Do you remember 2007? It was boring, if not depressing when it comes to enterprise technology environments. You had a choice between x86 and RISC servers, with three viable providers in each segment. Most infrastructure domains look this way — two or three storage options, network hardware options, end user device options, DBMS options. CIOs and CTOs often faced even fewer choices in application software segments. I never heard about CIOs spending time with venture capitalists in that era. What would they even talk about? VCs in that era often focused on consumer, rather than enterprise tech.
Cloud infrastructure, SaaS applications, a revolution in cybersecurity and now AI have upended that unhappy equilibrium of oligopolies. Large enterprises less exclusively act as “takers” of offerings from giant technology providers. They integrate a wider array of technologies from a wider array of providers — some of whom are venture-backed attackers.
Acknowledging this, we did something new at May’s Technology Leadership Forum session. We convened a panel of venture capitalists to discuss and debate how they see the enterprise technology market. Ed Sim from Boldstart, Daniel Frankenstein from Joule Ventures and Will Summerlin from Autopilot engaged with the group on how enterprises can best work with VC-funded companies and how AI will change B2B software markets
Here’s what I wrote about it after the event:
Wow, there is some frustration out there! What do TLF members see from their incumbent software vendors? Slower innovation, degraded quality and more aggressive negotiation. They think some providers see their products as “falling knives” and have resolved to extract as much cash as they can from the portfolio before it declines into irrelevance. Others simply don’t get AI — they want extortionate rates for unimpressive capabilities that only reinforce silos between different parts of the environment.
TLF members found the discussion so valuable and VCs had so much fun that we decided to reconvene (after much scheduling gymnastics) for a Prosaic Times video discussion.
The conversation covers five things.
Whether vibe coding and agentic software engineering are the same activity — they aren’t, and the distinction matters for how you staff and govern AI work
Where the moat lives as the cost of producing code approaches zero — my answer involves latent information, and it’s more interesting than the conclusion.
Which sectors are most exposed to disruption — legal and accounting are the easy answers; I have a less obvious one.
How enterprise technology leaders should actually engage with the startup ecosystem — the VC-as-intermediary model turns out to be more consequential than most enterprises realize.
And whether you should leave a corporate job to found a company. Ed has a checklist for that last one.
Introductions
James Kaplan: Welcome to a special Prosaic Times podcast. Back in May, we did something different — we always get together about 50 or 60 enterprise CIOs, CTOs, and CISOs, and this time we invited a few of my favorite venture capitalists to share their perspective on the enterprise technology market.
All of our VCs had a blast and found the session both enjoyable and informative. All the members of the Technology Leadership Forum told us they appreciated the session and found perspectives they hadn’t heard before.
So I said, we have to get this group together again for a podcast — so let me start out with some brief introductions. Will, do you want to start?
Will Summerlin: My name is Will Summerlin. I’m a co-founder and general partner at Autopilot. We’re a growth stage venture capital firm, typically investing from Series B onward
Daniel Frankenstein: A co-founder and partner at a firm called Joule Ventures. Seed and pre-seed investor doing B2B software investments, coming out of the Israeli ecosystem.
Ed Sim: Boldstart Ventures. We partner at inception with technical founders when they’re in the idea maze — companies automating the autonomous enterprise, physical AI.
James: Daniel, when we were chatting before, you said that the interaction with the members of TLF was very eye-opening for you. Could speak to that a little bit
Daniel: One of the challenges we face in commercializing companies that aren’t from the United States is building that connection to corporate decision-makers and technology integrators. What we got from the TLF session was direct feedback on specific technologies — candid views on where enterprises are struggling to integrate AI solutions, and where their long-standing incumbents are trying to introduce AI but not yet meeting the mark.
It was a fascinating conversation, and it clarified where the real opportunities are for new technology businesses to carve out space.
James: We’ll say incumbent rather than legacy.
Ed, what was your takeaway? What did you learn from the day?
Ed: Everyone is truly, truly interested in deploying agents, and two, it’s really, really early, and I think that everything we think and know today may be irrelevant tomorrow.
People are talking about RAG and all these other things — like, hey, that was gone yesterday. And in fact, everything that we talked about almost since the last TLF, what was that? Six weeks ago? Is almost irrelevant again. The speed at which things are moving is staggering.
Will: We’re extraordinarily early on the adoption curve in terms of enterprise AI adoption. And I think it’s interesting in that there seems to be a disconnect from the myopic Silicon Valley view and the reality of enterprises. If you talk to a lot of founders or early stage investors or even growth stage investors in Silicon Valley, there’s this view that adoption’s happening very, very quickly, and we’re farther along on that adoption curve.
James: But what we heard from members of the forum is that we’re still very early. It’s still very much in the experimentation phase, moving to the broad-based adoption phase. And so I think that presents an incredible amount of opportunity ahead, but I also think it reflects this disconnect with the Silicon Valley view. You hear a lot in the ecosystem around token costs, and news stories about companies struggling with token costs. But the enterprises I know are spending trivially on tokens in the context of, say, a USD 1B or USD 2B or USD 500MM IT budget.
And that is a measure of quite how early we are from an adoption standpoint.
Ed: Some of the companies that were the more advanced ones were the ones beholden to one large language model provider.
The events of the last six weeks — particularly some where providers said, “Hey, we’re gonna interfere in some of these things” — have shown that not only do token costs matter, but also which model you’re beholden to matters.
So no one’s gonna want to be beholden to one model. Multi-model architectures have to be built, I think, because of that.
James: It reminds me of the era when some people believed you should standardize on a single provider.
Will: I think another interesting data point related to token costs is compute resources. We’re already in an environment where compute is radically supply constrained, and if you look at the growth rates of demand for compute versus supply for compute, demand is growing at a much faster rate than supply possibly could. [2]
Demand is growing because we see more broad-based adoption. It’s growing because agentic models use a lot more compute than legacy LLMs from a year or two ago. And on the supply side, we’re hitting all these constraints and bottlenecks related to supply chain in the semi space.
We’re entering a world where that gap is gonna continue to grow. And that’s all in this backdrop of what you said a minute ago, which is, the single digit millions of spend. And so I think we’re in this interesting environment where part of the world, is super early on that adoption curve, but at the same time we’re already hitting these constraints around compute.
If you want any semblance of an SLA from one of these model providers, you’re going to have to start committing to more meaningful spend. There’s this conflict and tension in betting your entire AI strategy on a single model provider.
James: The enterprise is maybe the shoe that hasn’t dropped yet in terms of token demand, and my God, what happens once the, spenders start using, scale?
The flip side is that you’ll probably see a lot more efficiency in token consumption. You don’t need to do all your non-deterministic processing at runtime. More of it shifts to build time — software engineering, data ingestion — with deterministic execution at runtime. And we may see a meaningful share of inference move to edge hardware entirely.
Ed: Think about models like OpenRouter. Companies now are about to sell a box — or a virtual box — that has a router for any frontier model on top, and then adds your own open source model on the box itself with H200s bundled in.
The idea is to make it very, very easy for people to build an agentic workflow, deploy it, and then actually maximize... I think we’re gonna move to a world of ROI per outcome. It’s no longer gonna be, are you automating me? But what is my ROI per outcome?
How does that look? And then there’s lots of other things involved in measuring success.
Daniel: Obviously folks have concerns about costs and limiting those costs. But right now, those costs are the actual real costs businesses face when they start using this at scale.
So the demand curve is absolutely outstripping the supply curve when it comes to compute. Enterprise adoption is going to be more of an increase on a dimmer light switch.
James: Enterprise adoption is always an increase on a dimmer — not a flip of a switch — which is why claims about AI impact on the bottom line feel like an attempt to be cute.
How could you expect otherwise? We’re what? Maybe a couple of years into the use of gen AI. How could you possibly expect that to have impacted enterprise operating income statements at this point?
The only impact on the income statement has been for some of those tech companies who are spending. I’m thinking about banks and pharma companies at the moment.
Ed: As far as the ROI on that spend — questionable.
Will: I do think we have small case studies where we’re starting to see it.
As one indicator: we have a portfolio company called Replit, which is an AI-native software creation platform. They allow anybody to create software using natural language. And what we’ve seen is in some cases the replacement of incumbent software providers where a non-technical user is able to create their own custom CRM system for a USD 100,000-a-year software license. The ROI on that is extraordinary. That’s like nothing you’ve seen from traditional software.
Ed: I still think the super complex stuff — Workdays — when people say, “Hey, we can vibe code that stuff away,” I think people have moved on from that really.
Whether or not those companies are gonna be massively successful, who knows? But I do think the whole idea of vibe coding things, the maintenance becomes absolutely incredible for some of these people as well.
Will: On one end, you have the bottom of the market that has simple use cases where they can vibe code their own CRM and displace incumbents. So I think that’s one end of the market.
The other end of the market in regards to enterprise adoption is just building more — where now you can use tools like Replit to build agents on top. And so it’s not that you’re gonna get rid of your incumbent provider, it’s just that you’re gonna build more agents on top of what you already have.
We see this primarily around internal tools, where to build a custom internal tool for your sales operations team or for your legal team, you had to either go work with a third-party software development agency to build that, and it would cost you hundreds of thousands or millions of dollars, or you had your internal software engineering resources, which would be expensive and you’re competing for internal resources.
Now, a non-technical person could go into a tool like Replit and build those agents or build those custom pieces of software on top of the existing architecture that you already have. And so I don’t think it’s a replacement for the incumbents in this case. I think rather it’s just gonna enable more software to be developed on top.
Daniel: When we got together initially, there was a real fear-of-replacement moment for incumbents.
We’re more in an all-of-the-above moment now, where a lot of incumbents have the customers, they have the data they own — which, by the way, is one of the key things to be competitive in this moment.
And that’s actually what makes this really exciting from a venture perspective: these incumbents are your customers, and also your acquirers.
You also have this entire new world to go after as well.
Ed: The most impressive thing was the idea that unicorn SaaS companies are all dead. This founder went all in on AI three months after ChatGPT came out, and today Salesforce announced it was buying it for — I don’t know — three and a half billion or something, which is above their ZIRP-era financing round. [1]
It takes very special companies, but maybe there’s a five percent chance that some of the very forward-thinking ones can create that kind of value. And combined with some of what Will said about Replit, there’ll be net new — more things banging on the system.
Vibe coding vs. agentic software engineering
Vibe coding and agentic software engineering are distinct activities: one is a non-technical user building something simple; the other is a professional engineer with 2–10x leverage.
The non-technical user persona is larger than the professional developer pool — and that’s where the near-term adoption surge is coming from.
In enterprise, the term “vibe coding” doesn’t travel well; the equivalent is building skills and automations on top of existing platforms.
The abstraction layer keeps rising — but semantics still matters. Syntax can be generated; data models and business logic still require human understanding.
James: What’s the difference between vibe coding and agentic software engineering? When I say vibe coding, I mean someone who’s not particularly sophisticated building something — maybe it’s a reporting tool or analysis. Versus a software engineer using a set of 2x, 5x, 10x tools.
Will: There’s a completely different user persona between the professional software developer that now has these agentic tools and the non-technical user who’s now empowered to create software. We’re seeing the adoption of the non-technical user — people in sales operations, people in legal, people in finance who have never written a single line of code, who don’t understand any JavaScript syntax — who are now empowered to create their own software in a way that is easy, simple, secure, compliant.
That’s where we’re really starting to see this adoption. The other important nuance here is that the pool of users who are non-technical is larger than the pool of users who are professional software developers. And so if you look at even a technology company, you might have fifty, sixty, seventy percent of the employees falling in a category that’s non-technical, and they are now empowered to go create software.
James: I wonder if we’re gonna see the rise of strats. In banking we have the strats who were either former traders who learned how to code, or former coders who learned about markets — very fuzzy. I was wondering if that might happen in more sectors and more domains.
Daniel: I agree that non-technical users are increasingly empowered to vibe code solutions. The problem is the influx of people who are vibe coding something, white-labeling it, and pitching it as a venture-fundable startup — and it’s not. Across sales enablement, marketing, the CFO stack, I can tell within five minutes of a pitch that there’s no defensible moat, nothing that takes particular expertise or meaningful compute to maintain. Buyer beware.
To use your strats analogy: to be a great trader takes years of domain expertise and longevity in the space. The same applies here. The people who can build something special are the ones with that depth. A vibe-coded sales stack with no moat is not that.
James: what do you think of non-technical founders?
Ed: I agree that more people building things is continuing to get more abstracted. I think the word “vibe code” and enterprise just does not really mix together — I don’t think you’re gonna walk into a CIO and say, “Let’s vibe code stuff.”
But it’s the same thing. It’s the same thing as what Will said — skills. The enterprises we talk to are using skills platforms, where you can type something into your AI of choice and say, “Hey, I want to build this, add this in, and then create a skill or automation.”
That is happening, and there’s gonna be hundreds of thousands of skills out there, and you’re gonna need to manage that and package it like software. You’re gonna need a version control system where there’s a skills repo for company XYZ, where these are the approved skills, and these are security vulnerabilities that can pop up, so let’s scan those skills.
What happens when Daniel changes something that Ed made? Is that better or worse? So how do you do an eval around that? So I, I think, I think we’re gonna be moving towards a world where skills become software,
James: If you remember the late ‘80s, we went from assembler to C — people said software engineers would go away. The engineering continued.
And I wonder if two things are true here. Abstraction matters, but you still need to understand the data model, even if you’re programming in a very abstracted way.
Ed: I think what you’re talking about is that you need specialized humans. Back to your earlier point — everyone wants to get automated, but they don’t know where to start.
Those specialists can help people understand what their workflows are so they can automate them. So you need specialized skill for that. And then two, you need specialized skill for evaluating whether a workflow is actually doing what it’s supposed to do. And then what happens, for example, when a model changes — is it actually gonna do better or worse?
People are gonna be needed, and even more specialized to understand those things. And I think people are finally understanding that right now.
James: Sometimes I draw a distinction between syntax and semantics, and in an AI software engineering world, we can worry a lot less about syntax, but semantics still matters. So Will, let me ask you, are we being overly negative on vibe coding? Give us the other end of the case here.
Will: First off, I know of several examples, including one where a company, built entirely on Replit is doing well over 100 million ARR, mostly enterprise revenue.
And so you, you can build real businesses, and I think we’ve gotten to a point in the technical maturity of the platforms like Replit where you have, implicit security, you can host on a private cloud instance. You hit all the enterprise check boxes, and a lot of the code that’s now hosted on tools like Replit specifically is as good as what you would get from a junior engineer. And so I disagree with the premise that you can’t build a venture-backable company on platforms like Replit.
Ed: I’m getting my popcorn. This is fun
Will: I agree that we as investors should operate with this premise that the cost to develop software is approaching zero over the next three, five, seven, ten years — just like the premium for having more software engineers is eroding. As that skill barrier is eliminated and the cost to build approaches zero.
Daniel: Of course there’s going to be vibe coded businesses built on companies like Replit that are gonna be absolutely venture fundable and extraordinarily successful.
The barrier to entry to put a product in the market has gone to zero, which means there’s so much stuff that isn’t defensible and isn’t venture-backable. It’s a huge increase in the denominator without a proportional increase in the numerator.
James: The percentage of effort associated with the core logic, relative to the effort required to fit into a broader enterprise environment, is decreasing. The pain is now in the integration: connecting to identity and access management, compliance, the whole stack.
So Ed, how do you think about this challenge of integration? Because in many places that will be the long pole in the tent.
Ed: The last mile in the enterprise is the longest, and it’s probably getting longer. It’s not easy for a large bank or a healthcare firm or a large CIO to bet their reputation on deploying your agentic technology.
There’s a lot of stuff you’ve gotta pass — all the SOC compliance and everything else. What you’re seeing is the idea of these forward-deployed engineers showing up and just getting it to work. Part of that is a response to how hard the last mile is.
The model is: build product, then finish the last mile with people. And the learnings you get from it are invaluable. The key question is whether the most important data to own is not just the data you have to run the AI on top of, but the private evaluation of whether the outcome looked really good — because that is really the knowledge of your business. That’s what separates a P&G from another marketing company.
Are these forward-deployed engineers going in there and helping you get those outcomes? And who owns the data? Are they giving it to you? Or are the forward-deployed engineers from the model providers themselves taking that data and using it to build vertical solutions?
That’s going to be the big battle. It’s what Satya calls hill climbing — reinforcement learning feedback. The question is: if you give that to a model provider and they’re helping you improve, is that the right trade-off to make?
I think we’re moving toward a world where people want to control their own evals — which means infrastructure on the edge, private models. We need a lot better open source coming down the line. It’s still not anywhere close to what the labs are providing, but that’s where we’re gonna move towards.
Where the moat lives when code is free
If code approaches zero cost, the competitive advantage shifts to ontology, data models, and latent institutional knowledge — the things that can’t be scraped.
The right model for the right task matters more than any single model: orchestration layer bets are more durable than frontier-model bets.
Enterprises that hand their outcome data to model providers for convenience may be signing their own death certificate — private evals are the new proprietary asset.
Architecture commoditization is coming but isn’t here yet; scaling laws are still working and a step-change in model capability is 12–18 months out.
James: sometimes I wonder if code becomes free or close to free, but the ontology and the data model becomes the source of competition.
Will: It’s very possible. This goes back to a point that we were talking about earlier, you probably don’t want to bet on a single model. It’s not just about cost, it’s also about, performance in different domains. And so I think that- that’s benefit of betting on something more at the orchestration layer, where, if you’re trying to do a task, we’re using software engineering as the example here, you might be writing, some more complex code, you might need a frontier model from one of the labs.
You’re writing simple front-end code, you could use an open source model. You’re doing a design task, maybe, Gemini or another model, from a, a different provider is better at that specific design task. So I think you’re seeing mixture of experts, to, solve these
As software code itself becomes a commodity, what matters is matching the right model to the right task — and everything else that isn’t captured by coding benchmarks: platform depth, integrations with Databricks or Snowflake, security scans, the full enterprise stack.
Daniel: think having specialized humans that really have domain expertise combined with owning your data and your moat, somewhat model agnostic and having that’s what the future it’s a mixture of really great humans, different models, and, owning your data. I, companies that choose to allow their outcomes and their data to be out there for the LLMs to scrape, because they’re, they’re prioritizing speed, ultimately are, are signing their own death certificate.
James: And does architecture get commoditized?
Ed: Not anytime soon. So the answer is not today, but I wouldn’t be surprised, like in 18 months if we kinda get there, because this stuff is changing so quickly.
Will: I don’t know, but if I were to place a bet, yes. Just look at the improvement in performance over the last three years. It doesn’t appear that we’re plateauing — in certain skills or certain domains it’s actually accelerating. It’s really hard to extrapolate out the next two, three, five years. But I think if the curve continues to look like it has the last three years, it’s very possible that architecture gets commoditized.
Ed: In other words, scaling laws are actually working, and if you have the compute, it’s improving. Everyone I’ve talked to on the models they’re training now says it’s still moving in the same direction. Hasn’t been proven false yet.
James: I’ll answer my own question and say my question was invalid. Any model requires context, so it depends on explicit information rather than latent information. Models will only get good at architecture to the extent that people using them figure out how to extract or capture the latent information they need.
Ed: How much will they be willing to hand over of how their system is architected and actually operates to a model provider? That’s the question.
James: Do they even understand enough to be able to write it down in such a way that it can be consumed? Taking latent information and turning it into explicit information is not a trivially easy task.
Ed: It’s the last place that hasn’t been automated yet. If you’re thinking about most of the automations — it’s on the edges. IT ops themselves have not really been automated. Partly because people are afraid of AI just shutting things down. But I do believe we’re gonna move toward a world where there’s more trust with AI around automation of IT.
James: Let me ask a related question, maybe one that’ll be controversial. So I, I, I got myself told I was up a tree because I offered the thought that we’re gonna see more strats in the future, more, convergence the line between, business optimization and technology execution.
But then I offered the hypothesis that it may be easier to teach software engineers about the business domain than it will be to teach people in the business domain enough about data models, for example, to, sufficiently engage. I don’t know whether you call it vibe coding or agentic software engineering.
Daniel: I spend almost all of my train technical people on the business side of things. so, so I probably over-allocate to frustration in that department. So, maybe that skews my answer, that maybe it’s a little easier to teach business people, the software side, but I could go either way.
James: Ed, what do you think?
Is it easier to teach a bus- a business analyst software engineering, or easier to teach software engineers about how to think about a business problem?
Ed: I think the underlying models can be so good that what matters is the expert who knows what to prompt and ask. If you actually look at how to use LLMs today, it’s all about how you prompt — two of us could sit next to the same thing and get vastly different outputs if we’re trying to achieve the same goal.
Will: I completely agree with Ed. There are so many nuances to different problem sets or workflows. The people who intimately understand them will have access to tools that can manage all the technical complexity.
One case study from us: we vibe coded our own custom LP portal, and my co-founder, who’s not technical but who’s been running all of our operations, built this in probably 12 hours. And it’s better than anything we could find from third parties. Daniel and Ed probably lament the challenges of fund admin more than anyone else.
James: Nothing’s more fun than fund administration
Will: it’s the great joy of life. there are so many little nuances to it that I think y- you just wouldn’t understand unless you’ve spent months or years in the trenches
Ed: by the way, that goes back to my other point, is that your person that built it and you are the experts, and , you’re the ones that are gonna actually evaluate it as well. You’re the only ones that can keep evaluating everything else, right? So that’s kind of your workflow, that’s your model.
And, and your same model could be deployed at Daniel’s place and my place, but how we evaluate it and what successes might be different, right? And that’s kind of what I mean by the institutional knowledge and workflow that, that I think those private evals is so valuable to capture that
Daniel: One of your great moats is domain expertise. domain expertise can be found on the business side, and it can also be found on the technical side. It depends on what problem you’re solving and who’s your constituency.
James: I had a discussion with a bunch of 22-year-olds a couple months ago, and they were, you know, having the typical 22-year-old, ”Oh my God, any job I might consider is going to be replaced by, by AI.“ They asked what I thought would be a good thing to learn, and I said data modeling.
I don’t know whether that’s business domain or technology domain expertise, you both need to understand all the nuances of the business process, but there’s that, process of abstraction, that I think becomes incredibly important, which people who are only deep in the business domain don’t always get.
Which sectors face the most disruption
Legal and accounting are the obvious targets — high information volume, largely objective outputs, limited customization requirements.
James’s less obvious answer: B2B interactions broadly. They haven’t changed in 15 years while B2C was transformed. Gen AI’s ability to ingest unstructured data makes this the next frontier.
Ed invokes Jevons: freed capacity can be absorbed by new demand. Companies that cut headcount fastest are already adding back — different kinds of people, more senior engineers, specialists.
Robotics is the adjacent disruption no one is talking about enough. Ed’s portfolio company has 500,000 hours of manipulation data and is deployed at four large manufacturers — the ChatGPT moment for physical AI is close.
Okay, Daniel, predictions about which domain, technology domains do you think will be disrupted the most?
Daniel: I’d say I’m still a big believer in places where there are largely objective answers to questions that require sifting through a lot of information. I think legal and accounting are two of the low-hanging fruits.
Ed: I think, James, that everything is up for absolutely massive disruption right now. But I’m also a believer in Jevons paradox — because if things are cheaper and you can use AI more, you’re gonna be able to do more. And I do believe in a world of more productivity. The tech companies I’ve seen that have automated the fastest may have cut headcount and used AI as an excuse, but now they’re adding. They’re just adding different kinds of headcount — more senior engineers, different kinds of people. Perhaps the specialists you’re talking about.
So I think it’s gonna disrupt everything short term. The next two or three years will take a while to absorb the hit. But I believe in the longer term that we will actually be more productive as a society and generate more output with less cost. And hopefully you see those GDP growth numbers increasing.
James: Will, what do you think?
Will: I completely agree. If you look at any technology cycle throughout history, for the most part it’s led to wage increases, job creation, new goods — and I think the same thing will happen here over a ten to fifty to a hundred year time horizon. What we see in the next five years: the most disruption today is in two categories. Where incumbents have low-quality software products and have not innovated, you can now build that version yourself — and often it’s better, cheaper, and higher quality.
That’s our case with this fund: we built it ourselves because the incumbent alternatives were not high quality, they were very expensive, and there was a lot of low-hanging fruit.
I think the other area where we’re starting to see more disruption is in domains where software is highly customized — back to the point both Ed and Daniel made, which is that it’s really important to have deep domain knowledge specific to your workflows.
The way we build an LP portal might be different than the way Ed wants to build one, because he has nuances in his business and portfolio that we don’t. Where you have a high level of customization required to make software work for your business, there’s a lot of opportunity for disruption — both disrupting third-party software and disrupting the incumbents who have mediocre products that work okay for everyone but not great for anyone.
James: it’s what I call the one size fits none challenge.
Ed, you said in the session that you looked to fund companies that were problem-obsessed rather than solution-obsessed because, you, you iterate through different solutions, but problems are eternal.
Messiest part of, the advanced economies is B2B interactions, and that’s not just CRM, but the broad scope of all the large, complicated businesses. , Over the past fifteen years, maybe twenty years, we’ve seen an utter transformation in B2C interactions.
But many B2B interactions look a hell of a lot like they did ten or fifteen years ago, and I think that, the, the ability of, gen AI to, to ingest complicated, unstructured data to transform those domains.
Ed: I, I love that. And by the way, can I add one thing
James: Please
Ed: we didn’t talk about software and robotics,
I have a company. We’ve built our own LLM for robotics, with our own data, and we’ve proved that scaling laws actually work in robotics as well.
So I feel like we’re almost at that ChatGPT moment in robotics where a generalized robotic software model will be able to do a lot. So in other words, once upon a time where you would have to have 100 different robots or 100 different kind of brains operating and, and doing 100 different tasks, you can now have one.
And I can tell you this, that the market is moving so fast in that space, so manufacturing, pick, pack, and ship, auto manufacturing, all of those kinds of things are getting transformed. In the next three to five years, you’re gonna see a massive, massive dislocation in those markets as well
James: Is that an LLM for robotics or is that a type of world model, right? I mean, are we moving from language to physics when we talk about that?
Ed: there’s three different kinds. So the one that we have is a basically a foundational model for robotics. Other people are trying the world model approach, and what my company is doing is they’re getting their own data, right? There’s no Reddit for data.
They , ship these 3D-printed devices that goes on people’s hands that are very, cheap. we take video of people manipulating these things as if they had these grippers, and we’ve got 500,000 hours of data.
And now we’re deployed at four very large companies doing very complex manufacturing tasks all within two years. This is massively moving so fast. [3]
It’s basically building off the shoulders of giants? Because it’s already been done before, and now you’re applying it to a different realm,
James: Daniel, how bullish are you breaking the, breaking the barriers between the physical worlds when we come to,
Daniel: How can you not be bullish on it, right?
James: What’s the pace, do you think?
Daniel: One of the common themes we’ve discussed throughout this conversation — which, again, was part of the reason why I so enjoyed our in-person discussion — is the pace of innovation. It’s at a lightning speed.
Literally every week things are changing. Every day things are changing. That’s moving at a breakneck pace. But the integration of those changes — the integration of products that encapsulate some of this innovation into large businesses — is very slow.
And so I, we’re, we’re, we’re it’s, it’s one of those where you’re like in an F1 race where innovation is just lapping, the jogger, which is the, the, the enterprise integrator.
And, and so the question is how do you lean out the window and throw something that the jogger can actually digest? That’s, where the rubber hits the road, not to take the, this too far. But, that’s, where the bottleneck is.
Will: Agree with, with, everything said here and that there’s an incredible amount of opportunity for AI in the physical world. you look at some domains in manufacturing where AI is now used to inspect batteries coming off an assembly line, and it can do it, ten times faster and, fifty times better than a human inspector can.
And that’s a job that humans don’t like doing. You wouldn’t want to sit under bright, bright fluorescent lights all day and look at batteries. So I think there are incredible amount of opportunities we’re already seeing implementation, the ROI is there. But I will say, I think that humanoids broadly is probably in bubble territory.
And I say that for two reasons. I think number one, we’re very early on the S-curve and that, the, the technology is not quite there. when you start to do things in the physical world and chain together different skills and make that work successfully in an enterprise environment, the, the, the success rate has to be very high in each skill.
You know, if your success rate is ninety-eight percent in one skill, ninety-eight percent in another, ninety-eight percent a third, you’re gonna fail a lot of the time because you multiply the probabilities together.
You really need to get to like ninety-nine point nine nine percent accuracy in each skill set or each domain in order for this to work reliably over time. The second problem I think we have in the humanoid space is people underestimate how hard it’s gonna be to scale up manufacturing.
Like you go talk to the team that, that scaled up manufacturing for the Model 3 at Tesla, like it’s a really hard thing to do to go from, you know, zero to scaled production of, of complex hardware products.
And I think the sort of view that we’re gonna be shipping millions of these in two years or three years is, is probably a bit shortsighted.
Ed: One comment though. I, I agree with you on humanoids, but my company’s actually going after, robotic arms, So when you control the variables with just this and a gripper, you don’t need five joints on it to do things. that actually allows you to deploy much faster.
On the humanoid side, there’s so many more issues. Battery life is another one. Safety, what happens when the battery runs out? Does it fall on somebody? there’s so many issues there to, to your point.
The, more you constrain all the, variables and focus the processing power on the task at hand, the better off, you are in terms of deploying these things with real accuracy.
How enterprises should engage the startup ecosystem
Routing startups through innovation teams is a trap — those teams are typically disconnected from the business owners who actually have the problem.
VCs are intermediaries in two directions: LP → founder, and founder → enterprise. The second role is underappreciated. They filter signal from noise, translate languages, and set expectations on both sides.
Enterprises build reputations too. Kick the tires on three companies and do nothing and VCs stop bringing deals.
The right frame for CIOs: decide where you want to sit on the adoption curve, then match the VC tier accordingly — seed investors for early bets, growth investors for proven businesses.
James: Let me pivot a little bit. I’d love to discuss a bit how enterprises, CIOs, CTOs, CSOs, others, should interact, with venture funds and venture-backed companies.
So Daniel, let me start with you. Advice would you have, for, technology executives or IT executives at big traditional enterprises as they engage with venture-backed companies and potentially as they engage
Daniel: Absolutely. First and foremost, large businesses are structurally challenged when it comes to engaging with younger, earlier stage businesses. Some organizations have tried to address that through innovation departments or groups built to test technology. We tell our portfolio companies to avoid those like the plague, because they are typically disconnected from the business owners themselves — the folks who actually need solutions to solve problems. We as a fund try to serve as a bit of a filter there, because it’s really hard as a startup to knock on the door of a Fortune 1000 business and get any attention. Our hope is that when we help knock on those early doors with our portfolio companies, that comes with a vetting process, and an understanding that we’re gonna ask for that time only when there’s good ROI going both directions. We tell our portfolio companies to go directly to the person who has the problem — try to engage directly with the business unit.
James: Will, what are the mistakes that large companies make when they interact with venture-backed, companies?
Will: Trying to push companies through an innovation team that doesn’t really own the problem is often ineffective and wastes everyone’s time.
As an enterprise customer, you have to decide where you want to sit on the adoption curve. If you’re willing to be an early adopter and really partner with a company, I think that can be incredibly fruitful early in their life cycle — you’re gonna get a lot more attention and probably more customization specific to you.
If you’re not willing to put in that effort, if you’re not willing to view this as a partnership, you’re probably better off waiting until, other early adopters have embraced the startup and it’s gotten to this inflection point where it’s now a more mature company, with a fully baked product.
And then third, you know, a lot of, a lot of larger companies now have venture programs where they’re investing in startups, especially investing in startups that they’re customers of.
If you’re gonna have a venture program, connect that directly to the business unit that’s actually buying the software and, and understand why you’re making venture investments. Don’t just do it because you think it’s cool
James: Okay, Ed, why is it so tough for, for large enterprises to engage with venture-backed companies?
Ed: Filtering. There are just thousands and thousands of startups, and our job is to filter through all those and invest in the ones that make sense. When you partner with venture firms, you should partner with all different kinds — because there are folks that go super early, at inception. There are folks that go a click later. There are folks that invest after the company has USD 30MM of revenue.
You need to understand where you are on the innovation curve and figure out which venture capitalists you’re willing to engage with to see what they have.
And we as VCs have to be very careful as well. We’re not gonna throw something at an enterprise knowing full well that it’s gonna be an 18-month sales process — because that could also kill the startup, but also we don’t want to waste your time.
It’s all about trust and reputation. On both sides, you have to figure out what you’re willing to show an enterprise to say that it’s ready for deployment. Maybe the company and team have actually built something new, and they’ve done it 30 times before — so therefore there’s trust.
And on the enterprise side too, they build reputations as well. Because if you kick the tires for 18 months and do nothing with three companies, VCs will say, “Don’t ever go to them again.” And therefore that enterprise might miss out on innovation.
It’s a two-way street where you actually have to trust each other.
James: How do you think about the filter? How do you think about who to speak to versus not to speak to?
Ed: Well, it just comes down to people though, right? It’s all about the people, right? You want people that move very fast that actually can solve a problem today, but also presents a kind of vision to solve your problem 18 months from now.
And that’s what we look for, and hopefully you come to us and we can present the founders that actually do the same
Will: The bar for speaking to somebody is very low. I’ll try to speak to as many people as possible. But for us, us to actually make an investment, we’re, we’re investing at a different stage, and so typically the companies that we’re investing in already have tens or even hundreds of millions in revenue. It’s a real business at that point, and the question is not will this succeed? Do they have product market fit?
The question is, what is the sort of revenue scale gonna look like five years from now? And what are the unit economics gonna look like five years from now?
We’re asking a fundamentally different question , than Ed or Daniel are when they’re making investments. We have a set of criteria that we filter to. We look for technologies that are inevitable, . There’s adoption, but we’re early on the S-curve. We look for markets that are default dominant, where an outcome is gonna be an oligopoly or a monopoly.
To Daniel’s point earlier, we don’t like markets that are commoditized, where you have hundreds of companies without any real moats. And then finally, we look for what we call dynastic DNA. It’s really about the people. Is this founder pursuing their life’s mission? Do they have the wherewithal and the fortitude to push through over the next ten, fifteen, twenty years to build a large, enduring business? Can they attract amazing people around them? Do they move at a high velocity?
James: So it’s your job, Will, to, speak to people, but if you’re a CIO or a CTO, it’s like, well, it’s my job to keep the business up and running, and I got, some number of hours per week to go speak to people, and an infinite amount of demand to be spoken to. So what’s your advice for someone who in an enterprise, role
Daniel: very nature of the venture business and the very nature of us having a funnel that ultimately results in a set of investments means when we are, making intros and trying to have conversations with corporate decision-makers,
if you ask for a busy person’s time and you do not use it effectively, you will not get it again. it is my, desire to be able to go back to the same folks time and time again when there’s an opportunity that’s relevant.
And so by the time I’m asking for the time of an enterprise CIO or an enterprise CISO, I’ve done my homework. I have done the work to know that even if it doesn’t result in an investment for us, it’s going to be a good use of both of their times.
Ed: It’s always not us pitching them. I find the most value is when I talk to CIOs or CSOs and say, ”Hey, what’s top of mind for you?“ Maybe once a quarter I have, like, a dozen folks I’ll catch up. ”What’s top of mind for you? What are the biggest problems you’re seeing? What are the existing, incumbent vendors not providing for you? What do you wish you had? What are you thinking about building?“ Right?
Because a lot of the things that startups end up creating are things that companies want to build themselves, right? So I think that’s part one. So once we understand that, we can say, ”Hey, I’ve got this one or that one.
We funded a company, um, you know, like a year and a half ago in the agent identity space. I, I walked into a very large bank and we just said, “Hey, look, I would love your feedback.” And, and they had eight people in the room. We spent an hour together. We, we mapped out what our architecture would look like, what problems we’d solve.
And then guess what? We went away, built stuff, raised rounds of funding, came back a year later — not only did we show that company that we had built everything, but we also showed them what the forward roadmap was.
And I remember as I was leaving the room, three of them were huddling in the corner and saying, “Holy shit, did you see what they built in the last, you know, year?” We built it really fast, and then we had a future vision that kind of aligned with theirs. Another thing: we’re not expecting a sale. We want feedback. Why wouldn’t you provide feedback and spend the time? And then if we come back and make the mark, great.
James: You assume that VC funds serve as an intermediary between limited partners and founders. They channel investment to companies. But what I’m hearing here, and have heard in previous discussions, is you’re just as much an intermediary between startup companies and the enterprise — helping founders understand the enterprise, and helping the enterprise understand how to engage with startups.
Is that, is that a fair way of thinking about it?
Daniel: Absolutely. I have two track records. I have the track record to my LPs, and I have the track record to the entrepreneurs we back. And the track record that is for the entrepreneurs we back is: how many customers did I help you get?
How many versions of your product did I help you think about? how many key hires did I help you with? those are all critical things that are on the company support side of things. and it really involves being an intermediary with the enterprise, and understanding how to translate different languages.
A startup speaks a different language than a large enterprise. You gotta have somebody in the middle ideally making that translation
Ed: Look, I’ll toot our own horn here for the three of us — I’ve been doing this for 30 years, and not every venture firm does this. So if you’re a founder building in this space, come to guys like us who actually do this every single day, because we learn a lot during the process.
But all I’d like to say is our job is to help it, help you make it an unfair fight against your competitors so that, so that when you get funding from us, that we’ll , accelerate your path to your next round of funding and kind of product market fit or post-product market fit, depending on the stage.
One or two influential customers at the beginning of a company’s journey, like, of their first five customers, can actually reap tremendous dividends, create so much value, multipliers of value based on the contract size.
By the way, the contract size does not have to be massive. perhaps, James, that enterprise gets a massive discount in year one. The point is those referenceable customers are so massive for the success of the business because what you really want that CIO or CTO doing is talking to the next four or five large customers coming down the pike.
James: Will you invest in s- companies a little bit further on in the journey? How is your experience or your model similar or different?
Will: For a company that’s just starting out, having an Ed or a Daniel on your cap table is really important. It’s almost like they’re an extension of your founding team, to the extent that they can broker these relationships and help you navigate the enterprise. For us, the set of problems that a founder is facing is different.
They have often overwhelming market pull. The problem is not finding customers, the problem is scaling up the organization to meet the demands that you have. And so we try to help out a different way. It’s always great to bring customers. We try to do that where it makes sense. But I think often it’s more about talent.
It’s bringing in people who can scale, help you scale from 100 million in revenue to a billion in revenue. It’s more sort of strategic finance, and so how do you think about bringing in some of the larger crossover funds that will be shareholders, through an IPO and into the public markets?
It’s thinking about the next act. You have one product line that’s extraordinarily successful. You know, how do you start introducing a second product line in parallel, your second act? And so often we’re helping founders, navigate a different set of challenges than, than Daniel or Ed are.
Should you leave to found a company?
The most compelling founding stories come from insiders who experienced a gap, built a workaround, and realized others had the same problem. Coralogix is the archetype.
Ed’s checklist: Is this a market of one, or a market of many? Have you talked to peers at other companies? Who owns the technology? Who are you bringing with you?
Domain expertise is necessary but not sufficient. The ability to abstract — to see that what solved your company’s problem could solve a category — is what separates a founder from someone who built a useful internal tool.
Will’s criteria at the growth stage: problem obsession, grit, and “dynastic DNA” — the willingness to push past a billion-dollar exit toward a hundred-billion-dollar company.
James: So not infrequently, I have people working in enterprise technology organizations come to me and say, “Hey, I’ve done something really cool. I think maybe I should build a company out of this.” Maybe the grass is greener outside. Daniel, how do, how should someone think about that,
Daniel: Some of our most successful investments have come from that exact process where somebody is, at a business, they are experiencing a gap or a problem, and given their knowledge of the problem, their knowledge of the domain, decide, “Hey, I’m actually going to leave the company, and I’m going to build the solution I was looking for,.”
And, one of the, a, a really exciting company where we were involved from day one in the observability space called Coralogix is literally exactly that story, where , the founding team there was literally a product team at another business that built a solution they couldn’t find to buy. That’s a , very compelling, thesis. the challenge is a lot of times that thesis isn’t always right. The best thing to do is talk to guys like us, right? , Have a conversation while you’re still, ideating. Ed gets involved a little earlier than we do, we regularly meet with and advise founders that, aren’t yet founders.
They haven’t yet left their job. They don’t exactly know what they’re doing, but they’re really smart. They know their domain. They want to stay in the domain, and we want to stay close to them.so you gotta test the thesis, but you gotta test it with a toe in the water, not, jumping in the whole way, y-before that.
I, I, I always, when, when founders come to me and they say that they’re onto something big, I always say, “Show me your work. what validation did you do to, to achieve that conviction?” and to the degree we can be helpful in building that case with the founder that leads into an investment, great. but you gotta do the work to validate.
James: Ed, what do you think? And in particular, how should someone, what questions should someone ask themselves or him or herself,
Ed: There are a few lenses. You should have conversations with folks who do this. two is I think you should think about, is my solution a market of one, a market of a few, or a market of a many, ? Because I, I’ve talked to lots of folks building stuff, and they think they’re on their aha moment, but really their organizat- organization is so special that, I, I think it’s a snowflake, right?.
James: Abstraction is important
Ed: Do your homework, right? Also understand what’s in the market, ‘cause a lot of times also when you’re internal, you don’t understand what the other startup vendors are doing or what the other large incumbents are doing.
Like, why? Why is this built? Why’d you have to build this internally, doing a startup is really effing hard. You have to be absolutely stupid, insane, and crazy to go do a startup right now, and so I’m just warning you right now, it’s not all glory. It’s actually mostly 99% pain and 1% glory, right?
And, and so, so you better be pretty, pretty sure about that when you go out. And so let’s just have this conversation. A few things I’ll ask, will be understanding how’d you come to the conclusion that there’s just more than you. Have you talked to other people at other companies, your peers, for example, at other companies that might want to buy this solution? Two is who owns the technology? Are you gonna actually go leave and rebuild it yourself, or are you gonna spin this out of a larger org, and then perhaps maybe give them some equity, which also opens this a whole, a whole new ball of wax. I’ve tried that before, and that ends up becoming a very long legal nightmare.
I’m not advocating for that. You might be better off just going out and creating your own thing. If you’ve been locked in a corporate, corporate environment for, for 10, 15 years, man, I, I don’t know if you can... if you have even the stomach, that you can be a founder. so prove it to me, man. your, your salary’s gonna be 1/10th and 1/15th what you had before. It’s all gonna be about the equity. Who are you bringing with you? i-i-if you tell me, “I got five or six people I’m bringing with me” , I’m gonna know that you can recruit people, right?
I mean, those are the things you’re gonna have to think about once you get past even the does this make sense.
James: Will, what are, what are, what’s your observations about people who make for successful founders?
Will: I think first off, you have to be obsessed with a problem that you’re solving. I think that’s, that’s table stakes. And I think second, you’ve gotta have the grit and the fortitude to push through all the pain, all the moments of near death, all the suffering, and keep going and going. And for us at least, in the stage we’re investing at, we’re investing not because we think this could be a billion-dollar company.
Often it already is a billion-dollar company. It’s because we think it could be a $100 billion company. And any rational founder that got a billion-dollar offer , would sell their company and make tens or hundreds of millions of dollars and go retire.
It’s the people that, that have that fortitude, that grit, that drive to keep pushing farther and farther. And I think, it, it can happen. You look at one example is Daniel Dines, the founder of UiPath. he worked at a big corporate before, and, he decided to leave and, and start UiPath, and it took him, I think it took him, if I’m not mistaken, like six years to go from, zero to a million in ARR.
It was just this brutal slog. And so, you know, you look at that, and then obviously they went from, one to 100 million very quickly and, and now it’s a, it’s a large public company. And that’s the journey you have to expect going into this.
Daniel: Well, and also someone who can win, right? I, one, we talked about problem-obsessed. Absolutely. We talked about grit. Absolutely. we define it internally as like, do we think someone can win in their market? because it’s not enough to just have a good product.
It’s not enough just to, you know, have one of these pieces. You have to have all of the pieces, and you have to be resilient the entire way.
It’s actually one of the reasons why we focus on the Israeli ecosystem. , Really resilient founders, that have to persevere and go through a lot, that deal with real-world problems at a much younger age, that do their national service, that deal in real-world environments. and it creates a, a bit of a different type-built entrepreneur, someone that can, get up when they’ve been, you know, pushed over and, you know, when the market punches you in the face, you gotta get up. and that is , critical to being a successful founder.
James: All right. Let me ask for final words.
Will: I would say if you are a CIO or CTO trying to figure out where to spend your time, look inward. A lot of the most successful AI companies we’re seeing, at least at the application layer, are seeing incredible pull from the bottom up rather than being pushed top down. Go to your functional leaders, figure out where their problems are, figure out where they have pull, and focus your time and energy there rather than filtering all the people banging on your door trying to sell solutions top down.
Ed: Um, I would say that the time is now, right? Just don’t wait. Start small, start somewhere, just get rolling right now, and then don’t be afraid to throw things away. because I can promise you that by the time this podcast probably comes out, I know you’re fast, James, but the world may turn again one more time or two more times, right?
So whatever we say might be 90% irrelevant and 10% relevant right now. So I think the world’s just moving so fast. If I were a CEO or CIO, I would just build my own stuff on the weekend just to really understand the power of it.
That’s the only way you get to know these things
Daniel: We’re entering a really interesting moment where there’s not such a stigma anymore to try new things.
Companies need to look inward — to Will’s point — to really build the capabilities to integrate. And I think there’s openness now to try new stuff.
You’re not gonna get fired anymore for giving something a try. And I think that makes it a really exciting time to be an enterprise executive who can, to Ed’s earlier point, really put your foot on the gas on productivity.
James: Thank you all. This has been terrific.
Implications for CIOs and CTOs
Three things you probably expected going in: VCs are optimistic; enterprise AI adoption is early; legal and accounting are the obvious targets. One thing you probably didn’t: enterprises build reputations with venture funds too. Bring three companies through your procurement process, commit to nothing, and the introductions stop. The world is far enough from 2007 that waiting is no longer neutral. It has a cost now.
Footnotes
[1] Ed is referring to Fin, the AI customer service company formerly part of Intercom. Salesforce announced the acquisition for USD 3.6B — above Fin’s ZIRP-era financing round, as Ed notes.
[2] The imbalance is structural, not cyclical, and the data supports Will’s claim. On the demand side, Epoch AI data shows frontier model training compute has been expanding at roughly 5× per year since 2020. On the supply side, the global stock of AI chips is growing at approximately 3.4× per year — aggressive, but mathematically insufficient to close the gap.
The constraint is no longer primarily silicon: transformer lead times have stretched from one year to five years (Bessemer Venture Partners), and RAND Corporation projections show single training runs scaling toward 1 GW of power per site by 2028 — approximately the output of a nuclear reactor.
Broadcom’s Q2 disclosures illustrate the production gap directly: bookings for AI accelerators surpassed USD 30B against USD 10.8B in actual shipments. The one credible counter is algorithmic efficiency improvement (~3× per year), which could flatten demand before infrastructure matures — but that remains a scenario, not a trend. For the physical infrastructure constraints in more detail, see my conversation with Harqs Singh of InfraPartners.
[3] Ed is referring to GeneralistAI, a foundation model company for robotics — Boldstart Ventures portfolio. Ed is an investor.
