# Everyone Is Still Undersizing the AI Market | Eric Vishria

Invest Like The Best · 2026-08-11

<https://iltb.podhood.com/5444991a-2aa4-4965-8109-339bd8d791e2>

Eric Vishria, General Partner at Benchmark, argues the AI market is still being undersized: just as AWS didn't eat all of software, one AI lab won't consume everything, and he expects an oligopoly plus $100B 'smaller winners.' He explains what Fireworks taught him about how hard AI inference is (5x performance gaps versus cloud providers), how Sierra treats software as sandcastles that must be rebuilt as models shift, and why the SaaS competitive frontier has moved—every day a company hits its old plan it destroys value. He also tells the Cerebras story from 2016, the energy bottleneck to intelligence, the need for 'AI Sherpas' in enterprise, and his three founder tests before investing. Benchmark's new growth fund reflects that high cash-on-cash returns now exist beyond early stage.

## Questions this episode answers

### What does the history of AWS teach us about the AI market?

Eric Vishria says AWS’s launch in 2006 looked like a commodity to smart investors, yet by 2014 the fear was that AWS would eat all enterprise software. Instead, Snowflake, Confluent, Elastic, Mongo, Databricks, Datadog, Azure, GCP and Cloudflare all became winners, including multiple $100 billion companies. He concludes the cloud market was too big for one vendor, which rhymes with AI today.

[3:49](https://iltb.podhood.com/5444991a-2aa4-4965-8109-339bd8d791e2?t=229000)

### Why does hitting their plan destroy equity value for SaaS companies in the AI era?

Eric Vishria says the competitive frontier for SaaS has shifted completely. Hitting a preset plan was how managers built equity value, but now every day that a company faithfully executes that old plan, it is actually destroying equity value. He urges CEOs to invert their priorities, making AI the main job rather than an afterthought; winners are nimble about what they optimize against.

[17:56](https://iltb.podhood.com/5444991a-2aa4-4965-8109-339bd8d791e2?t=1076000)

### What did Eric Vishria learn from Benchmark’s investment in Cerebras?

Eric Vishria says Cerebras taught him the naivete required for hardware investing. In 2016, the founders argued GPUs actually suck for deep learning. He understood three levers: more cores, better communication, and memory closer to compute. But hardware is brutally hard; parts came back in 2019, and he watched a chip melting after raising $500 million. Still, AI is a giant workload that can create $100 billion chip winners.

[32:23](https://iltb.podhood.com/5444991a-2aa4-4965-8109-339bd8d791e2?t=1943000)

### What questions does Eric Vishria ask himself before making an investment?

Eric Vishria says partnership and chemistry matter more than an investment-grade opportunity. Before investing, he asks whether he could honestly tell someone he cares about that this could be their life’s work, and whether he would pick up the phone if the founder called at 9:00 p.m. on a Saturday night. He also asks whether it matters if they are right; if nobody cares, it won’t build enough equity value.

[57:50](https://iltb.podhood.com/5444991a-2aa4-4965-8109-339bd8d791e2?t=3470000)

## Key moments

- **[0:00] Opening**
  - [0:00] “Every day that you are hitting your plan, you are destroying equity value,” says Eric Vishria on AI-era SaaS companies.
- **[0:48] Fireworks**
  - [1:18] Fireworks runs open-source models 5x faster than hyperscale clouds on the same NVIDIA hardware, says Eric Vishria.
- **[3:36] Cloud war**
  - [3:36] Cloud history shows why AI won’t be winner-take-all: experts called AWS commodity in 2007, then Azure and GCP became giants, says Eric Vishria.
  - [5:44] Snowflake out-Amazoned Amazon by running on AWS to beat Redshift; Datadog built a $100B business against a cloud offering, says Eric Vishria.
  - [7:06] “What if it all works?” — Eric Vishria on why the AI market is being undersized, like cloud was.
  - [8:04] AI is scaling faster than cloud did, so expect an oligopoly of winners plus smaller $100B companies, predicts Eric Vishria.
  - [8:54] Enterprises avoided cloud in 2010 but now chase AI, so startups should be their 'AI Sherpa,' says Eric Vishria.
- **[12:52] Sierra**
  - [13:18] Sierra’s Brett Taylor calls AI-era software 'sandcastles' that wash away; winners embrace replacing their own product, says Eric Vishria.
  - [15:35] Product management has inverted: PMs must understand AI capabilities’ jagged edge, not just customer problems, says Eric Vishria.
  - [16:26] Winning AI builders need customer-problem sense, taste, and jagged-edge curiosity — job titles don’t matter, says Eric Vishria.
- **[17:35] SaaS frontier**
  - [18:14] Database moats are collapsing because AI excels at well-specified migrations; the competitive frontier has shifted, says Eric Vishria.
  - [21:03] SaaS companies grew 4x but now trade at 6x revenue vs 30x in 2021, leaving them worth less, says Eric Vishria.
  - [22:37] Ann Lee Skeitz’s advice to CEOs: invert 8-to-5 core work and 5-to-8 AI work, says Eric Vishria.
  - [25:10] Old-school leaders flame out at scaling AI companies because they cling to plan-execution and quota-capacity models, says Eric Vishria.
  - [26:11] AI sales reps are closing $10M-$30M instead of $1.2M quotas because magic products pull demand, says Eric Vishria.
  - [28:11] The best AI salesperson is the founder, bridging the model’s jagged edge to each customer’s capability, says Eric Vishria.
- **[28:28] Energy bottleneck**
  - [28:52] Demand for intelligence looks unlimited: the smarter the model, the more demand it creates, says Eric Vishria.
  - [29:33] China is bringing on 10x as much energy as the US next year, and energy will bottleneck AI tokens, predicts Eric Vishria.
- **[31:28] Cerebras**
  - [32:23] Cerebras pitched in 2016 with 'GPUs actually suck for deep learning': a wafer-scale chip with 450,000 cores and 20GB on-chip SRAM, says Eric Vishria.
  - [35:02] Hardware design gets you 2% of the way versus 80% for software; Cerebras ground from 10% of roofline for years, says Eric Vishria.
  - [36:44] Every compute generation — CPUs, graphics, networking, mobile — created a $100B chip company; AI is next, says Eric Vishria.
  - [39:14] Benchmark made an unannounced bet on a new CPU architecture because LLM-generated code needs a fresh design, says Eric Vishria.
  - [39:55] Q: Did Cerebras make Eric Vishria want to do more hardware investing? 'Fuck no' — the chip was melting in a 2019 board meeting.
  - [42:15] “If it doesn’t work, it’ll be for all the reasons your partner said; if it does work, those reasons didn’t matter,” says Eric Vishria on hardware naivete.
- **[45:10] Robotics**
  - [45:42] Robotics lacks an internet-scale training set, so Sunday Robotics is chasing high-value data to bootstrap models, says Eric Vishria.
  - [48:23] Sunday Robotics uses custom gloves matched to robot hands and vertical integration, following the Waymo/Tesla playbook, says Eric Vishria.
  - [50:17] In a Stanford basement, Sunday Robotics showed a dozen robots folding arbitrary laundry with rigorous evaluation — 'this is happening,' says Eric Vishria.
- **[52:53] Board partner**
  - [52:53] Eric Vishria says he is 'an investor second and partner first,' only backing founders with real chemistry and mutual-learning potential.
  - [55:08] Benchling had no churn line for six years, then absorbed seven years of churn in 12 months during the biotech crash, says Eric Vishria.
  - [58:36] Q: What questions does Eric Vishria ask before investing? 'Would I pick up at 9pm Saturday?' and 'If we’re right, does it matter?'
  - [1:01:00] Benchmark raised a growth fund because high cash-on-cash multiples now exist outside early stage, says Eric Vishria.
  - [1:03:06] Turning down a right-intuition AI company because it was outside Benchmark's box was 'obviously dumb,' says Eric Vishria.
  - [1:05:41] “Getting the hole in one, that’s luck. Getting the ball close to the pin is hard work,” says Eric Vishria on increasing your luck.
- **[1:07:30] Going public**
  - [1:07:39] Going public gives companies trust, currency, and capital; most should do it, like college athletes going pro, says Eric Vishria.
  - [1:10:08] 500 private SaaS companies between $100M-$500M are stuck with no exits after AI natives sucked up IPO windows, says Eric Vishria.
- **[1:10:49] Partnership debates**
  - [1:10:49] Benchmark’s big debate: where AI value accrues — labs, semis, infra or apps — and whether outcome-based models replace SaaS, says Eric Vishria.
  - [1:12:04] AI will support CSPs, neo-clouds, Nvidia, chip startups, edge and datacenter inference at once — but most companies in each area fail, says Eric Vishria.
- **[1:13:36] AI jobs**
  - [1:13:39] Jeff Hinton’s 2016 'stop training radiologists' was wrong on timing: data, reimbursement, liability keep AI a co-pilot, says Eric Vishria.
  - [1:16:49] Mass AI unemployment predictions repeat Jeff Hinton’s radiology error — real-world friction delays impact, says Eric Vishria.

## Speakers

- **Patrick O'Shaughnessy** (host)
- **Eric Vishria** (guest)

## Topics

Artificial Intelligence

## Mentioned

AWS (company), Azure (company), Benchling (company), Cerebras (company), Cloudflare (company), Datadog (company), Fireworks (company), GCP (company), Nvidia (company), Sierra (company), Snowflake (company), Sunday Robotics (company), Tesla (company), Waymo (company), Cursor (product)

## Transcript

### Opening

**Eric Vishria** [0:00]
I'm very dismissive of this idea that, like, people are going to vibe-code their own shit. Like, and whatever. Like, that isn't the issue. The issue with SaaS companies is every single day that you are hitting your plan, you are destroying equity value.

Like, think about that. Our whole careers we learned: you lay out a plan, you execute against it, like, relentlessly and violently. You hit your plan, or you exceed your plan, and you keep on building that, and that's how you build equity value.

And now you're going to hear, just like, "Every day you hit that plan." You're fucking up. You're fucking up. You're destroying value.

### Fireworks

**Patrick O'Shaughnessy** [0:48]
I love asking you and all your partners this every time we hang out, which is, okay, you've got these singular investments, you don't do that many investments each per year, and then the ones that go on to work, like so far Sierra and Fireworks certainly are, you get to learn so much about the world through the lens of the company.

**Eric Vishria** [1:04]
Yeah.

**Patrick O'Shaughnessy** [1:04]
So I'd actually love to do both of those, maybe starting with Fireworks. What do you know, or what have you learned about the world and how it's reordering itself, based on watching the world through the lens of Fireworks?

That would maybe be surprising or interesting.

**Eric Vishria** [1:18]
These are 2 trillion, 3 trillion, 4 trillion parameter models. Well, it turns out running those models is damn hard. And running them efficiently is, like, super hard. And the way to see this, and everybody can see this, which is

everybody from AWS to Azure to GCP to the Neo Clouds to the Fireworks-based hunting togethers of the world, like, all of them, they all run these stock open-source models that are available in their developer pools and everything else.

And, like, that's what it is. And the performance difference for a Fireworks versus, like, a cloud provider is, like, 5x. And that is just the speed performance. Then you add on top of that the, like, throughput, which is not visible externally.

It's only visible if you look at, if you know the economics of these businesses. And you're like, wait a minute, this is the same open-source model with the same NVIDIA hardware, and there's a 5x performance difference and a multiple x throughput difference.

And, you know, and the way to just simply understand that is, like, these companies are paying the margins of the cloud providers and running on top and making money.

**Patrick O'Shaughnessy** [2:47]
Yeah.

**Eric Vishria** [2:48]
And so, like, how can that be?

**Patrick O'Shaughnessy** [2:50]
Yeah.

**Eric Vishria** [2:50]
And so what I think my big takeaway on it was, like, wow, this stuff is actually really hard to run. Like, it's just really hard to run. And, um, and there's a lot of expertise involved in doing that.

And it's kind of like this very specific expertise that exists. And it is the kind of thing that when you look at it as an investor from the outside, and we should talk about, like, early days of AWS, but, like, when you look at it from the outside, it's like, this is a commodity.

This is just, like, pass-through resell.

**Patrick O'Shaughnessy** [3:19]
Scale game.

**Eric Vishria** [3:20]
Yeah, scale game, like, whatever. Like, this doesn't, and you're like, oh, wait a minute. No, it turns out it isn't. It isn't at all.

**Patrick O'Shaughnessy** [3:25]
Do you think that's just a moment-in-time thing? And I'd love to just hear you, I'd love to really riff on, like, cloud. You watch cloud very carefully and closely. You know a lot about it. And the adoption curve there versus how people use these things and the nature of those two businesses in comparison.

### Cloud war

**Patrick O'Shaughnessy** [3:38]
It's tempting to say, like, there will be one or two scale winners.

**Eric Vishria** [3:41]
Yeah.

**Patrick O'Shaughnessy** [3:41]
Like, there typically have been in a commodity market.

**Eric Vishria** [3:44]
Totally.

**Patrick O'Shaughnessy** [3:44]
Where cost of serve is everything and scale drives cost of serve down. And, like, that's the whole story.

**Eric Vishria** [3:49]
I just, I think the AWS example is so good. Okay, so 2006, you have S3 and EC2 launch,right? Their compute platform and their storage platform. Those are the first two AWS offerings in 2006. And you kind of start talking about it in late 2006, whatever.

2006, 2007, I think the 2007 annual letter, like, Bezos talks a lot about, um, about AWS and why it's important and why it's interesting and everything else. And the investor reaction is just not good. And I think if you put 30 of the smartest investors at that time in a room and ask them, like, what's the probability that this AWS business is a good business with durable long-term margins and, like, super interesting and everything, not commodity, I think you would have gone zero for 30 with really smart people that you and I know.

**Patrick O'Shaughnessy** [4:37]
Yeah.

**Eric Vishria** [4:38]
Who were around at that time in '07. Okay, fast forward from '07 to 2014. I joined the venture business in 2014. And a really common narrative in 2014 was, oh my God, AWS is going to eat everything. There's no enterprise opportunity left.

It's going to eat databases and infrastructure, but it's going to eat the apps too. And they're going to offer it the cheapest and best. And, you know, and you got to remember, like, we all had these, like, amazing SaaS and software businesses that we were involved with or investors.

And part of the reason people loved them was they were annuities and they ran at, like, 85% gross margins and, like, everything else. And so it was just like, oh my God, AWS, Amazon can offer things at, like, 8% gross margin and, like, they're just going to crush this market.

**Patrick O'Shaughnessy** [5:20]
You're marketing my opportunity.

**Eric Vishria** [5:21]
Yeah, you're marketing my opportunity, like, the whole thing, the whole narrative. And think about from, like, 2014 to now in enterprise, okay? Like, of course, you have Snowflake, direct competitor to Amazon Redshift, ran on Amazon. You're out-Amazoning Amazon on Amazon.

**Patrick O'Shaughnessy** [5:44]
On Amazon.

**Eric Vishria** [5:45]
Okay? But it's not just Snowflake. You had Confluent and Elastic and Mongo, Databricks, all of these companies. Okay, amazing. That's the infrastructure layer. Then you have the whole app layer. In the app layer, like, think about offerings that they offered at the beginning, Datadog, $100 billion company today.

Like, they had a competitive offering,right? And they did it. And, of course, there was tons and tons of roadkill. There was tons of roadkill. They did run over a bunch of stuff. But even then in 2014, the thesis was the view that AWS was going to eat everything was massively wrong, not because of all the examples I just mentioned.

It was massively wrong because Azure and GCP were irrelevant then. And fast forward to 2026 and they're unbelievable businesses. So now is AWS the biggest? I think it's like a 40-30-20 split.

**Patrick O'Shaughnessy** [6:38]
Yeah.

**Eric Vishria** [6:38]
You ended up with an oligopoly of that. And even outside of those big three, you have Cloudflare, which is a cloud provider in a different sort, which is another $100 billion company. So you have these, like, smaller players that emerge as $100 billion companies outside of it.

So what's the takeaway? The takeaway to me is, like, oh, there's just a bunch of, like, kind of zero-sum thinking and not realizing, like, how big.

**Patrick O'Shaughnessy** [7:06]
What if it all works?

**Eric Vishria** [7:07]
What if it all works? And so it all worked. And of course, like, getting the relative winnerright matters. And there was roadkill and there were companies. So it's all of those things still matter. So I'm not saying, like, it's just, like, you know, like, spray and pray.

I'm not saying that at all. But I'm just saying, like, the market was so big that one vendor could not scale and consume it all. And they just couldn't consume the whole industry. Now, I just, like, again, like, it's different now and, like, all these things.

But if I just, like, just that whole notionright now with what you and I are seeing in AI sure feels like it rhymes,right? It's like, it's.

**Patrick O'Shaughnessy** [7:44]
Anthropic's going to do everything.

**Eric Vishria** [7:45]
Right. It's like, it can't, like, really? Like, is that reallyright? To me, that view that it's just like, oh, this one company is going to eat it all doesn't hold. And I would tell you that scaling, while cloud scaled very quickly, cloud did not scale anywhere close to as quick as what's happeningright now.

And so just for that company to actually scale and deliver it, scaling in this case requires, like, a ton of infrastructure buildup,right, from, like, energy, like, power, shell, you know, chips, memory, obviously the algorithms and everything else on top.

So it just, it feels, it feels to me like we're going to end up with an oligopoly of winners. And there will be, I really believe they will be these, like, $100 billion crazy

smaller winners. And so, like, that's, you know, that's, like, that just feels like that the same thing is kind of happening.

**Patrick O'Shaughnessy** [8:46]
Can you talk about it also from the demand side and compare it to how you watched cloud get adopted in enterprise versus how you're seeing AI get adopted now?

**Eric Vishria** [8:54]
So if you kind of go back to, like, 2010, 2011, like, even, you know, three, now you're, like, three, four years into AWS being an offering, enterprises were super skeptical. Like, super skeptical. Like, traditional enterprise, blue chip enterprise.

You had digital natives out here. You had new companies forming that were, like, using the cloud. You know, I think kind of famously, like, Snapchat was built on GCP. And I think at one point in this era, maybe 2012, 2013, 2014, like, Snapchat was, like, 40% of GCP.

Like, it was just, like, those kinds of things were happening.

**Patrick O'Shaughnessy** [9:31]
That's like cursor being 30% of all these things.

**Eric Vishria** [9:33]
100%. 100%. Same exact thing happened.

**Patrick O'Shaughnessy** [9:35]
Yeah.

**Eric Vishria** [9:35]
Same exact thing happened. Enterprises were very skeptical, I think, of cloud until, like, they really, you know, by 2014, 2015, 2016, it was like, oh, yeah, yeah. You know, then I think the big banks and financial services and insurance companies and more conservative blue chip enterprise were like, oh, wait a minute.

We, this is actually, you know, different and we're going to have to pay attention. And, you know, it became an issue in recruiting for them because they couldn't get the best developers because the best developers wanted to work on the easiest platforms.

And all these kinds of things happened. And so the difference now feels really profound to me because while it is 100% the case that blue chip enterprise AI is not, like, well absorbed and well adopted and not the same thing as going to Cursor and, you know, walking the halls of Cursor versus walking the halls of, you know, a big New York financial services firm in terms of their AI use, they want it.

They want to figure it out. They're running experiments. They are spending against it. They're trying to figure it out. They're talking about it. They're not being dismissive about it. And I think they view it probably as more of an opportunity than they did the cloud in terms of the potential impact on their business.

I think they probably view it more as a threat than they did the cloud. And maybe they just also learned lessons from the cloud in terms of, like, what's possible here. And so I feel that they will continue to try to figure it out and absorb it.

Having said all that, I do think one of the more interesting things, if you go from Silicon Valley out into the world and talk to these companies, these enterprise companies, and you realize, like, what the pace of adoption is and what the barriers to adoption are and everything else is, I think that, like, there's a ton of opportunity to help the enterprises get there.

To be one of the things that I tell the companies I work on is, like, hey, let's be their AI Sherpa. Let's be their AI Sherpa. If we're in that position to be their AI Sherpa where we're crossing both worlds, that's very valuable.

And I think that'll continue to work.

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**Patrick O'Shaughnessy** [12:51]
What is Sierra teaching you about the adoption of this stuff? And it's kind of actually, it's really interesting contrast to Fireworks where Fireworks is the sort of infrastructure provider. Sierra is feeling its way through what are really cool things that we can do for end consumers, enable companies to do for end consumers.

### Sierra

**Eric Vishria** [13:08]
Yeah.

**Patrick O'Shaughnessy** [13:08]
Starting with customer service, but I know with Horizon now, you know, going beyond that. Again, same question as for Fireworks, like, what do you know that the world doesn't fully appreciate because of how you've seen that business grow?

**Eric Vishria** [13:18]
Well, I think this is a good, it's a great example. So first of all, like, my partner Peter and Brett, I think this is their third company working together. So it's like a 20-year relationship, which is just, like, an amazing and great place to start.

And one of the things that I think makes, you know, Brett so special is, and the team is, they're technologists, but they actually have, like, lived in the enterprise world for a long time and, like, really understand it and everything else.

And I think actually he's personified this whole idea of, hey, let's be their AI Sherpa. Like, let's start with customer service. We're very admirable and, like, be their AI Sherpa. And now we have these long-running agents that can do, with Horizon, that can do more and more stuff.

And one of the things that I think is most interesting about this to me is, yes, it's an application company on the surface, but they're doing real AI work. They're experimenting with the models and they're building agents and they're very close to the metal of the models and the capability and the harnesses.

And they're understanding the jagged edge of AI capabilities, which is very different than the human smooth arc that we understand intuitively. They understand that jagged edge of capability and they build around it. You saw the evolution of Cursor from, like, IDE to, like, tab autocomplete to agentic work, like, over and over and over again.

They were obsoleting their work from, like, six months ago.

**Patrick O'Shaughnessy** [14:41]
Yeah.

**Eric Vishria** [14:42]
You know, and it's what Brett Taylor calls, like, sandcastles,right? We used to be building castles. Now we're building sandcastles that are going to get washed away. And that's, if you don't think of software that way.

**Patrick O'Shaughnessy** [14:50]
Embrace it.

**Eric Vishria** [14:51]
Yeah, you have to embrace it. Because if you're an artisan and you're like, hey, I built these, like, perfect foundation of castle and the bricks and it was, like, perfect and I really care about this and it's going to be here for 100 years, you're just not going to make it.

So as you get emergent new properties in the models, which is, you know, you get a new every four weeks, you get new capabilities. They understand that jagged edge. They understand that application of that jagged edge or that valley to their customer base.

And they're filling in those gaps and translating it. You can't just be superficially applying things. I think it's actually a complete inversion of how product development used to work to how product development happens now. And product development, you know, it always used to be, say, you'd also say, like, hey, product managers you train.

Oh, the product managers shouldn't, you know, should understand the technology, but, like, really shouldn't be thinking about implementation and shouldn't be specifying implementation and shouldn't be doing this and doing that. And the product manager's job is to really understand the customer and translate that problem to the engineers so that the engineers build the solution.

Like, that was traditional product management. Like, good luck doing that now. That's a horrible way to do it. You can't do it that way. You actually really need to understand the nuances of model capabilities, what they're great at and where they fail.

And you need to understand the customer problem and put those together and bridge those gaps to build valuable solutions. I think that's one of the things that Michael and team at Cursor did so well from the very beginning is they really understood the jagged capability and built a product that allowed that translation from developer to that jagged capability and kept on iterating on that as the edge changed.

**Patrick O'Shaughnessy** [16:26]
It's kind of ironic that, like, all that sounds like the returns to being technical are going up even as models supposedly are taking away technical edge.

**Eric Vishria** [16:35]
Well, yeah, yeah, yeah. I think there's two things.

**Patrick O'Shaughnessy** [16:36]
20%.

**Eric Vishria** [16:37]
Yeah, 100%. And actually, I think there's this weird thing where there was this whole discussion about, like, the traditional roles of product manager and designer and engineer and, like, whatever it is. And I think what they really are, there are people who understand customer problems, people who have taste, and people who understand the jagged edge of AI capabilities and are curious about it.

And, like, those are the three things.

**Patrick O'Shaughnessy** [17:03]
If you got those three, you're going to do well.

**Eric Vishria** [17:05]
If you're going to be your, if you have those three things, you're going to do great. And it doesn't really matter if you're an engineer or a product manager or a designer. But if you have taste and understanding of customer problems and understanding the jagged edge, you're going to do well.

**Patrick O'Shaughnessy** [17:18]
When we first did this so many years ago now, which is crazy, it'd be fun to revisit some of the ideas that we talked about the first time with SaaS. But you use this term that I've used ever since, which you call it the competitive frontier, meaning, like, the things that will determine the winners and the losers.

My partner Brie has this great idea that's stuck in my head, which is that everything is a jump ballright now.

### SaaS frontier

**Eric Vishria** [17:36]
Yeah.

**Patrick O'Shaughnessy** [17:36]
And I'm really curious, in addition to this, like, idea of sandcastles versus real castles, what else you're seeing amongst the people that are becoming competitive winners? Like, is it different? Are the traits different for winners now, personality-wise or business strategy-wise or business model-wise versus what you learned in the SaaS era?

**Eric Vishria** [17:56]
I'm very dismissive of this idea that, like, people are going to vibe code their own shit. Like, and whatever. Like, that isn't the issue. That isn't the issue at all with SaaS companies. The issue with SaaS companies is the competitive frontier completely shifted and everything they thought they were building against and it would make them win are not what's going to make them win.

Let's take databases. Like, databases have been a phenomenal area for software for a long, long time. Great margins. Why? You had app developers that would build against these specific database interfaces that existed for that database. You would have more and more data over time.

And so migrating an app from one database to another database was a giant project that was, like, very, very difficult to do. And so these were unbelievably sticky businesses that you could generate a ton of margin in,right? Of course, you got Oracle and SQL Server and, like, and a whole slew of smaller players like that did really, really well in databases.

Well, let's think about that in the context of AI. So, like, now you don't have a developer building against a database interface. You have Cloud or Codex building against a database interface. One. Two, the beauty of database interfaces is they're very, very well specified.

Well, it turns out AI is very good, very good at things that are very, very well specified. Three, agents don't get tired of monotonous work of translating one specification to another. So it turns out that now all of a sudden database migration, which used to be, like, the, like, number one thing you would not do in software, is, like, kind of trivial.

**Patrick O'Shaughnessy** [19:34]
Yeah.

**Eric Vishria** [19:35]
Yeah. It's just like, eh, put some money against it, move it. So what changed? Well, what changes is the criteria to be an amazing database company changed. It isn't that we don't need databases or that everyone's going to build their own database or whatever.

Like, that isn't what's going to happen. What's going to happen is the criteria changed. So now you're going to have a ton more applications that start, obviously, as we're seeing everywhere. Like, people are going to experiment a lot more because it's much cheaper to experiment.

It's much cheaper to start a new application. So now you need databases that scale from, like, basically zero usage to if it works all the way through. Like, that matters a lot more. Your cost matters a lot more.

You want to be able to spin these things up, spin them down, tear them apart over and over again so the iteration speed goes up and what you need on database. Ultimately, I think the cost becomes the arbiter of this.

So the cost and then this, like, kind of zero to infinity scaling and transportability and everything else around that become, like, the arbiters of who wins and who doesn't. That's really different than, like, hey, I specced this database for our user thing and I, you know, procured a license and I ran it on this kind of hardware and, like, everything else.

And so there've been elements, of course, of these things over time, but I think that just criteria changed. And I think one of the big messages to these SaaS companies, you know, a few years ago was, like, you have a choice.

Get to AI or be worth three times revenue. And I think a lot of these public SaaS companies are trading at six times or whatever. But keep in mind, in '21, they were going for 30 times.

**Patrick O'Shaughnessy** [21:15]
Yeah.

**Eric Vishria** [21:15]
Right? Like, everybody,right? And so you're like, three times, wow. I've grown 4x since then,right? This is the whole multiple compression is the bitch.

**Patrick O'Shaughnessy** [21:24]
Yeah.

**Eric Vishria** [21:24]
I've grown 4x. The multiple has gone down by a factor of six. So I'm worth less even though I've grown 4x in a few years.

**Patrick O'Shaughnessy** [21:31]
Yeah.

**Eric Vishria** [21:31]
And gotten to break even and, like, all these things. So, like, that was the first message. But I think that even it became really visceral to me where every single day that you are hitting your plan, you are destroying equity value.

Like, think about that. Our whole careers, we learned, like, you lay out a plan, you execute against it, like, relentlessly and violently. You hit your plan or you exceed your plan and you keep on building that. And that's how you build equity value.

That's what the whole management teams learned. That's what these CEOs learned, like, pre-AI. This is, like, everything learned. And now you're going in here and just, like, every day you hit that plan.

**Patrick O'Shaughnessy** [22:11]
You're fucking up.

**Eric Vishria** [22:12]
You're fucking up. You're destroying value. You're destroying value. And the point of, like, you know, saying that to them was to set them free. Another articulation of this from my friend Ann Lee Skeitz was just, like, the CEOs who were going through this transitory period and had a business that was at hundreds of millions and they thought they, you know, they were already, they were working on their business from 8:00 a.m.

to 5:00 p.m. and then trying to do AI from 5:00 to 8:00 in the evenings. And what they needed to be doing was the inverse.

**Patrick O'Shaughnessy** [22:46]
Yeah.

**Eric Vishria** [22:46]
And it's so hard to do that because of all of the training and all of the muscle memory and all the inertia and everything that we learned about. We're all hill climbing in a way. And, like, you know, the success model was, like, set the plan, execute the plan, build value, compound value.

And it's like, oh, no, no, no, stop that. You're, you're, you got to completely invert. And I think this is a very, very long way to get back to your question of, like, what the profile or what the mentality of the kind of winners areright now.

But if we look at, you know, Brendan from Rekor or Lynn from Fireworks or Max or Brett, any of these people, they are so nimble about what the eval is. Like, what is, what are they optimizing against? They are so nimble on all of it.

And if you look at every one of those companies, the evolution of the business, it's just the business is constantly evolving. And they've done such an excellent job at that. And I think that is very, very different than certainly the way I was, like, taught.

**Patrick O'Shaughnessy** [23:58]
What's your sense of the disorienting nature of model progress and, like, how to, we were talking, you said it earlier, every four weeks, like, I got a new jagged edge I got to figure out. You're one step removed from that as an investor versus as the technical founder, you know, with your hands on the metal.

How are you behaving differently than you would have, you know, three years ago or something because of the pace?

**Eric Vishria** [24:20]
Anytime that I'm talking to a founder about, you know, a problem in their company or what they're doing or a move they're making or anything else, which is what I spend 80% of my day doing, I'm very, this is how we used to do it.

This is what we would typically do. This would be the typical readout on this. Like, this would be the old school readout of why this candidate's better than this candidate. Now, like, let's reevaluate that in the context of today.

Let's reevaluate that in the context of an unstable technology substrate. Let's reevaluate that on, you know, in the context of, like, a business model that's growing this way versus that way. And, and, and so I've really started to question, like, every assumption and every lesson that I learned before, like, which of it translates and which of it doesn't.

And, and so, like, that's a huge difference. I'll give you a really concrete example, which has been very disruptive inside of these, like, scaling AI companies. So these scaling AI companies, we've had a lot of leaders come from the prior generation with great experience and everything else come to these scaling AI companies and completely flame out.

Like, and you see it across the industry. And so the question is, like, why? And it's, these are, these are some of the best leaders from four or five years ago. They had all the lessons. They learned it all.

You know, they're excellent, but somehow it's not translating. And, and there's just, like, some impedance mismatch between, like, the AI founders potentially, the needs of the business, and what these people are bringing. And I saw it, like, really abruptly with, with a particular sales leader who we hired who's like, hey, we can't hit any of these things because the way that software sales has been taught forever is a quota capacity model.

You have a quota capacity model. Each rep does this. In the early days of a company, the quotas are, I don't know, 1.2, 1.5 million. You know, maybe the ISRs are at 750 or 850. And then over time, it scales up and enterprise gets to 2.5 million.

And that's the whole thing.

**Patrick O'Shaughnessy** [26:25]
That's how it works.

**Eric Vishria** [26:26]
That's how it works. That's how all these financial models are built. You start with a quota capacity model. You take a discount on attainment. This is, this is what we can do. Boom, boom, boom. Now, like, fundamentally, without, like, realizing it, everybody was implementing something that was based on pushing demand, not pulling demand.

And for so many of these companies, they're operating here, these customers, and there's a new AI-enabled product that comes along and it's just fucking magic. So these companies are selling magic. Like, magic. Well, it turns out if you're selling magic and you're the first one there.

**Patrick O'Shaughnessy** [27:05]
Sell a lot more than 2 million.

**Eric Vishria** [27:06]
You're going to sell a lot more than 2 million. And so the whole notion of a quota capacity model and that being working, like, it's not that it doesn't matter. It matters kind of, but it's definitely not the first order thing or constraint.

And so you have these, like, execs come over with this. It's like, here's our math and here's the territory assignment and here's what we would do. And, you know, first you do West Coast and then you do East Coast and then you do Central, all of these things.

And it's like, oh, wait, no, no, it doesn't work like that at all. And so one of my big things that I started to realize is as I'm interviewing these folks and talking to them is just like, hey, you need to check everything at the door.

Just, like, check it all, which is probably good practice anyway, but check all the baggage. Check everything that you learned and just learn this from first principles. Like, how is it working? What are really the bottlenecks on delivery?

What are the bottlenecks on demand? Because it turns out that in a lot of these companies, you have reps doing 10 or 20, 30 million.

**Patrick O'Shaughnessy** [28:00]
I saw 50 recently.

**Eric Vishria** [28:01]
I mean, like, and so it's like, okay.

**Patrick O'Shaughnessy** [28:05]
Yeah.

**Eric Vishria** [28:05]
It turns out that's different.

**Patrick O'Shaughnessy** [28:06]
What is, like, the best salesperson that you've seen that's doing it in a de novo way doing?

**Eric Vishria** [28:11]
Honestly, the best salesperson at any of these companies is the founder. And what they're doing is bridging the jagged edge to what the customer's capability is. And that's it. And it's like, it sounds so simple, but it's not.

But, but that's what they're doing. And, and then the, you know, the market is just so big. Like, just like we were talking about with cloud, I think the biggest mistake everybody made was the market. They just undersized the market.

### Energy bottleneck

**Eric Vishria** [28:34]
And it turns out the market's just really, really big. And this market is fair.

**Patrick O'Shaughnessy** [28:38]
It feels likeright in this moment in time, actually, just this morning, you and I are in this great, this great group chat together where the discussion is the demand kind of demand for intelligence. Like, I don't know, it seems kind of unlimited and it seems like the smarter the thing gets, the more demand there is.

And that maybe the bottleneck is just capital. Like, the world just feels like it needs to take a breath.

**Eric Vishria** [28:56]
Yeah.

**Patrick O'Shaughnessy** [28:57]
Like, the RSI kind of concept, if you apply it across technology, doesn't need to breathe. The agents don't get tired.

**Eric Vishria** [29:03]
Right.

**Patrick O'Shaughnessy** [29:03]
But the world feels kind of like, oh, man, it sure would be nice to have three months just to, like, digest this a little bit. And for capital to form and, and, and evaluate its prospects and the scale is getting so big.

Like, does it feel to you like this can just keep going or are we just going to get tapped out of money that can be invested in these things when it seems like we could consume, like, any amount of money to build any amount of stuff and serve any amount of inference?

Like, how do you feel about the market?

**Eric Vishria** [29:33]
I, I, I would say this. I am worried about energy. If you think about the models as translating compute into intelligence, like, quite simply, what do models do? They, they very effectively translate compute into intelligence. What's the demand for intelligence?

Well, it seems like a lot.

**Patrick O'Shaughnessy** [29:50]
A lot.

**Eric Vishria** [29:51]
Right? So then it follows that we will kind of continue to, like, have more and more demand on compute. I'm saying compute broadly, not, not chips, not storage, not whatever. But then, like, what do we need for compute?

We need energy. Like, a lot of energy. There's all this topic of distillation and, and, and Chinese open source and all these things. But the bigger thing to me is that I think China's bringing on 10 times as much energy next year as we are in the US.

If energy is what you need for compute and is the bottleneck and there's unlimited demand for intelligence, then it stands to reason that if we have a lot less energy, then we will have a lot less intelligence or a lot less tokens or a lot more expensive tokens.

And if we have a lot more expensive tokens, then supply demand, you're going to end up with less. And that seems very bad. And so to me, I think the energy bottleneck, however that that is, and it'll manifest in 20 different ways.

You know, gas turbines go up and down, natural gas goes up and down, and solar, whatever, like, all these different things, rare earths, like, that to me is, is probably more concerning. And if I'm thinking about it from a regulatory perspective or government perspective, and I think the administration is doing some things around this, like, you know, fostering really investment and development of all energy that, and I, I mean all, like, solar, nuclear, gas, like, whatever, do it all.

**Patrick O'Shaughnessy** [31:28]
Yeah.

### Cerebras

**Eric Vishria** [31:28]
Like, we should do it all.

**Patrick O'Shaughnessy** [31:29]
Yeah.

**Eric Vishria** [31:30]
And, and it'll work itself out, you know. But again, this is one of these things where, yes, one will be relatively better than the other and I don't know and I'm not smart enough to predict which one's which, but, like, it'll all work.

**Patrick O'Shaughnessy** [31:40]
Speaking of compute, I would love to hear this Cerebras story. I, I haven't heard you tell the full version of this. I think it's, the reason I'm asking about it is I'm deeply interested in compute. I have big investments in compute and I'm fascinated by it.

It's just like the most magical thing to watch happen. It's mind-boggling when you get close to one of these things, what humans have been able to do on these chips and in these systems. I think you invested in 2016 or thereabouts.

So I think it was your first foray into, like, extremely difficult hardware type investment.

**Eric Vishria** [32:10]
Shit's so hard.

**Patrick O'Shaughnessy** [32:11]
And, and I'd love you. Now the world is full of opportunities like this.

**Eric Vishria** [32:15]
Yeah.

**Patrick O'Shaughnessy** [32:15]
Whereas back then it was like a, you know, a really a one-off. Teach me everything you've learned about hardware investing through Cerebras.

**Eric Vishria** [32:23]
Mostly, mostly it's really hard. It's an amazing example to me about, like, the naivete required. So, you know, the, the, the company came in in 2016. It was five founders in a deck. I really did not want to go to the pitch, but it was my job because I'm like, why are we going to go to hardware investment?

Like, this is crazy. It's been 10 years since we've made a semi-investment. I, I think basically the team was excellent. And then the kind of first slide was just like, GPUs actually suck for deep learning. They just happen to be a hundred times better than CPUs.

And as soon as you said it, yeah, you have to remember this is pre-Transformer. Like, OpenAI is this like weird research lab at this time. Like, NVIDIA was worth like 40 billion, not 4 trillion. The TPU hadn't been announced.

Like, none of that. So this is like early, early. But the whole idea, just like, as soon as he said it, I was like, oh, shit, of course, of course. Like, why? And I'm, I had been spent the last 18 months trying to figure out, like, applications of deep learning and looking at the security thing and looking at this medical imaging thing and, like, all this other stuff, thinking like, hey, there must be something here that's going to be really transformed by this stuff.

And so anyways, we go through this, like, whole journey. We end up investing, which was amazing. We just, like, we first met on Wednesday, partner meeting on Monday, had a bunch of meetings in between, and it was like, got just built a lot of conviction that this was a great swing.

And I'll tell you what I understood and I really just didn't understood so little. But basically there are three things that we know how to do to speed up deep learning and hardware still till this day. Increase the number of cores, increase the communication between cores, bring the memory closer to the compute.

Those are the three things. That's it. Those are the only three dimensions that we know in hardware. So my articulation of what they said to me and, like, honestly, all I understood was if we, let's just take all three of those things to their logical maximum.

You have a wafer scale chip. At that time, you would have 450,000 cores on it. You'd have something like 20 gig of SRAM on the chip. So you'd never have to go off chip to get to memory. And because they were all in the same wafer, the communication between cores is maximized.

So this is the best you could do on that process. And I think the first, the first chip was 7 nanometer or something. And so, like, you're like, okay, like, that's it. Like, that's what we, we do. And it turns out that in software, if you have like that kind of like logical block diagram of like why it works and everything else, you're kind of 80% of the way there.

And it's a matter of like go-to-market execution. In hardware, you're like 2% of the way there. Like, there's things like physics and, and, you know, and, and this entire supply chain of vendors and that like, you know, like there's of course TSMC, which everyone knows, but it's not just TSMC.

There's 30 other vendors that like matter and like putting all this stuff together and everything else. And so, like, I didn't know any of that and like really didn't know. So, you know, fast forward from 2016 to like 2019, I think they, they got their first parts back and, and it's like you go through this like bring up and then it's like bring up, oh, yes, we got a part back.

And then it's like bring up. Yeah. And then you got to go through like wait, bring up. And there's like 14 steps of bring up and everything else. And then by like 2020, we had our like first thing that like works.

And then you're like, then it's just this march of like actually getting it to work. And, you know, one of the lessons that I've learned on this stuff is basically you go through all these SIMs and everything else in hardware and, and semis in particular, and that, that basically is your roofline.

Like, that's the maximal performance. Best it's ever going to be is like what that is. And then every bit of like software and reality and compilers and kernels takes away from that roofline. You might start at 10% of the roofline once you bring it up.

And then you're like, you know, then these guys are grinding for, for months and years to like get closer and closer and closer to the roofline. It's really different. It's really hard. I'm, I'm, I'm actually like astonishingly bullish if I kind of rewind.

Part of the reason we made the investment was if you looked at the four prior generations of compute that like in my lifetime. So you had CPUs, you had graphics, networking, mobile, there, there was a new workload each time.

So you had multipurpose compute and it loaded to the CPU. You had massive parallelism, which, you know, led to the graphics processor. Graphics processor offered massive parallelism, which led to graphics. Then with networking, you needed really low latency chips.

And so they had low latency chips. And then with, with mobile, you needed really power-efficient chips. And so each case, we ended up with a new $100 billion company. And the question, the first question, you know, going back to 2016 was like, is AI that big a new workload?

Because there'd been many, many other attempts for specialized chips for other things that like really just didn't end up mattering. Like there, there's some fine outcomes, but like just didn't really end up mattering. Right. Exactly. And so it's just like, well, okay, well, you need something that's like a really, really big workload.

Okay. So that's one. So the, and we, we had a lot of conviction on that. And then two was the nature of the workload. Did it introduce a new constraint or problem in it? What I learned was like basically the AI workload benefited from the parallelism of GPUs massively, but GPUs didn't solve the core to core communication, basically the layers of the network problem.

And so like you're like, okay, wait a minute. Like there is a new constraint, which is communication, it's a communication bound problem. And so then you're like, okay, is AI a new giant workload that is going to have specialized chips?

You know, basically everything that I just said was the entire, everything I knew at that time. And I, I do actually think so that, that's obviously played out. And, you know, like in each prior generation, we obviously, we got Intel, we got NVIDIA, we got Broadcom and Vogo, we got Qualcomm and ARM, like, you know, in each of these generations and like you're, there will be like these giant winners, standalone winners.

Obviously the TPU itself is, is a winner. Trinium is a winner. You know, you've had Grok and Cerebras, you know, Etch, like it'll keep getting fought out.

**Patrick O'Shaughnessy** [38:54]
Yeah.

**Eric Vishria** [38:54]
And, you know, but I think that will end up being big. And actually I think there's a new sixth one that's coming generation, which is, and I'm really excited. We've made an investment that's unannounced in this, but I think that for the first time in a long time, there's actually room for a new CPU approach.

The thing that's happeningright now, and, and you, you see this reflected in all the semi stocks and everything else is the LLMs, which are running on, you know, accelerators and GPUs generate code. The code runs on CPUs. Andright now it's running on classic CPUs we've had around forever.

But there's a whole bunch of constraints on CPUs that have existed and CPUs have dragged all this baggage forward that you might not need to anymore. And so I'm actually like really excited about, about that possibility.

**Patrick O'Shaughnessy** [39:49]
The next category.

**Eric Vishria** [39:50]
The next category.

**Patrick O'Shaughnessy** [39:50]
Does the experience with Cerebras make you want to do a lot more investing in companies?

**Eric Vishria** [39:55]
Fuck no.

**Patrick O'Shaughnessy** [39:57]
But why not? Like, like the defining question.

**Eric Vishria** [39:59]
In 2019, we're sitting in a board meeting and this thing is melting. It's like fucking melting. Okay. And we've raised $500 million or something. And it's like, wait, what? Like what? It's melting or it's burning or something. And, and like we're looking at it and I'm like, holy shit.

Like, you know, and I realize like $500 million isn't that much in today's era, but like I was just like, we're going to lose all this money. Like this is not going to work. And, you know, and, and I'm, and talk to Andrew about this, obviously just what that team did.

Insane. Like in, insane in terms of the technical.

**Patrick O'Shaughnessy** [40:34]
Hardware teams are built different.

**Eric Vishria** [40:36]
They're built differently. They, they're, they're built so differently. And I have so much respect and thanks to them for what they've done. Look, I, I, I, you know, joking aside, I think that there are efforts that make you really proud to be a venture capitalist and like to, because you're funding something that like makes a difference and matters.

And for me, I'm an investor because like that's a means to work with companies, not because I like fundamentally like love investing or something like that. I like the working with the companies. That's my, that's my favorite part of it.

And working with teams like that and companies like that is so special on these like giant ambitious efforts. And, you know, and I said this before, like well before 2018 or whatever it was, like whether Cerebras worked or didn't, I think it was an effort that was worth venture capital.

Like it, it, that's the kind of thing you should do. You should try to build something that people have tried for 50 years and have been unable to, but now we think we could do and there's a reason and application for it and everything else.

I love that.

**Patrick O'Shaughnessy** [41:47]
Yeah.

**Eric Vishria** [41:48]
And, and so I, I do like those kinds of things, joking aside. And, you know, we do have a robotics company. We actually have a defense company that, that I'm also really excited about. And then, and then this like, you know, CPU project that we're talking about.

And so like I, I think these kinds of things are actually really fun and interesting and good use of venture capital, but they, they definitely aren't easy.

**Patrick O'Shaughnessy** [42:07]
I love the, the sort of productive naivete that you described where it's probably a virtue that you didn't know more than you knew. Otherwise you wouldn't have done it.

**Eric Vishria** [42:15]
Yeah.

**Patrick O'Shaughnessy** [42:15]
This is certainly my experience with Etch that like when we first called around asking, like if you asked experts, everyone says like in any of these fields, you ask experts, they're going to tell you, don't do it.

**Eric Vishria** [42:23]
Yeah.

**Patrick O'Shaughnessy** [42:23]
Like it sucks. It's too hard. Base rate's too low. Young people can't do it. Well, there've been 47 reasons.

**Eric Vishria** [42:28]
All those things. Yeah.

**Patrick O'Shaughnessy** [42:30]
Is there anywhere where that is, it's just a bridge too far? It could be like, I'm thinking like bio or something like this where you just are unwilling to invest if you're naive.

**Eric Vishria** [42:39]
Actually, like most of the time, all of these like stereotypical statements are correct. Like they're, they're not like correct like three times out of four. They're correct like 19 times out of 20. Maybe 99 times out of a hundred.

The thing that Bruce, one of our founders, always says is just like what could goright that we have to ask ourselves is like what could goright and do we see that path? And it's like, yeah, you know, young people can't build chips or you shouldn't do another semi company or you shouldn't do this or like whatever.

Like all of that stuff is actually totallyright except when it isn't. And so apparently there's a saying that someone, someone said to one of my partners, which was, if it doesn't work, it'll be for all of the reasons that your partner said.

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### Robotics

**Patrick O'Shaughnessy** [45:14]
You and I think are both interested in robotics.

**Eric Vishria** [45:17]
Yes.

**Patrick O'Shaughnessy** [45:19]
It's not controversial that if robotics works, it's going to, it might dwarf what we're currently living through. What do you think has to be true for it to work? Like obviously it's exciting. I want a robot in my house folding my laundry.

It sounds great, but it, it's kind of one of these classics. It's always 10 years away and it's been that way for a long time. What do you see happening? What has, what has to happen for this to actually be a thing in the near to medium term?

**Eric Vishria** [45:42]
We've had classic robotics forever and they're all over the place and they're on assembly lines and manufacturing lines and all those things. It's where you're doing repetitive tasks in controlled environments. Repetitive tasks in controlled environments is like more or less solved and like that, you know, that'll continue to happen.

But having tweaked tasks in real-world environments, that's where you need the AI. Like that's where you need the AI plus the, plus the robots. And the trick with it all is you need a model that can do that.

And of course the problem, the first problem is there is no internet-scale data to bootstrap the whole thing,right? LLMs are all bootstrapped on the internet, which is a ton of human knowledge. And the equivalent for that for robots doesn't exist.

And people are trying different things with videos and simulations and, and teleoperation. So like there's a lot of different ways, but if you kind of think about, take teleop as an example, like how much you need to teleop robots to get to an internet-scale data.

This first step is like getting a good set of data to bootstrap the model. And one of the, I think, insights that you can have is with the internet, there's like a lot of slop data. Like even before AI generated all this stuff, there was also a bunch of junk data.

And some data was more valuable than others. Like you may value, okay, certain things on Reddit more valuable than other things. You might value Wikipedia more than like other forums. You might value GitHub more than other things. And, and all of the model companies did that,right?

They prioritized data that was more valuable and less valuable and, and ran that through the model. So I think one of the most interesting things that these AI robotics companies are doing are saying, okay, well let's just go after the high-value data to start.

And if we go after the high-value data, then we can kind of bootstrap this, this, this model. Now, once you do that, then can you, through the pre-training process, get to a place where you can have very small auxiliary examples of data that you add in post-training and all of a sudden it works for that.

That's the magic we have with LLMs is you have this giant pre-trained base and then you add a little magic on it

in RL and post-training.

**Patrick O'Shaughnessy** [48:10]
You teach it.

**Eric Vishria** [48:10]
And, and you teach it. You teach it a new thing that it wasn't in the pre-training set and, and you kind of go from there. And I think the exact same thing is happening in robotics. We're investors in, in Sunday Robotics, which is going after this exact pipeline.

**Patrick O'Shaughnessy** [48:23]
It's a cool company.

**Eric Vishria** [48:23]
It's a cool company. And they, and they, you know, they're doing household robots, but the, the key part before you get to household and all of that stuff matters much less actually than like can you get this like training pipeline to work and how do you do that?

And one of the lessons I learned and I look back, I try to learn from history 'cause, you know, it doesn't repeat, but it rhymes. And, and if I look at autonomous vehicles as an example, which is they're robots basically.

They're, they're AI plus robots. You look at Waymo and you look at Tesla and they were both designed vertically integrated in their own way. You have a Tesla, you had a fleet of Teslas that everybody owned who was collecting data on the Teslas and it was used to train the models to drive the Teslas.

And same thing with Waymo. And I think part of the lessons is they gathered very, very high-quality data. They did their pre-training and, and then they worked off of that. And that was kind of, that was a simplification to allow you to get to a complete product or complete solution, which of course is going to continue to improve and ultimately we'll generalize, I'm sure it'll generalize in some, some ways.

So you'll be able to strap it on any car and everything else. So I think the same thing, exact thing is happening in robotics where you have companies like Sunday and others who are using techniques where they are vertically integrating the robot and the model, the data collection around the robot.

Sunday uses gloves that are designed just with the robot hands. So they're, they're, they're perfect. So you get very good data transferability from one to another. You do this pre-training and you have a great pre-training data set, you have a good model, and then you start adding these examples and you RL and post-train on top and you get cool emergent behavior.

**Patrick O'Shaughnessy** [50:15]
Do you have a most visceral moment?

**Eric Vishria** [50:17]
When we invested in Sunday the first time, we saw, we saw them, my partner Peter arranged a demo and we went down into the basement at the Stanford lab and they had like this totally janky cardboard glove thing.

And you, you know, you see a few evolutions of it. And the last time we, we saw a demo, we like went down in the basement of their now office building and, and you see, and there was like a dozen robots just folding arbitrary lines.

It wasn't, it wasn't a demo. It was just trial and error. And then they have people like taking, taking the clothes and then measuring them to make sure that they were folded properly and like, and they were like creating a rigorous baseline and evaluation criteria.

And I was like, oh my God, this is like, this is happening.

**Patrick O'Shaughnessy** [51:05]
Do you ever worry about how to pick theright customer for these companies? Like every one of these things folds laundry, which like I don't think anyone likes folding laundry. So it like seems like a good, a good use case, but it feels like we don't actually understand the demand for what these things will be used to do.

Do you ever worry about that? They were sort of building solutions that will then be in searches of problems.

**Eric Vishria** [51:29]
I don't, and, and I'll explain why. And it's, it's, it's like certainly informed by watching the LLM evolution, which is like, you know, I think some of the people who were involved in the early LLM at, at OpenAI like really understood that like code was going to be important.

And, and obviously, you know, the Anthropic team had this perspective that you could get to RSI if you've got code generation going and, and automating AI research and whatnot. But if you think about it, like the first use cases were very much language-oriented.

They were very much like essay writing and editing and, and, and like marketing. Like I think the first application that really took off was, was Jasper, which was just like writing marketing copy. And, you know, I think it's going to evolve a lot.

Basically, I think like laundry is kind of a good task because it's arbitrary, it's complex, it requires dexterous manipulation. It's done on, it isn't time-sensitive. So you can like, if it takes three times longer, so be it. Who cares?

It doesn't matter. Just let it run all day. So I think it has like some of those properties, but I don't, I don't think the task is actually that important. What I think is much more important is are you pre-training an amazing model and then being able to post-train on top of it and get that flywheel going.

And if you get that flywheel going, then, you know, the task capability will just keep, it'll keep multiplying.

**Patrick O'Shaughnessy** [52:52]
Maybe this question will be annoying or slightly uncomfortable for you, but if you ask basically every founder and critically other investors of your type, almost everyone, if I ask like who's the best board partner, will say you. Like you come up way more often than, than anyone else that I've come across.

### Board partner

**Patrick O'Shaughnessy** [53:12]
And I'm curious why you think that is. Like what, what it is that you're doing that other people, the incentives are there like to, to do a great job as a board partner. What do you think you're doing on the boards of these companies, partner with the founders that's actually different from other really talented investors who are also nominally doing the same job, but don't come up nearly as often when asked that question?

**Eric Vishria** [53:35]
One of the things that I've realized is

we each are attracted to like different types of entrepreneurs and like where we have chemistry. And I think one of the differences when I say I'm like, I'm really like I'm an investor second and I try to be a partner first.

And, and here's what I mean. We'll, we'll see companies come in. We, we had one come in yesterday and it's an, what I would call an investment-grade opportunity. It's kind of like you can invest, it probably works, you make money, like it's, it's good.

And an investor would do that and

a partner wouldn't because that's not sufficient for a partner. Because like unless you have real chemistry with that person where you feel like you're going to be able to work together really effectively and, and I'm going to learn a ton from them and they're going to learn something from me and, and together we're going to just like feed each other's loops,right?

Unless you feel that way, then like you can't be a partner. And, and so you pass on that. But like that's, that's a really important like fit element to me. And so it kind of starts with this like mutual selection actually weirdly of like they want to partner with us and, and, and I want to partner with them and like I'm really like looking forward to working with them together.

And, and I'll give you a really good example of where this like kind of comes into play for me. If I take like Sage and Benchling, Benchling is, you know, life sciences SaaS companies absolutely crushed, done really, really well.

And then of course, you know, you had this like biotech crash and the company, it, it became hard. Like it became grindy. And I, and I joke, the company had never had any churn for the longest time such that even on their like reports, you know, for every SaaS company you have this like, okay, gross ARR added, churn line, net ARR added, like everybody does the same thing.

They never had a churn line, never reported it for the first of like six years that I worked with the company. So then they got seven years of churn in like 12 months. Turns out like life sucks when you get seven years of churn in 12 months.

And like through that grind and through it all

and related to the whole like, wait a minute, the goalpost moved, we have to do something different. Everything else, they kept like thinking about how to apply AI for these biotech and pharma customers, which they're very close to.

How can we make it better for them? How can we apply these models in their world in a way that they're excited about and, and continue to iterate? And like, and it was grindy and I was there for it.

And I, I just like, and that's one of these cases where it's like you, you, you don't, you're excited to work with that person 'cause like one, of course you think it's a really special opportunity and there's a way out, there's a path and we can find it.

But two, because of the joy of the game and like the relationship and like everything else, like that's part of it. And I don't, I don't know what it is more than that. I, it's, it's very different. I've seen different models and, and lots of different models of venture capital work,right?

Like Moritz was a writer, Dore was a sales guy, like, you know, Gurley was an engineer, like Peter's a career venture capitalist. Like they're all different,right? And, but for me, I think

like that working with them, it's like when I call these people, I learn something and they push back on me and then I ask them questions. And what I've realized is like so much of my job is they know the answer.

They know what they want to do. They know the answer. And it's maybe asking questions of them to maybe help solidify their conviction or solidify their articulation of what they want to do and why. And, and, and through that process, hopefully we get like, you know, 1% better a few times a year.

We make a 1% better decision, 2% better decision a few times a year. And if you do that over a decade, that compounds to real results. One of the questions that I ask myself before making an investment is

there are all these people that I care about through my life, like you care about yours. Could I talk one of them into going to this company and honestly, intellectually, honestly to myself, explain to them why this could be their life's work?

And if I can't do that, I should not invest because it's just not like, it just means that the project is not for me. It, it just doesn't line up in that, in that way. And so as long as we have one of those things, it doesn't matter that much what it is to me.

It's just, it's important. It could make a big dent. And if it can make a big dent and it's a special person, I'd love to work on it.

**Patrick O'Shaughnessy** [58:31]
Are there any other questions like that that you ask yourself before investing? That's a particularly good one.

**Eric Vishria** [58:36]
Yes. The other question, if this person calls me at 9:00 p.m. on a Saturday night, will I pick up the phone?

**Patrick O'Shaughnessy** [58:41]
Call that the green button test.

**Eric Vishria** [58:43]
Yeah. Yeah. Yeah. Right. It's just like, it's like, it's one of these things which is like if you don't, then you just know if it's yes or no. You just know like for whatever reason, it's just, it's a chemistry thing and they have to feel the same way obviously.

And then, you know, one of the other ones is like if, if it'sright, does it matter? Which is different than this like first one, but it's like, you know, there's so many things that we could beright on as a business.

I, I, I looked at one last week and I told entrepreneur, I was like, I, I don't, I really think that you can run build an amazing company here and you just shouldn't raise venture capital. But there's like so many things that you can beright on that, but they ultimately just don't matter.

Like nobody cares. That's a better way to say it. If we'reright, we'll not really care. And it's like if they won't care, then you're just not going to build enough equity value. And I think that's a, that's another useful one.

**Patrick O'Shaughnessy** [59:33]
One of the coolest things that's happeningright now is all of that that you just described has higher stakes and more leverage attached to it.

**Eric Vishria** [59:39]
Yes.

**Patrick O'Shaughnessy** [59:40]
Which is manifested most simply in more dollars.

**Eric Vishria** [59:42]
Yes.

**Patrick O'Shaughnessy** [59:44]
And higher prices. You and I have talked about this notion of what high multiple invest meaning like high multiple on invested capital investing is like and what it has been like and what it's moving into.

**Eric Vishria** [59:55]
Yes.

**Patrick O'Shaughnessy** [59:55]
You did something recently, which was you raised a growth fund for the first time in a long time. And I think that is related to this concept of like these companies need more capital, the prices are higher, the outcomes are bigger.

Maybe we can earn the same multiple on a billion-dollar entry price that we could on a $50 million entry price, you know, 10 years ago or whatever. Can you talk through that evolution? Talk through the partnerships like way of thinking about it and talking about it, like what you believe to be true that, that results in this decision to do this?

**Eric Vishria** [1:00:23]
Just go back to like why did LPs, you know, starting with, with Swenson and everyone else, like why did they start investing in venture capital? And fundamentally it wasn't because they thought they could beat the NASDAQ or the index by like 3 percentage points a year, five or whatever stupidity.

Like it's because there were, there were situations where venture capital could drive these insane multiples on invested capital. Like that's from a financial perspective, that's what they were seeking. And for the longest time, for most of the industry's history, two things were synonymous.

Early stage investing and high cash on cash multiples. Like the way to get high cash on cash multiples was to do early stage. And like that's it. Like those two circles in the Venn diagram like almost perfectly overlap.

And I think the thing that's changed is recently, relatively recently in the last few years, because outcomes have gotten so much bigger and these markets are bigger and everything else, the circle of high cash on cash multiple opportunities is bigger than just early stage.

And it's not so, so big that like there's like a gazillion new companies in there that you can generate 100 Xs on. That's not true. But there are, there are certainly many outside of early stage where you can generate really high, high returns.

And so that's what, that's it. Like that's what we want to go after. You could argue we're a few years late. I, I, I think I take that criticism. I think that, but I think that opportunity exists for on a go-forward basis.

And so like we should go do it. And, you know, everything else that, that we represent, which is the high conviction, high commitment, you know, partnership, everything else, like that, that has to still be there.

**Patrick O'Shaughnessy** [1:02:21]
As part of that discussion, what, what, what were like the other sides of the debate, such as like maybe we would have said the same thing in '99 and 2020 as markets get exciting.

**Eric Vishria** [1:02:32]
Yeah.

**Patrick O'Shaughnessy** [1:02:32]
It seemed like the possibilities, we all do this extrapolation error.

**Eric Vishria** [1:02:36]
Yeah.

**Patrick O'Shaughnessy** [1:02:37]
What were the counterarguments to like, let's, despite all that, still not do it?

**Eric Vishria** [1:02:41]
Yeah. I mean, there's all the counterarguments that you would expect. I, I think the biggest counterargument that really made this timeright versus two years ago or whatever was you need the team that can do it. Like it's just a different mentality.

Like there, there are differences in how you evaluate and think about things. All, all the other stuff or there's, there's, you know, why not change and stay within your circle of competency and all those things are true. But, but to me that, that was like the biggest one.

And we had several examples over the last couple years where we had, I think, theright intuition on a company or an opportunity and we didn't do it.

**Patrick O'Shaughnessy** [1:03:20]
Because it was outside the box.

**Eric Vishria** [1:03:22]
Because it was outside the box. And that's, that's obviously dumb. And I think it is quite different than a lot of, you know, the industry does. We, we, we are really chasing these like very rare special companies that have like very high cash on cash opportunities where we think there just can be runaway successes and, and we can invest in them.

**Patrick O'Shaughnessy** [1:03:43]
Is there any lesson to be pulled from the many, let's call it 20 to 100 Xs that you personally have observed? I mean, it's like such a crazy amount of return that like obviously it doesn't pencil in the beginning.

Like you can't make something pencil if it was that clear. Like the price would be different.

**Eric Vishria** [1:03:59]
You can't. You can't.

**Patrick O'Shaughnessy** [1:04:00]
What, what has, what have the 20 to 100 Xs taught you in aggregate, if anything?

**Eric Vishria** [1:04:06]
Work with really special people. You know, it's kind of like you want to work really hard, you want to work smart and, and get lucky. Like it really all has to come together. And there's a lot of things that are like timing dependent, you know, that you have no control over as a company.

Like, you know, take the Cerebras example is a good one, which is like we took it public this time in May, but like we tried to take it public in 2024 and it would have been taken public at a much, much lower valuation.

And, and like, you know, it would have been rough and it didn't, it didn't work out because of Ciphius and all this stuff. So timing matters. Like, and, and you know, just the advancement of that 18 months like made all the difference in the world for a bunch of things that were honestly outside of our control.

There were some things that were in our control, like getting inference running and everything else, but like there was a lot of stuff that was outside of our control. These are all the classic things which is like you got to focus on what you can control.

That's one of the things that's really different than software companies. With software companies, you know, aside from like building on AWS or whatever, you pretty much own your whole stack. And so you're really fully in control of your, your destiny in that way.

With hardware companies, you don't. Like there's an entire supply chain and all this other stuff. HBM's a thing and DRAM's a thing and TSMC's a thing and, and a lot of those cross geopolitical borders. And so geopolitics gets involved and, and so then that makes that complicated.

And so that's a, a really big difference. You got to get lucky on the timing and macro and, and, and other stuff. But I think it really starts with like working with these like crazy people with unbounded opportunities.

And if you work with these crazy special people on unbounded opportunities, you know, then, then you, you get, you get lucky from time to time. You're bound to. I, I, this when I was 20 years old, I was working at an investment bank.

Ben Horowitz, Mark and Ben had started LoudCloud. It was still in stealth. And, you know, Ben gave me an offer to be his assistant. And I was talking to this associate who like seemed like this like elder at the time.

He's probably 25, but like, you know, talking to this associate at the time. And, you know, he, he said this thing to me, which I, which, which really stuck with me. He was like, "Do you golf?" And it's like classic banking question.

"Do you golf?" No, I don't fucking golf. But he's like, he's like, you know, with golf, you, you keep on practicing and you like keep getting the ball in a three-par like close, close to the pen, close to the pen, close to the pen, close to the pen.

And he's like, "You keep getting the ball close to the pen and you keep practicing." And he's like, "That's hard work. That's like working smart. That's like, that's what you want to keep doing." He's like, "Getting the hole in one, that's luck."

I kind of love that framing and I use it with my kids actually because what it says is like, "Yeah, there's luck involved." And there really is luck involved, but there is actually a way to increase your luck.

And the way to increase your luck is get a lot of balls close to the pen and then like, "Yeah, eventually one will drop." And, and so I like that, that mental model. So I kind of go back to this for, for companies, which is, and I think in, you know, each of us invests in one to two companies a year.

I think in my 12 years, I've invested in 18 companies total.

**Patrick O'Shaughnessy** [1:07:15]
Crazy.

**Eric Vishria** [1:07:15]
Which is a relatively small number. So it's very high conviction and very high commitment. It's, I have a lot of skin in the game. I believe in these entrepreneurs. I believe in these companies. But

if you keep on working with these like very special people and these opportunities, magic can happen.

### Going public

**Patrick O'Shaughnessy** [1:07:35]
What have you learned about the best reasons and conditions for going public?

**Eric Vishria** [1:07:39]
Benchmark does these Monday night dinners. You've, you've been once or like. And so we had a, a CEO last night of a multi-hundred billion dollar private company. And we had this whole conversation. So it's kind of, it's, it's a little like fresh.

I think that ultimately when you go public, you have a range of new opportunities in what you can do. You have public trust, actually weirdly, because there's some transparency that comes with being public, being a public company. You obviously have a currency that you can do things with that ends up being there.

You have an unbelievable ability to raise capital, which I think is why the labs will ultimately go. Although I think the trust thing is actually a really important element of why they should go and it's beneficial to the world and to America if they do go public is like, it's like what's going on.

Yeah. See what's going on. Everyone can see it. I think that's like a really beneficial setup. There's another element of it, which is what does a collegiate athlete want to do? Go pro. They want to play at a higher level.

And is it harder? Yeah, it's harder. Is the competition tougher? Yeah, the competition's tougher. They move faster. They're tougher, they're bigger, they're stronger, the stakes are bigger, the stage is bigger, the scrutiny's bigger. All of that's true. It's kind of the same thing with companies.

There are a handful, and it really is a handful, three, four, whatever, that can get to this like tremendous scale without going public because things have gone through their execution and excellence.

**Patrick O'Shaughnessy** [1:09:09]
Lots of free cash flow.

**Eric Vishria** [1:09:10]
They've lots of free cash flow and they've done really well over a really long time. And I think that's fantastic. And, you know, good for them. But, you know, in general for everyone else, like get out there. And then the other thing that I would tell you is there are windows for a particular type of company.

So like, yes, the SaaS companies that went public in 2021, a whole boat of them have struggled and it's been tough in the public markets because their stocks ripped to this 30, this multiple compression issue. They were trading at 30 times.

They've 4x'd in size, but now they're trading at six times. It turns out you're under still,right? Like that's, that's a tough place to be.

Yet I will also tell you that there's 500 something, probably SaaS companies that are between 100 million and 500 million that are private. What, what happens to them? Those, those employees never got a chance to sell. Those employees don't have annual tenders.

Those employees don't have an opportunity to exit. Like those investors don't have an opportunity to exit. They're stuck. And, you know, I don't think they're all going away. And as I said, I don't think they're all getting vibe coded and everything else.

But ultimately, you know, the AI natives with their growth rates sucked all the oxygen out of the room and all the interest and the window was missed. And that's tough.

**Patrick O'Shaughnessy** [1:10:33]
What are the biggest debatesright now inside of the partnership? I always love coming here and talking to you guys when there's something interesting going on because, you know, you debate, it's healthy, it's can be, can be really fun to watch and I learn a lot from it.

What are those debates today?

**Eric Vishria** [1:10:49]
There's a ton of debate around this AI infra apps, you know, well, foundational models, infra apps like ecosystem, where does value accrue and how does it accrue and like why, where are the moats and, and how do we think about that?

### Partnership debates

**Eric Vishria** [1:11:04]
But also like the business model innovation. I think one of the things that's people don't understand about like why SaaS did so well versus traditional software was it wasn't just that it was a better delivery model and everything else.

There was actual business model innovation on it,right? Like you, you really did have this like subscription element that ended up being fantastic for both the company and the customers. Like it was a win-win situation. And so I think there's like that same thing actually exists in AI and, you know, selling by outcome and, and that piece of it.

But then, you know, wrapped up into that debate and discussion is like how much value just accrues to the labs? How much of the value just accrues to the semis? I mean, that's, that's a real discussion.

**Patrick O'Shaughnessy** [1:11:53]
What do you think?

**Eric Vishria** [1:11:54]
Like I am of the view that it all works, as I said. And I, I really like it's a very weird thing. It's like, will the CSPs do well?

**Patrick O'Shaughnessy** [1:12:04]
Yes.

**Eric Vishria** [1:12:05]
Yes. Will some of these neo clouds do well? Yes. Will the fireworks of the world do well? Yes. Will Nvidia do well? Yes. Will these like chip startups do well or, you know, some set of them? Yes. Like are we going to have edge inference on our phones?

Yes. Are we going to have like near edge inference on pops? Yes. Are we going to have big models in data centers? Yes. There's so much zero sum thinking, which is just like, okay, how do we cut up this pie and they're going to eat this much and like, oh no, no, no, no, Anthropic or whomever is going to eat 98% of the value and they're going to do all the drug discovery.

And I was like, come on. Like no, that's not what's going to happen. Like, and, and we have patterns like in the not that distant past where it's like this exactly manifested,right? When I say like I think everything's going to work and I listed off all these everythings, I, it's really important to understand that that doesn't mean that every company that's doing every one of those things is going to work.

It actually means quite the opposite of that. Like most companies in each of those areas are not going to work. And it's actually more important than ever to have real differentiation and to, to like really like take each of these thoughts to, to their logical extreme and understand like, wait a minute, you got to go all the way on these things and really be differentiated on it.

**Patrick O'Shaughnessy** [1:13:20]
Is there anything else that you have your eye on, whether it's in the funding market, in the technology world, like anything at all that, that you really are watching carefully?

**Eric Vishria** [1:13:28]
It's actually funny to me that some of the people are so, so smart

### AI jobs

**Eric Vishria** [1:13:36]
and yet

they're in this like tech world where they just, they're like kind of reaching these like deterministic almost conclusions, which they. Of like mass unemployment and like all of these different things. And, and I'm, I'm going to give you a really concrete example, which I think is just so good.

Take Jeff Hinton in, in radiology. So I think it was 2016 where he was like, we should stop training radiologists. Like AI's going to do it all better. And like, look, Jeff Hinton's like three orders of magnitude smarter than I am, but like could not have been more wrong.

But the actual thing that led him to make that statement or that conclusion was 100% correct, which is if you look at these radiology images, like we should be able to train AI to do a better job of like reading these things than humans.

And that's probably true. And, and, and actually I think the studies and areas have shown that to be true. And we have an investment in a company called New Lantern, which is approaching this. But the big hurdle and the big thing that it articulated was like, wait a minute, first off, all of the aggregated training data set doesn't exist anywhere.

And so like what you see is like companies going after like chest CTs or like very specific elements, but your typical radiologist looks at a whole variety of things every single day from x-rays to CTs to MRIs of all parts of the body and everything else.

And so an AI climbing in specific areas like chest CTs is like very marginally helpful because it's only doing that one thing, which could be one of 20 things or, or 40 scans that they read that day. And so, okay, so problem number one, you don't have the data, just like we talked about in robotics and everything else to, to, to train the AI.

Problem number two, the whole healthcare industry is oriented around reimbursing doctors for making readouts. And so like how is that going to work? And like who's, and there's liability associated with that. And there's repercussions of getting something wrong or missing something and there's medical malpractice and everything else.

So like how are we going to avoid that? And how are we going to get around that? So that's, that's like, like problem number two, just like real world stickiness. And so I think in the end, we're going to end up with this application where AI really does help radiologists.

It helps radiologists get more and more higher, higher throughput because the AI can do some parts and the radiologist does some parts and they're checking each other and everything else. And you do kind of weirdly end up in this co-pilot situation for, for some time.

And then you're going to slowly have the AI read more and more of the scans and build up and build up and build up. But the actual duration to get from here to there is going to take a long time.

And in the ensuing time, we need more radiologists, not less, because, oh, by the way, everyone's getting more imaging than they used to get because the cost of imaging's going down in a Javan's paradox kind of way. And so like my point on it is, is like you have someone very, very smart who really understands the capabilities, really understands what's happening, has theright data, but by not thinking of that data in the real world application comes to the wrong conclusion.

And that's how I think of the unemployment thing. I think it's just, it's almost the exact same setup.

**Patrick O'Shaughnessy** [1:16:54]
Eric, I love talking about markets and companies with you. An absolute blast.

**Eric Vishria** [1:16:58]
Thank you.

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