in time. Not all of them need to run on like the newest hardware. So, the analogy I always give, let's say you bought an iPhone back in 2011 and it's an iPhone 3G and then you bought another iPhone and another iPhone and another iPhone. You could have a drawer in your house with like five iPhones that are collecting dust cuz you can only use one iPhone at a time, but if I own a 100,000 GPUs, I'm still going to use all of them at the same time. And so, I'm not losing
6:12anything by having more GPUs because not only do I own a bunch, I still rent from the hyperscalers all the time. And I rent both dedicated instances that I prepaid for and I rent spot instances.
6:23For example, more people use speify in September because everybody goes back to school. So I need to like level out the load. And so the parts of that load that I know for sure I'm always going to use whether it be training or it be inference, I might as well just own it.
6:37And then on top of that is also the case that I have so many other friends who are running training and running inference. I can always rent it out to other people if I have excess capacity, which I don't expect to have. But like every once in a while you have an interesting situation. So for all those reasons, it just makes mathematical financial sense. Lastly, if you have excess capital really, you either stick it on the bank or you buy a bond, right?
6:58Like the best long year, the best bond you can buy long tail, I don't know, will yield you like 5%. Or you can buy a GPU and because renting it would cost me 1.5x buying it for the year, the return is like way higher.
7:12So how many GPUs do you buy then?
7:14So let's talk about Reuben's for example. So Reubins come in the form of 72 cards in one rack. So we'll buy multiple racks of Reubins and then on top of that we'll buy B300s which are like the newest form of Blackwells uh because we can get them earlier. And then the same thing like you know when we bought our first instances of uh DGX edge 100 GPUs. We bought just like a bunch of racks of those and then those get delivered in a truck to the data center. Uh we rent the data center uh space and so the data center provides
7:42the uh networking capability. It provides the energy which is actually the largest constraint now and it provide like physical engineers that take it off the truck, they install it, if it has an issue, they fix it. Um, and then it just runs.
7:57Does 11 Labs do this?
7:58Yeah, 11 Labs is amazing at this. 11 Labs I think pot at 11 Labs literally bought a bunch of GPUs early on and set them up in his house and then they just kept building bigger and bigger and bigger clusters. They they do the same thing that we do.
8:11How do you think about forecasting chip buying? It's incredibly difficult to know a demand but also b supply of chips. How do you think about forecasting chip purchasing?
8:21Yeah. So number one I want to explain again it's very different than buying an iPhone or buying a MacBook. I can only use one MacBook at a time, one iPhone at a time, but I can use all the chips I have at any given point in time and I still will have more demand, especially when I have multiple teammates and 60 million users who are using inference on my speechify software that's providing text to speech and helping them, you know, read their work and dictate their work and, you know, use speechify work, which is our our newest product. That's a gent uh kind of like Jarvis from Iron
8:48Man. And so I go, okay, let's imagine I have 100% capacity. That is the average usage per month that I need for GPUs for training of my AI models and for inference on my AI models. Inference is when you actually make a call to speechify and you give me text and I give you back audio. Like there's math that happens in the background. That's inference. Training is I take a gigantic amount of data. I take all the architecture and software engineering that we're doing and I go I think that this will give me a better model. I'm kind of baking that model in the oven and I'm going to come out with a new
9:18black box. And then when I give you text, that black box is what calculates it and gives you back the audio. So those are the two usages. Let's say I have 100% which is what I would have in let's say a month like November. In October I'll have 140% because it's like a big month for us. In December, you know, everyone's at home, you know, they're not necessarily studying or working. So I might have 80% utilization. Okay. So I go, cool. Well, I can take 20% of the usage that is normal and let me buy it because it's
9:48the best deal. I'll take another 25% of the usage and I do long-term contracts with hyperscalers. The rest I'll rent what's called spot instance from the hyperscalers. And then I'm still not even close to overcommitting myself. Um, and so that's kind of how we think about the math. And then we go, okay, well also we have 45 engineers, but we want the team to be 150 engineers. And even inside of my 45 engineering person team, like there's a couple people who are rock stars. They have dedicated like DGS
10:16racks just for that one person.
10:19And 25% of my team are almost like waiting and I want to double the size of the team. They just need like it's like you have a football team and you just need another field because they don't have enough field to practice on. Uh and so that's how I think about how to allocate. And then in terms of depreciation of the asset over time, I go, okay, well, it these are still amazing GPUs. Like even A100s you can run amazing experiments on. So it's completely valid to use that as long as it's hooked up and as long as it's not stopping to work. And so think about the
10:48mileage of a car, right? If a car gets to like 250 miles, you know, it's kind of going to break at this point. That's not necessarily true for a GPU because it doesn't have as much wear and tear.
10:59Yes, it's moving and yes, all these things, but like it's in a very clean environment. It's very much cooled. It has constant maintenance cuz it's not moving around. It's very expensive. Um, and Nvidia just does a really good job.
11:11And so that asset is going to stay for a very long time. And let's say it got so not good, so outdated that I no longer can run training on it. Cool. Now I'll use it for inference. There's one more thing that's very interesting that just happened. So, um, I believe earlier this month, Nvidia did a huge deal with Blackstone, Black Rockck, Apollo, and Goldman Sachs, and they said, "Listen, we want more people to buy more GPUs.
11:38We're going to underwrite for you up to 25% the value of a GPU." that if you lend money to someone who buys a GPU, let's say Google or or a startup Coreweave, and that startup goes out of business and you have that GPU as collateral against that investment will buy back the GPU for up to 25% of the value of the GPU. And so they're succeeding in creating a liquid secondary market for GPUs that they're underwriting. So now the large banks have an incentive to loan money at much
12:07better interest rates. This is actually exactly what Elon did in the beginning of Solar City. He went to Morgan Stanley and Maril Lynch and got them to advertise the price of a solar panel over 30 years. So the whole invention behind Solar City was the fact that you could take a loan against the collateral of your solar panel. So Nvidia has done an amazing job now in creating a clear floor for the value of the GPU over time.
12:28Do you think the circular economy fears that people often cast against Nvidia are justified or not? we saw their CFO push back on them and say enough enough of this [ __ ] Do you think that justified or not?
12:40Uh I think that a lot of the things that about a year ago like we're going on between Oracle and OpenAI like that was way too much. Like that was ridiculous.
12:49I think the Nvidia stuff is is not because you're talking about a real asset. So if you think for example about the logic behind the value of Bitcoin, right? Bitcoin, what is the intrinsic value of Bitcoin? I can't really tell you, right? What's the intrinsic value of gold? Well, gold, you can use it for some medical stuff because it's a really amazing metal and you know it's jewelry, whatever. But a GPU, it has intrinsic value. Like you can actually use that asset for something that's really really valuable and it doesn't matter where that GPU is. It could be in Iceland, it's still useful to anybody all over
13:18the world as long as it's uh and so actually it has a pretty good store of value. Even if new GPUs come on online, really my one question and this is the math for everybody to come back to is how many terra flops per second can this device do? And that is like essentially a token. That's the value. And so like there is intrinsic value. So yes, you can have all these like circular things, but at the end of the day, Nvidia is making a project, a product that's real.
13:46It's not complete tool mania. Like there's a real real intrinsic value here. What does no one know about buying chips that they should know?
13:54What's the like, oh my god, people are so naive about this?
13:58I mean, it's not that people are naive, it's just they haven't been in the space. So, I'll give you an example.
14:02Imagine you're buying a GPU. Well, you're going to buy it. You know, it's an Nvidia produced product, but Nvidia is not going to waste the time talking to Cliff Whitesman. So, who do I buy it for? Well, one of the best rated vendors is Dell. So, everybody thinks Dell is a personal computer company. No, Dell is a GPU rack uh supplier at this point. And then, okay, I want to buy it from Dell.
14:25Well, Dell has a constraint because there's not a lot of like, you know, black wheels out there. Well, it happens to be that they have someone in France.
14:32All right. Well, I'm going to order mine from France. Okay, shoot. It was supposed to come a month ago and it's still not here, right?
14:39Because of whatever, like, you know, there's demand. So, then you need to negotiate to make sure that you get it, which is why we're very willing to pay 100k per month extra to get them earlier. So, so you'll call up Pierre in France and say, you know, hey, we'll give you an extra 100k kicker if you get them here in a month. Uh, even more than that. So, in that France situation, which is something that happened to me, um, I was like, Pierre, what the heck?
15:01We have a contract. You're not delivering on time. And so, it is the case that we had a contract with another company beforehand and they were, I don't know, a few weeks late. And I called them and I was like, listen, I've got a better deal. I'm canceling our contract cuz you're you you're out.
15:15Like, you didn't deliver. So, I'm going to go with this other contract, but if you have a better price, like we'll go with you, but like I just need the GPU now. And remember, I'm paying for the renting space of my data center. So, the most expensive part of a delivery of a GPU is if it's late, I'm still paying rent for that data center space. Now, that GPU and so, you know, you put pressure on Pierre to send you the thing when he said he was going to send it to you. And then you go to Nvidia or Dell or whatever, and you're like, look, it's a market. Hey, can I pay more to get it earlier? Skip the queue. Yeah, you can.
15:44Cool. Now there's a truck somewhere in the United States with a GPU whose value is the value of a house that's coming to my data center, right? Well, I should have insurance from that, right? Because if that truck gets hit or there's too much humidity or the GPU gets flipped, like I lost multiple houses worth of GPUs. So, okay, the insurance is really, really important. And then also the value of the amortization is really, really important. And it's like there's all these nuances of how to do the math through. Then there's the cooling, right? So, like you're not only paying
16:13for the physical space and the networking and the energy and the energy is the biggest constraint. We'll talk about it in a second. Well, how do you cool that thing? Because you have a thing that's just like moving and moving and moving moving and moving. Um, well, the thing that's like most new now is liquid cooling because air is just not enough. And the uh the thermal load of water is much better and there's other liquids that are even better than water. And so Reubins are are liquid cooled, but most of these data centers don't have liquid cooling installations
16:43already approved. So we had to do a bunch of research and we find, okay, we could buy what's called a sidecart of liquid cooling that you enter into the data center. Then you pay someone at the data center to install it for you. Cool.
16:52Now you can have like the rack that you want. Uh and so there's a big difference between running a purely software company and running a company that includes hardware. Um, and but when I listen to all of this, I'm now more sure than ever that it is a mistake to price optimize and to spend the money to buy it versus to rent it because I get you on the optimization, but you're not saving 10 times more.
17:14It's.5x more per year per year. Exactly.
17:18Per year. But you have the flexibility to tailor it up and down. You don't have any of the logistical nightmares of insurance, transportation, security, water cooling, logistics, and then you can build your product. Actually, what matters most against 11 Labs who are [ __ ] running fast. I don't want to worry about water cooling and insurance for a freight truck.
17:3911 Labs worries about the same thing because for them to train excellent models, they need to have colllocated GPUs with a lot of memory available.
17:47You can't do it if you rent it.
17:49You can. it just becomes one ridiculously expensive, two you need to commit for many many years ahead of time because you need to build a coll-located cluster um and then you don't have as much control because you don't own it.
17:59So it's like difficult to like you suddenly need an infiniband cable which allows for the memory to flow from one DGX to the other one. And the answer then is well now my ability to train is so much bigger. I can have a larger AI team. Every person in the AI team is leveraged and I could just I could shoot ahead of everybody so much faster. And let me just make one thing clear.
18:20If I want a Reuben, which is like these much faster GPUs, I'll get it faster if I buy it than if I wait for Google to buy it and then there's other people in front of me in line. So, I'm going to skip the queue by like a lot and then I'm going to have like a year of access to Reuben before everybody else does.
18:34The way I think about it is the following. How do you build an amazing company in a world where there's so much competition today? The team is the most important part, but the team is the most important part because the team gets you the other resources. And so, what are the missing pieces? The missing pieces are data, compute, and architecture. In a world where intelligence is commodified and no one needs to handw write code at all anymore, right? Our engineers really what I'm looking for is 10 really good decisions per day, which is very tiring. Not like optimizing random parts of the code. And each one
19:04has like, you know, five to 18 agents running at any point in time, doing long horizon tasks on these GPUs, coming up with thesis, testing them, going back and forth, back and forth, back and forth. If they don't have the capacity to train, the team is limited. If they don't have the data to train, the team is limited.
19:23And by the way, a lot of data it cleaning the data, right? You get this raw data in the beginning. Well, you need to organize it into data sets. And so, you need the GPUs to also organize the data sets, too. Like I have one of my my best engineers right now is not even writing models. He's making synthetic data sets to train models and so like it really becomes a indispensable asset.
19:44Would you ever buy data?
19:45We have but like small data sets. So I suggest you use fireworks but uh I mean fireworks is amazing. Lyn, the founder is she one of the co-founders of PyTorch. But I I had the very obvious realization that you'd have every company having their own specialized models of a certain size trained on their own data. Um, but you would need supplemental data sort of like this synthetic data or real world data that you just don't have and that you would buy that from data providers like Mccor, which is why I was
20:14micro one surge all these companies are amazing and they shorten the cycle by the way to getting to revenue 100% because if you're a Meror uh shout out Brendon Foody um 11 Labs or OpenAI or Anthropic is going to make money for the next decade or two on the data that they bought from you and so they're willing to pay a fraction of that 10 years of revenue to you today to supply them the data and again it's all a speed thing yes 11 labs or you know open AAI can go and make a team that will get the data
20:43but they don't want to manage it and so the data is key all this like you need all three things you need compute you need data and you need team that writes great products and ideally you need users that use you a lot and a lot of them to have a feedback loop of whether the stuff is good or not so benchmarking and so for me when I was doing the as a venture investor we do outcome scenario planning which is the most [ __ ] exercise to pretend like you're smart predicting the future We do it because you know it makes us feel important. Um but you know they predominantly sell to Frontier Labs today and that's where 90% of their revenue is from. With the rise of
21:11specialized models on a per company basis with their own data, I believe that you move that customer base from purely frontier labs to every large scale enterprise who needs supplemental data. If that is the case, how big a outcome is the data marketplace?
21:29So the first problem to understand about the data marketplace is it's not ARR, right? It's not annual recurring revenue. It's one-time deals every single time. So the buyer of the data is not required to buy it from you again. So it's a very risky business. And if you look at early days of companies like Merkore, they didn't raise significant funding off the bat because investors were very skittish about that fact.
21:49Let's put that aside. Well, very important that the tees are crossed and eyes are dotted about how you got that data, right? And so like you know like you need you need to indemnify the companies who are using you and that's part of why they buy it from you as opposed to sourcing it themselves or you've seen the lawsuits. Um but it's a great business and if you could do it well but like you need to be an ops monster like you need to be really really good at operations. You need to
22:17be very fast and really the key is the company training on your data needs to actually see improvements in their model at the end of the day. thing that has always been challenging for Speechifying compared to other companies is B2C customers pay a lot less than B2B customers. So 11 Labs, huge credit to them, leaprogged us because they sell to B2B. Well, historically we've only sold to B2C. And so our big constraint is we needed to do this on a cost basis of it needed to cost us, you know, less than $10 per million characters. 11 charges
22:4610 $100 per million characters. The OpenAI model and the benchmarks cost $196 per million characters. So ours when we sell it to other B2B companies now we just launched our API Simba 3.2 it costs $10 per million characters.
22:59Dude, I am I'm too old to not ask the painful questions and I think the joy is the more you ask them and kind of less you know why you are about asking them. You said 11 Labs kind of leaprogged you.
23:10Is that on you for not doing?
23:12100% was on me. 100% was on me. It's the biggest strategic mistake I made in the history of speech fire.
23:16How do you reflect on that? So I met Patrick and uh Mati. I was living in London at the time in my house in London. I think it was 2022. And we were very impressed by them. And we wanted to use the model by the way. It was just too expensive for us to use. And I looked at it and my thought to myself was they're very smart. They're going to do well, but I don't like their strategy because I think that an API for text speech will become commoditized with
23:46time. Right? you're going to get to the point that you can run that API on your computer and then on your phone and then like what are they selling anymore? So, I don't want to go into that business and I made a critical error. What I didn't understand is that the point of an AI lab like speechify or like 11 Labs is to continuously innovate and the first product that you release is your wedge that gets other people to then later use your other technology. So, for example, if you're in text to speech, you build the best text speech model in the world for one specific voice. Cool.
24:11Well, now you can do other voices. Now you can add emotional procity. Now you can add voice cloning. Now you can add speech to text. Now you be build duplex models where it makes the um ah laughter uh interruption handling, turn taking.
24:24Um you add a harness for voice conversations. Then you optimize it for sales and you optimize it for customer support and you optimize it for all these things. And so what they did is they first built an amazing API. They were great at launches. They built a really great uh product for creators.
24:40Then they built their best product ever which was agents. agents is amazing because the buyer is no longer a software engineer. The buyer is a CTO, CIO, CEO, executive in the company. Uh Sierra has this concept called outcomebased pricing. Brett Taylor is amazing. And so you can start finding the outcome. And having a AI agent is like having an AI co-orker. But it was my mistake to think that an API product
25:09was a bad strategy because I thought it was something that would become commoditizable. And I forgot the central thesis about Silicon Valley, which is constantly innovate. Get the user to start using your product. I don't care if it's free, then you sell them other things. And so that was my big big big mistake. How possible do you think it is? I think people underestimate the complexity of building out a B2B GTM.
25:28Super hard.
25:29I think it's a strategic mistake for speechify to go to B2B.
25:34A lot of people think that. Tell me your position.
25:37You are now competing against 11 Labs and Sierra. Really? And those two are competing whether they like to admit it or not. They absolutely are competing and they will I'm sure if you ask them off camera. Um that's Brett Taylor.
25:49Yeah. You don't want to compete against [ __ ] I don't want to compete against Brett Taylor. Uh and that is the tidal wave of 11 Labs. Now 11 Labs is an unstoppable machine at this point to the point where it has government buying across all of the large major western democracies. Actually it's insane the government buying they have and they started 3 months ago. I I you just said the key thing they started three months ago.
26:12Yeah. And so you know the graph of but I think they've reached a tipping point where actually they've just taken the market. I think Sierra running behind them chasing and they're doing a decent job of it but they've got Brett and they've got Sequoia and Green Oaks and every royalty of Silicon Valley behind them and they're still running behind chasing 11 Labs with Sequoa kind of pretending to be neutral because they're in both of them which is incredibly challenging. And I I just think being third, the Postmates effect
26:42is never a good market to be in when I could be the dominant consumer brand that leads with a really different and compelling story.
26:50So here's the two things to consider.
26:52The first one is if you go to the app store and you search Texas to speech, speechify has 98% of the installs in text to speech for B2C. Speechify has served more than 770 billion words to users over the last few years, which in terms of times of listening, it's like 6,000 years of listening, right? If you go from today to zero BC and back, you still have like thousands of years left.
27:14So, we've like completely dominated that market and it's still a business that's growing really, really fast. Uh, and we're constantly adding more features into that product. The thing is we have a pretty big engineering team and now everybody is capable of doing 10x what they did before. So, I have extra staff.
27:27I have a huge AI engineering team with the ability to make amazing models. Uh so like where is the highest ROI for that to go? Well, it needs to go both T B toC but it should also go B2B. And one thing that I will never be is a person who doesn't learn. So I might as well just freaking learn B2B. Now to your point about competing against giants like Sierra or 11 Labs. Hey, Anthropic came into the market as a second to OpenAI and they were second for a very long time and now they're not second.
27:52Facebook came as a second to Frster and now in my space and now they're not second. And so the nice part is this space is not a monopolistic space. It's an igopical space. And if you look at what happened with 11 Labs, I'm going to exclude Sierra because Brett Taylor effect is huge. It's just amazing to see how good of a business that is. And so it might very well be that for the core offering that they're currently winning on, I will not win. But what did I learn last time? It's fine if I offer my product essentially for free because I'm
28:21an AI research lab and as long as people start to use me with time I'll be embedded in the system and I'll keep coming out with more and more and more innovations that are useful to them. And so there's unbelievable demand from all these companies and governments and everybody else for great tools whether they be AI agents or APIs or products. I just want to be on your phone if you're a user or in your stack if you're a company and supply you with the best uh front deploy engineer experience and AI orchestration experience and API
28:51experience to give you an amazing experience and there's room for everybody.
28:55I agree there's room for everybody. I think value acrrues to top one player. I agree. I think you know it's kind of like the inference market where fireworks will be a multiundred billion dollar company and then like I think a genuine bas will be a hundred billion company and then together and a load of the others will be 50 and which is amazing hugely hugely amazing valuable companies but you would then think that open AI would be the place where value accrrews for voice AI right that's what you would have thought 3 years ago and that's not what ended up happening so you can't not
29:24go into the race because there's a big incumbent well I think with all candid that's because of incredibly poor management I agree.
29:32and like that that was theirs to take and they fumbled the bag across every every company in the world no matter how exceptional the leadership team is niches get fumbled right so voice AI was a niche for open AI right LLM are the core and by the way they also fumbled AI coding now they're trying to cash because it's such a big space all respect to patricki I think they're absolutely amazing and I love working adjacently to them I just don't think they're going to fumble back that's my trouble but they have so much in their net right now.
30:01That's true.
30:02And so much is getting added to the net constantly. That's true.
30:04And so you just you have to like go where the football is going. Yeah.
30:08And so I think that it's too expensive for speech not to be playing in B2B as well as playing in B TOC. The best way to lose is not to be in the race. Be in the race.
30:17In terms of the products that we build, we were chatting earlier and you said that every startup say has to be a compound startup.
30:24Can you talk to me about that and how you think about that?
30:26It's not that every startup has to be a compound startup. It's at a certain point you can't afford not to be that.
30:32Do you not think there are a few companies that are just absolutely [ __ ] running rings around everyone else?
30:37Yeah, absolutely. Those are the winners, right? 11 Labs is an example. Um, Anthropic is an example, RAMP is an example, Speechify is an example. Um, all the companies that have absolutely maniacal leadership teams and engineering teams, like that's why people care about team more than almost anything else because the right team will iterate fast, get there, and then figure it out. And now when everything can be turned into a reinforcement learning problem where you can have long horizon agents and orchestrating agents thinking about the problem for like two
31:07weeks at a time. If you set that up, of course you're going to win.
31:10I got into a lot of trouble as I always do with most of my social posts. Uh I used to be quite a sweet little boy actually. No, really. I used to be the Harry Potter Capital and now I'm more like Yeah, you lost the glasses.
31:20Lost the glasses and kind of became more like Piers Morgan if you know Piers Morgan in the UK. Highly despised figure, very opinionated. Um but um a question that I have is like I said if you're a startup it's never been harder to hire great talent because open AI and anthropic candidly have such a carrot reward mechanism in front of you especially with impending IPOs um that the best talent just wants to go there and talent follows talent and you're
31:49seeing the [ __ ] founder of Monzo a multi-billion dollar bank in the UK go there from YC as a partner Matt Clifford you know, the founder of EF, which is a multi-billion dollar company. I mean, he should be [ __ ] prime minister and he's going to join Anthropic. Am I wrong that this is the hardest time ever of startups to hire because the prizes of Anthropic and Open AI are so great? My favorite type of person to hire is a CTO of another company. We have when we were 21 people at Speedify, 18 of the folks at the company were
32:19previously either CEO, CTO or VP of engineering of their last company. Anthropic I have never seen a company like this hires so many CTOs of publicly traded companies and other successful startups.
32:29Workday one of them.
32:30The reason is they build the they built the best most beloved product for engineers in the history of the world.
32:39So it's easy to hire CTOs. By the way, they hire much more CTOs than CEOs because CTOs are the ones who get the most excited about this product. And like you're right, they're the fastest growing company ever, especially at the scale that they are. So they're going to keep growing. Open AI is going to keep growing. Uh you had this like very condensed period like fireworks of growth in both of those companies. Yeah, it's very hard to hire. But remember, they're hiring people that their annual compensation needs to be $15 million a
33:06year minimum. What startup is hiring someone and paying them $15 million a year? You're not like dear seed founder, that was not something one that you were going to hire. And so I will push back against it. The competition for growth stage companies hiring exceptional leadership talent is more difficult. For seed companies, I would say it's the easiest time ever because the impact of even just the founder on their own is bigger because they can orchestrate agents. But the same thing for hiring.
33:33So one thing that we have changed about our hiring in the last even 6 months is we really cared that you read a ton of textbooks about software engineering and that your handcrafted code was amazing.
33:45I still care that you read a lot of textbooks about software engineering and you understand it. But the thing I care about the most today is technical aptitude and just like raw technical intelligence because I know that we could teach you everything else and in 6 months you could be a machine. And so we hire a lot of math olympiads and lee coders and like Kaggle award winners and people who like studied physics and math like they might have even not coded before because I just need the hunger
34:13and the work ethic and the intelligence and anyone can become so good so fast now. And so the pool for hiring exceptional talent is bigger than ever before. And Dualingo did this really well. They love hiring college grads and then coaching them. And so I wouldn't say that it's harder to hire than ever before for seed companies. Seed companies now almost anyone can be someone that you hire if they're smart and hardworking because you could teach them very fast. Um what is more challenging to hire is for growth companies cuz you're fighting with just absolute juggernauts
34:42and you're not a growth company.
34:43So it's challenging for us. Why do you think it's hard to hire a really good salesperson?
34:47I totally get that and I completely agree. I will see CRO packages in the 50 million plus range by the way.
34:53Yeah, exactly.
34:5315 15 is like kids play. By the way, by the way, with the greatest of respects, I will even see $15 million on the table for comp packages for seed companies today. That that is that is the dislocation that I think with the greatest of respects.
35:06Wait, wait, wait. Sorry, sorry. But so this is a seed company that's has raised how much money at what valuation?
35:11Uh well, I mean you've got to understand a seed round today will be 150 200 million and there are several of I mean there's 30 40 companies that at seed have raised 100 to 300 million and this is a company of like a guy who's like one year out of university.
35:26No, no, no. This is a guy who's probably spent four years at OpenAI or spent four years at then. What about the company that's like you know the guy who's been in university for like 2 3 4 years and now they're starting a company or do you think that those people are out of the water now?
35:38No, I I think that that's just a very different world and so yeah they'll they'll raise $10 million seed rounds.
35:43Yeah. So for the company that you just described, they raised a seed round at 150 valuation and they raised I don't know $20 million.
35:50No, I said it was 150 million raise.
35:53Oh, I wouldn't call that a seed round.
35:57But my point is, and that that's my point though, which is like the talent is concentrated. The people who really [ __ ] get AI and systems and have seen the magic inside open AI anthropic. I agree with you that if you have a company that's raised $150 million from$500 to $2 billion valuation definitely that company should give $15 million comp package there's a lot of them yeah that makes perfect sense and but there is a lot of them there's 30 and those 30 take 30 people and there
36:25is a thousand people now that is [ __ ] hard and so but what you just described is exactly what used to happen with Google and Meta let's call it 6 years ago which is if you were really cracked there was essentially a maximum amount that you can get paid at a company like Google or Meta.
36:40And the best way for you to make a life-changing amount of money is to go to a company that is small and ride from the beginning all the way through and be a really solid founding engineer at that company.
36:50I think people want more certainty of cash today than upside, which sounds No, I I think that the equation is the same as always, which is each person has their own equation in their head of how much certainty and how much risk they're willing to take.
37:01It hasn't changed. It's the same. Humans are still humans.
37:04But I think people would rather know that the certainty of a $10 million from Anthropic versus a 60 from that quirky startup they could make. This is the reason why companies IPO, right? There's two reasons. Either you want a ton of money or you want a lot of credibility in B2B like Zoom did or you're hiring and the value of the package that you offer is so much better when your stock is liquid.
37:26When we look at that dev team for you today, you said, "Hey, I wanted to go in. I want to see how we're orchestrating agents. What did you find?
37:33What did you learn in that discovery process around agent orchestration internally?
37:38So inside of our AI research team, everybody's orchestrating agents. It's when you go lower, not lower, if you go then into the product facing things that we build, for example, the platform team or the iOS team or the Mac team or the Chrome team or the web team or the Android team. These are super smart folks who have been working in those domains for like 10 years and they know iOS like the back of their hand. They know cotlin jet brains like the back of their hand and so it's very easy for them to hand code things because you're not dealing with something that's like
38:07super super new. So why change people you know it's hard to change right um and so you just need to force them to change. So one the best thing is to inspire. So you do a Zoom screen share and you show them how the best engineer in the team is orchestrating agent and they're like oh wow I didn't know you could even do that and then you go yeah like please do it. You recommend uh blog posts for them to read books for them to read Twitter team using claw code cursor codeex cursor and cloud code those are the two most popular. Yeah it's a little bit of
38:37codeex usage. It's not that big. I would say cloud code is number one then cursor then codeex. We want you to use as many tokens as possible in whatever harness way is the best for you. Um, you mentioned linear. Linear is amazing.
38:49Like automatically cutting tickets from linear is fantastic. And just like being able to go into your agents and be like, "Okay, I have these like six linear tickets. Start on them." And then really a good engineer today is just an exceptional QA, right? The AI will make them feature. You will test the feature, see if it's good. You'll figure out where the edge cases are. You'll prompt it to fix it. And then you try to make it as efficient as possible, which is hard to do. And then you need to make essentially like you know roughly 10 really good product and engineering architecture decisions a day.
39:18How do you think about token allocation internally? You know we we've seen leaderboards be used which is I think the most [ __ ] up form of incentive kind of playing. Uh you don't want to like prevent there's a lot of people who are a lot of talk and I'll ask for examples and I'll be the examples and like you know I'm doing this, I'm doing this, I'm doing this, I'm doing this and then you look and I'm like eh. And so I think about it in terms of demos. Can we hop on a Zoom call and you'll show me what you built and then I use it myself and I'm like,
39:47"Wow, that's amazing." Or you send me a screen recording of a feature or technology that you built and I'm like, "Wow, that's so good." And so we give credit when things get shipped to production to users. So even inside of the AI team, if you build a really amaz and this is part of why speech ended up winning, you asked, "How did you build bigger labs?" The answer is we ship to production all the time. That's how we won. We are not in the theory space. We are an applied AI company. That's why we win. And so if you're an engineer at Speedify, the analogy I always give people is imagine that you are in the milk delivery business and you make me a
40:16beautiful bottle of milk and you leave it down the road. The milk will spoil. You have to get it to my door. Knock. If you didn't do that, you get no credit.
40:24If you carry the football all the way to the line, but you don't cross over to the end zone. If you don't kick it into the goal, you get no credit. If you bring the ball just to the rim, you don't put it in the rim. You get no credit. And in the rim means push to production with no bugs. And users are actually using it. and then we get feedback. How many companies do that iteration cycle fast? Almost no one.
40:42Definitely not with the user base number that speechify has. And so in the AI team at Speechify, you make some amazing discovery. We're like, great, push it to production. And then you go, oh wait, there's this QA problem and this QA problem. And if you have this many people use it on the AI serving layer, then you have this other issue. Cool.
41:00You get no credit for me. It's not in production. I can't use it on my phone.
41:03When I can use it on my phone, I will give you credit. And so this morning, actually, not yesterday, yesterday, I had a call with our AI engineering team and I said, "Listen, the project that we have running for duplex models and for AI conversational harnesses is something I'm really excited about and it's been moving fast. I want it to move faster.
41:19Here's like 14 notes that I want." And then what I do always is I'm on a Zoom call, I flip my computer around to face my phone, and I use the product in front of them, and we record it. And so then they see all the bugs. And then I send the recording in the chat. someone on our team, he's 19 years old, sent me a demo this morning off of that conversation that solved all of my problems. And he was like, "Hey, I was waiting for like three training runs to finish, so I had a little bit of time while I was waiting. So, I implemented everything that you asked." And it blew
41:47my mind. It was so good. That's using AI correctly. So, it's not a token leaderboard. It's what did you show in production that was good. How many companies do you think are actually as tokenpilled AIcentric as we think in terms of devs?
42:04There's a guy Jason Joerger who used to work at Speedify and now he has uh my tech CEO on Instagram. He's super funny and so he makes a lot of videos about like you know crazy CEOs who all use tokens use tokens. I think all founders in some way have that animal inside of them because you know that it's the right path. But there is a difference between reality and theory and you need to make sure that you don't overdo it.
42:30Do you have any price sensitivity on tokens?
42:32Yeah, of course.
42:33Absolutely.
42:34I mean I'll lose my mind if to implement a tiny feature you use 15,000 tokens. Like why did you do that? And like we will let people go if they just go bananas with something for no reason.
42:45Are you able to accurately budget tokens on a not accurately but within bounds?
42:51Um the other thing is like a lot of engineers are look you go into engineering because you like optimization. Most engineers are not blind and it physically hurts them to overspend tokens. Um, and I again I always think that the best way to interact with AI is you are chatting in the chat or actually doing it verbally and you're essentially pseudo coding with your words constantly and you're explaining architecture and a great example would be um
43:20uh I know someone who uh has no engineering background and they wanted to build an app and they built exactly what they wanted. It took him two hours.
43:29Uh, but they needed an API call and they need to scrape this website and they basically scraped every single page of the website, every single part of the website. And so the bill that they got for the scraping was gigantic. And then I was like, why are you doing like that like that? Why aren't you going into the database to this exact URL and then scraping that from the URL? So the amount of nodes they needed to hit became like 20 instead of 25,000. And so an engineer will spend their time making sure that the thing is optimized like that. So that's how you build like a good database or a good architecture
43:58system, whatever. You do the same thing when you're interfacing with the agent. You want the agent to take the path of least resistance, not the path of most resistance.
44:05I think one of the biggest problems is that agents are goal seeking. And so they are like it's all about the target. You need to be good at picking the right target. And uh I think anthropic published this paper um when Fable 1 came out about long horizon tasks with Fable. So, the first thing is it was much better at like running a two-eek task and it could burn $12,500 worth of tokens in two weeks and basically make a better model with that.
44:31That's a perfect amazing way of using tokens. That's exactly what you want. And what you don't want is burning 12,000 tokens in the span of 5 hours doing something that's like totally unnecessary and doesn't make any sense. You need the loops to happen and then you need to check the result. So what you want to build and Boris who's the investor of cloud code talks about this all the time. It's all about the loop.
44:53You say here is the target. Here's how you measure the target. Now iterate against the target over and over and over again until you get it.
45:00What did you not know about building an AIcentric dev team that you wish you had known?
45:06How useful is it to own your own GPUs?
45:10What was that realization moment? Just did you see a build one there? The realization moment was when we realized that we had a really talented engineers who were essentially moving at 17th of the speed they could have if they had the compute um onetoone with their creativity and ideas.
45:29How if you're a founder listening to this, how should I change my hiring process in a new AI world?
45:36Number one, functional interviews. Build this and then you see if they can build the thing and then you run it through unit tests. The second one is give them a large codebase uh even an open source repository and have them understand the codebase make changes and then check what they broke and then yeah like they have to be able to orchestrate agents well and if they're not doing that it's kind of not worth to have the person then then the next thing I'll say is it is more fun to have a smaller team like having a big team is great as long as everyone's carrying their weight um but the way I kind of think about it is yes
46:06I can have multiple agents running on my computer or I can have several Slack chats with really smart people who are bigger domain experts person I am and basically that human being is the outcome owner for that task and they have the agents and so I can run as a founder multiple projects at the same time to a really amazing level of granularity and so I think about moments earlier in the year when my brother Tyler would literally have an alarm to wake up at 3 in the morning because he needed to check what the agent was doing at 3:00 in the morning and then you wake up make sure it's good go back to sleep
46:34like you want to babysit your agent basically every 3 hours and the beautiful thing now is you can go work out and the agent will tell you the answer and then you like voice not know it back with speech ify what you wanted to do next and like that'll happen and so you want people who are essentially that level of addicted obviously that creates massive AI fatigue so make sure your teams don't burn out um but you want someone who is that level of excited and so I think hiring for slope more than intercept is more important today than ever before said another way I look for the potential the person has more than I look for where they are
47:02today when I look at whisper flow and willow and I did this tweet and I deleted it because I don't ever want to be sulky and miserable and it's an amazing thing to build a company and you should be incredibly credited for doing so as an entrepreneur, but I found Whisper Flow's product was just getting worse. Um, and I said it on Twitter just cuz I honestly just wanted alternatives.
47:25I I really need this product and I wanted alternatives. I got 500 different alternatives and I was like, [ __ ] talk about the commoditization of a market that is not one that I want to be in. Can you help me understand? Have we seen a complete commoditization of that whisper flow willow speech to text for productivity?
47:44What they came out to the market with first was not necessarily their own model. Part of the reason they got worse is they switched their own model cuz it's a lot more affordable. Um and so they had a harness that ties together a bunch of other things. Probably it was DL under the or deepgram under the hood uh with a bunch of optimizations and more well now they're trying to do notes and they're trying to move more into I think they're being successful with it. Yeah.
48:05100%.
48:06Yeah. So that that like that's to your point of the compound startup. Uh one of my uh biggest when you look at them do you not reflect on your we said before not announcing fundraisers not announcing anything they've announced going to the bathroom um and hence they have a I would say a bigger brand uh not in terms of users like if you walk down the street in New York City way more people will know speech than know whisper flow just by virtue of the fact we have way more users. Um, but in the tech world, way
48:35bigger brand, right? Investors know who Whisper Flow is because they announce. We intentionally don't announce, but we don't have any competitors. Who are you going to use instead of Speechifi to do text to speech for your models? Like the closest thing is 11 Labs and we're like so much bigger than 11 Labs for BC.
48:49Gradium.
48:50What's Gradium?
48:51Brazilian company does text to speech competitors to 11 Labs.
48:55Okay. But they do B TOC.
48:56Yeah, I know.
48:57Yeah. So that's there's unlimited numbers of companies doing B2B text to speech, but like we are unique in our market. So because uh uh whisperflow was so public about it, they now have a lot of competition and so Peter Teal right only losers compete try to not compete and so yes they what happens to that market whisper flow take majority and then there's thousands of ankle biters I don't know I mean I want to market right I think that market becomes igopical as well um obviously and this
49:26by the way I think is another mistake that I made I built my own speechto text uh experience experience that I've been using on my computer for the last like 7 years. Um, sideloaded on my iPhone and on my computer, but I figured it's a commoditized product, right? Apple's going to release it instead of the button. It'll be great and like there you go. But Apple keeps not doing it. If you remember, two years ago, Apple announced a partnership with Chacht that will improve Siri. Nothing happened. And so that's also the reason why I never went after Siri. And so now we've launched a product to compete with Siri
49:56and we've launched a product to compete with whisperflow and we launched a product to compete with open uh with 11 labs because I learned a lesson that I should have learned before which is the same lesson from 11 labs. The way that you win is you offer an excel excellent product for free and then you have a wedge and then you add more and more and more things. So I don't know what happens with the whisper flow space. I just know that if you're a founder you should also always try. Final one, another one they get in trouble for, but I stand by strongly is I just think the customer support market's a challenging
50:25market to really get behind. Yeah, you have Sierra and Dagon out in front with the majority of funding and attention.
50:31But to say that there are 18 companies that have now raised over 100 million in the last 18 months. Uh there is kind of what I call like the mid tier which is like your intercoms and your tor desks and your crescendos all these ones where it's like you know they're not old but they're old enough 8 to 10 years old and they're pretty good and then you've got Salesforce Atlassian and the much older ones and then the worst thing about this market is that for any sophisticated
51:00buyer an airwall a cler a nan a technology facing company everyone has built their own.
51:06Yes.
51:06Because they need a sophisticated Why would you pay a tax for it?
51:10So why what am I missing?
51:13Yeah. So the first thing you're missing is the core product that we're offering B2B is the API, not the agents, right?
51:18So Sierra doesn't have their own uh model team. They use other people's models, right? Because the value of Sierra is the go to market. It's Brett Taylor. Um and so that's why if you talk to Monty and Patrick, they'll tell you we're not competitive with Sierra because their main business historically has been the API. So that's the first thing in the API business. You have Speechify, 11 Labs, Gemini, uh Gro, so SpaceX is now in the race, and uh Cartia, and that's kind of it. Um and so that's not that competitive of a space
51:47compared to Yeah. the B2B um uh customer support thing. Everybody's in that space. Finn, everybody. And so I'm not building that product.
51:55What can I offer you that's 10x better than the next person? Not much. And so in the core API side, I can offer you better quality, faster speed, and 10x cheaper, good offering. But then I have to also offer agents because there are so many pockets of value that have not been unlocked. And unless I am, again, I have this model for leadership. You don't want to be a fat manager who is like a general sitting in the back saying, "Take that hill." You want to be the warrior who runs up with their sword and engages the enemy first. You need to be the same thing with your product. You
52:25need to be the number one user of your B2C product. And you need to help your customers use your product better. And if you do that, you will learn their problems and then you will figure out what the next product is that you need to offer them. So unless I have front deployed engineers working with my B2B customers, building agents for them using our technology, I will not figure out what the really amazing next innovation across the hill is. And so you mentioned the right thing, which is 11 Labs now has all these partnerships with governments.
52:53Governments is not exactly customer support. They would have never gotten to governments had they not done a great job on the private sector first. I agree. 11 Labs is in addition to OpenAI is the most integrated company right now, AI company with governments. That means they figured something out, but you got to start in something like customer support. Now, we support the models. So, if you're a person building a customer support product and you're a CFO who's frustrated with the size of your 11 Labs bill and you want to cut it
53:20by 10x, go to speify.ai AI um and or hit me up cliffspeedify.com. I'll give you some discounts. Um but you you again if you're a founder, you need to try. You cannot not try. You cannot give up before you're even in the race.
53:34What will be a bigger company in 5 years, Sierra or 11 Labs?
53:38Brett Taylor has the best resume I think of anyone in the world. Right. I think he started Google Maps, then he was CTO of Meta, then he was co-CEO of Salesforce. He's on the board of OpenAI, and now he founded Sierra. I would never try to fight Brett Taylor. And I think the field is so large like no like we don't understand how big the space for AI agents is. Like not even like AI voice agents. Not even close. In the same way that people didn't understand how big the field was for LLMs in 2019.
54:07In the same way people didn't understand how big the space was for AI coding agents in 2021. Like this is the next huge space. And so both those companies are going to be massive.
54:15I think they're playing very different games. Right.
54:17I think I think Brett Taylor's actually trying to recreate the next generation of Salesforce. He he is absolutely not playing the customer support game. He's moving into pre-sales. He's moving post sales.
54:29Neither is 11 Labs. 11 Labs has a product that also does customer support, but they do everything else too. That's why I call it AI agents, not customer support.
54:35But I think building a very opinionated voice centric company. It's voice and I think I think Brett Taylor is doing all of it. Put another way, if you use a tool like Sierra, the wedge right now is voice, but the important part is tool calling. 11 Labs lets you do some tool calling, but that's not the bread and butter. There was a really good presentation that Brett Taylor did a screen share of him building a guitar store on Shopify and how he uses Sierra to do customer support and sales and
55:05everything else. It was extremely impressive. If you haven't searched this, you should search this. Brett Taylor is a big guitar guy. Um, that is a very different product than what Level Labs is doing. And so they're both going to crush. I agree with you in the Sierra uh uh conclusion.
55:19What crazy thing today will be incredibly And this is a quick fire, my friend, cuz I could talk to you all day. What crazy thing today will be very common in 5 years time? You know, before it was like find your partner online. Duh. No. Weird. Put your credit card online. [ __ ] no. What today is uh no. and in five years time we'll be like yeah of course human computer interface is going to become primarily voice as opposed to a
55:47screen so part of the reason why Google succeeded it is is a very simple interface there's a text box and a button that's it anyone can learn how to use it the reason why chat GPT worked as opposed to GPT3 is because it was also a very simple interface just chat there's a text box and a button you get a response that's it the simpler version of that is just having a conversation I say something I hear something in response If you use voice AI from CHP right now, it sucks. It's too slow. The LLM is much
56:16dumber than the core LLM. The escalation to the higher quality LLM is pretty weak. I think what will happen, and Meta has the right idea, by the way, so go Chris Cox, is people are going to be talking to their computer and phone and some wearable constantly throughout the day and using screens a lot less.
56:33You can buy one, SpaceX or Meta. Which should you buy?
56:36Meta.
56:37Why? Elon's distracted.
56:40Is he distracted or is he building full stack? Because actually I think he's never been more strategically positioned and he has an outlet for each of the different products that he's built and each one feeds the next. When you look at Zuck and Meta, you know, bluntly the compute spend that he's producing the outlet is increased conversion on an ads business which is the biggest ads business in the world. So 7% on $240 billion is a lot of [ __ ] money. Yeah.
57:05But it's actually not in the same quantum league as doing space data centers.
57:10Yeah. So let's take the space data centers out for a second. I think the space data centers is a very interesting idea. And what it does really well is it lets me underwrite a gigantic TAM for my rest, right? And so that's like that was a great rabbit out of the hat by Elon in order to pitch investors really well.
57:28Let's take that out for a second and I'm going to talk to you about SpaceX and Tesla like they're one company because really I'm assessing Elon. I'm not assessing every like you know SpaceX is an individual stock. Um you know for data centers the biggest constraint right now right now is memory cards and then very soon is going to be energy and it's energy a lot of the times. So what do you need for energy? You need energy supply and you need energy storage. And so the best energy storage right now actually comes
57:57from Tesla. Tesla also has a chip manufacturer that they're doing basically competing with everyone else and like that's going to do really well.
58:04And if you saw that uh Joe Rogan interview with Elon maybe two years ago, he was explaining that the hard part is not building the product. The hard part is building the manufacturing for the physical product. So Elon is number one in the world for manufacturing complex items like that. So like that's very exciting. Um and so the TAM for Elon's companies are bigger. However, I think the Meta Trades, what is Metatrade at right now? Less than SpaceX.
58:26It's less than SpaceX. It's It's [ __ ] And so I think Meta has more data than anybody else in the world. I think Meta is actually super hampered by laws like GDPR. Like if GDPR didn't exist and the other laws in the US didn't exist, Meta would be ripping.
58:39They just can't train on their data properly. And so they'll figure that out at some point in some way. I don't know how, but I believe in Zuck. And at the end of the day, I'm a huge believer in founder companies. And so both we're talking about two of the best founders in the world. And the last thing I'll say, look at Zuck's age and look at Elon's age and Zuck's not going to stop and Elon's not going to stop. But at a certain point, one of them will expire.
59:00Um, and so Zuck has like 20 extra years. And so depending on how long you're investing, I'm younger than Zuck. Let's see what happens.
59:07If Zuck expired, Meta's stock price would increase.
59:10What? I disagree completely. No, because you'd have a CEO who comes in and understands that and this may be a short term, but that saying we're going to invest more and more and more and more and more and more and more in capex when we don't have an outlet for it.
59:23You'd actually see a stock price appreciation in the short term. Every time Zach steps out on the podium and says capex, capex, capex, he's like [ __ ] hammered for it. Say that's why Meta is a good investment right now because what Meta doesn't have is what Palanteer has, which Palanteer has the Alex Karp effect. Alex is really good at pumping up the PE ratio of the stock and Zuck I agree is the same as Elon. It's the Elon. Exactly. If Elon were to be removed 70% of that value.
59:49Exactly.
59:49If Zuck's removed, you definitely don't lose 70%. You maybe lose I don't think you lose anything. I think you get an experienced exec who says we're in our business. And Charlie Mer and Warren Buffett actually no it's Benjamin Graham has this concept of the cigarb butt, right? like what's the intrinsic value of a company and they approach it from an accounting perspective. I think about it as from an underlying uh technology and business perspective. So the underlying asset, the intrinsic value of Meta is so large in relation to how it's valued in the
1:00:18markets today. And you're correct. What's the PE ratio of Meta? 32 something like that. And SpaceX is insane, right? Tesla is also in multiple hundreds. I I think that there has to be a correction that happen unless Elon succeeds with a big big big big vision in which case then he wins.
1:00:35Final one for you. What are you most excited by?
1:00:39We talked about Jeff Dean. I'm Jeff Dean. Yeah. Uh I'm most excited by applications of AI to pharmarmacology and biology. So uh I have a family member who has very severe autoimmune neural inflammation. He's had it for 6 years. I took a blood sample from him every week for 15 weeks, sent it to a lab, sequenced his genome, uh, did proteomics on it to figure out how
1:01:07the proteins are expressing in his body, and run an RNA analysis in each one of those weeks. And then I compared that to self-reporting data on what the quality of life is and what his mood effect was.
1:01:18uh every day I have like six years worth of data on him and ran it on a GPU cluster and like I found so many things that no doctor could ever tell me and he has a very rare disease. It was called like an orphan disease cuz there's not that many people. There's a Facebook group for this disease. I'm buying now uh basically like you know it's like a $5,000 device you can fit in your pocket but if you put uh a piece of hair or saliva or blood into it, it can sequence your entire genome. And so I'm organizing meetups with all the people who have this disease to sequence all of
1:01:47their genomes and then compare them all on a gigantic GPU cluster to figure out what epigenetic common thread there is between them. And I am I know I'm going to solve this disease.
1:02:01I would have never had an edge to do that in the past. And like it gets even more beautiful because I can then take all the conclusions that I have about it and put it into alpha fold from isomeorphic and I can design not just the protein that is creating these issues but I can design the molecule that needs to bind to that protein to either turn it on or off. I can use crisper to do the same thing. I can use a lab like Twist where I can tell it, I want you to make me this RNA sequence or this DNA sequence and it can make it for me and ship it to my lab or my house and
1:02:30I can create amazing outcomes with it and I can simulate all of it on my computer that's SSH into my GPU cluster in Scottdale, Arizona and I can cure my brother. And so my experience is I'm a kid who when I was 8 years old, I couldn't learn how to read and my dad had to open a book and read Harry Potter to me and that's how I learned how to read. And then when I was 13, I moved to the United States of America and I didn't speak English. And I listened to Harry Potter audiobooks 22 times in a row and I still had the first chapter memorized. And then I couldn't get into the private high school that my brother
1:03:00went to and that my sister went to and I was really bummed. I went to like, you know, a lower quality high school, whatever. And I didn't get into AP US history because I made a bunch of spelling mistakes in my essay and I couldn't read the passage in time. And I needed to train myself to read the SAT English portion. I wouldn't read the passage. I would read the answers and I'd go and hunt for the answer. And then when I got to college, somehow by the grace of God, I ended up going to Brown and like starting a major for renewable energy engineering because I couldn't do literature. And I built a text to speech tool that would read out all my books to me. And that's why I graduated.
1:03:30Technology solved my dyslexia and it solved my ADHD. And it's going to solve my brother's disease. And it's already solved my dad's prostate cancer because I figured out with a bunch of help from other people how to use GPUs to identify where in his body the lesion was. That's what I'm excited for is there's better quality of life for literally everybody because you have this magical machine that can run a trillion operations per second on as many GPUs as you want and it can solve problems that we can't. I find it staggering that still today we
1:03:57have orphan diseases which is like oh there's too few people to make it economically viable for us to try and solve and there are hundreds thousands low thousands but low thousands and again it's the same thing you just need data you need compute and you need to ask good questions like I said 10 good decisions per day either hypothesis or actual product decisions and you can solve these problems freaking amazing cliff it's been so great to have you on the show I I much prefer when it's a discussion. Um, it's been an amazing
1:04:27discussion. So, thank you so much for putting up with me.
Insights
20VC with Harry Stebbings
https://www.youtube.com/channel/UCf0PBRjhf0rF8fWBIxTuoWA
- Brett Taylor’s Sierra Shopify screen-share presentation (presentation/video; exact title and URL not provided): Cliff Whitesman, Speechify’s founder and CEO, recommended a presentation showing Taylor building a guitar store on Shopify with Sierra and using it for customer support, sales, and related work. Whitesman called it “extremely impressive” and told listeners who had not seen it to search for it.