Enterprises are not Adopting AI Yet

I think the biggest one is, you know, it’s really important to think not just about product market fit. You know, every first time founder is concerned product market fit. And there’s this adage that, like, you know, a second time founder thinks about distribution. My take on it is a little bit more nuanced, which is it’s still product market fit, but it’s about figuring out the right product strategy that marries with an effective go to market model that lends well to that product. Right? So certain products work well in a bottoms up viral organically adopted way.

Certain products are single user versus team centric. You know, accordingly, I think you need to design a go to market model, whether outbound sales or performance marketing or organic and viral growth that really pairs well with that particular product dynamic. So in hindsight, we didn’t think enough about go to market model in the early days of the company. We just focused on building a good product. And happily and and luckily, that was good enough to get us to the next phase where we did start thinking about go to market.

But I think it’s something that we could have thought about a lot more.

Yeah. I think one of the differences was, we built the product with the ability to become team centric. So we built we spent a lot of time engineering the back end to be real time collaborative in nature, which was not a small technical feat. We thought about team centric use cases, and when we started putting out templates, this was a little after we launched initially, you know, we built some templates that were more solo user centric, some that were more team centric. In hindsight, you know, I wish we had put even more emphasis on the team scale use cases and especially those use cases that involve larger teams.

Right? I think we got to those sort of organically, but it was a little bit more diffused. So early on, we got a very large range of different adoption. And I wish we had guided our adoption more and earlier towards the the team centric use cases with the anticipation that those would be the ones that, a, monetize better, b, you know, lend to more go to market models. Right? You know, if you’re taking a single user product out to market, it’s just harder to make performance marketing work.

You know, you can’t really make outbound sales work. Right? The economics of pitching a single user who pays a single and relatively small dollar cost to you is just hard. So being more committed to, from the earliest days, the team centric use cases and even larger teams would have lent better to early on at a pairing of more aggressive go to market models.

I think it’s a genuinely tricky question. There’s nuances. Right? I think for products where the use cases are not as self evident, the importance of verticalization, especially early on, is more important. It’s kind of the crossing the chasm versus inside the tornado metaphor where early on, I think, especially when the market is still trying to figure out what is this thing, it’s helpful to really go deeper into a few different use cases and make sure you fully solve those while at the same time preserving the optionality in the long term, you know, not overcommitting the platform or the product roadmap in such a way where, you know, you’re so brittle, you’re so hard coded for those narrow use cases that you can’t expand outward.

So I think these are pity statements that are easy to speculate on in the abstract. But when you get really specific in terms of what is the use case, what are the the functions, what is the product roadmap one way or the other, and the go to market model, I think that’s where it gets really interesting.

They’re different, but potentially going to be more profound. The reason for that is the benefits of cloud compute are pretty easily. Right? You don’t have to manage your own servers. There’s infinite scalability. You know, as hardware costs go down, you kind of instantly realize the the benefits of that. Right? Like, we just pay our AWS bill every month. And over time, over the long term, you get more compute, more scalability. By all means, there there’s a lot of power and value to the cloud compute model.

I think AI, while still in its infancy, and especially LLMs, are gonna be profound in a very different way, which is, when you think about the broad range of knowledge work, every function, right, whether it’s legal or finance or even the more creative functions, marketing, etcetera, you can just see the glimmers starting to emerge of how a really, really broad set of work can be automated or accelerated with especially Gen AI. I think objectively, it is able to produce useful output. Right? And it’s easy to squint and imagine as the capabilities of these models only get better and better.

You know, it’s really about the breadth and the depth of value that can be created with Gen AI. If you believe that we’re just at the beginning of this curve of not only adoption, but technological improvement. Right? I think with Gen AI, we’re gonna see more and more use cases get powered by AI. Right? And it’s gonna attack all these different functions, industries, etcetera, piece by piece. And so the total amount of disruption, I think, just a lot bigger.

One thing I’ve learned is is just that it’s super early. I think a lot of customers are still trying to to figure out what you can do with AI. So to some extent, I think we’re still limited by the broader understanding of what Gem AI is capable of, what its affordances are. So, you know, what are its limitations? Obviously, there’s, you know, hallucinations and and accuracy issues that are a major challenge. Right? So there’s all these limitations for sure. We’re still in the education phase where I think every enterprise is getting savvy as we speak or trying to get savvy as we speak in terms of what is an LLM?

You know, what are these basic primitives? Right? You know, I’m hearing the words even vector database and and other technical concepts you talked about in the enterprise. To me, that’s really exciting because I think once there is this baseline level of understanding of what exactly this technology is and what it’s capable of, that’s when the real fun will begin, where we’ll see enterprises actually get really smart about applying this powerful technology to specific problems that they have.

One difference between Gen AI and, you know, traditional enterprise tech. Gen AI is not gonna be confined to just enterprise. The way that we’ve all become aware of and and, you know, it’s become top of mind is through the consumer applications, whether it’s it’s ChatGPT, MidJourney. You know, these products have have gotten real scale. Right? So it’s not just the, you know, Silicon Valley elites who are adopting this product. I’m just

On what basis? Because I’ve heard all kinds of different, you know, perspectives on this from employees, including our own.

Yeah.

I think we have to, you know, get very, you know, solemn and serious about the very real economic implications of AI. Right? I tend to be a believer that AI has the potential to, you know, actually lower the cost of goods and services production and therefore increase also the demand. I think we’re in this weird transition phase where the risk is that everything happens very quickly before we as a society know how to kind of adapt. But I do think there is potential for every person to figure out how to augment their capabilities and ultimately become more productive and and even more valuable because of this human and AI symbiosis.

The technology has actually reached this breakthrough point even now public market investors are pricing into our stock, you know, based on whether we’re gonna be an AI winner or loser. And that’s not just for tech companies. It’s, of course, even for traditional companies. Right? If you’re a retailer, the ability to gain OpEx margin is driven by your ability or perceived ability to implement AI and improve the leverage of your business. So that being said, I I think some of the other bottlenecks are gonna be around data privacy and whether enterprises are comfortable trusting, you know, these cloud hosted providers.

OpenAI, for instance, has an amazing model offering. But right now, they don’t have a way for you to deploy in a, you know, self hosted way inside your own infrastructure if that’s something that you care about. Right? And of course, there are gonna be open source models that help bridge that gap. You’re gonna be able to adopt your own models that are pretrained. But even then, there’s gonna be challenges around you know, what is the nature of the training data? Is there copyrighted content on which this model is trained?

Does it have the risk of plagiarizing content, you know, especially if you’re producing content that’s gonna be public facing? And, ultimately, you know, how do you get the accuracy and the safety of these models high enough to be useful for the intended application?

I think services companies will definitely play a very important role. For how long and how much of the cake they take versus application companies, the services companies are going to be the hand holders. Right? They’re gonna help these enterprises both with the technical know how of, you know, for instance, how do you implement a vector database? What’s the right embeddings model to use? These are all technical implementation details that actually matter a lot, right, to getting a useful solution. I think enterprises are gonna need some help, you know, figuring this out.

Otherwise, you know, I’ve I’ve seen some, you know, go off and and do this themselves, and it’s possible, but it’s also a lot of heavy lift. Right? It’s a very uphill journey to go in and gain the internal technical expertise to do this.

One thought I have is it grows the entire pie. When you think about, like, Microsoft’s Copilot offering, it’s gonna add significant ARPU to every single seat of Office, which is a massive install base. Right? So it’s actually growing the pie in dollars because it’s creating new economic value in the world. Right? So I don’t think it has to be purely a, you know, winner takes all, Adobe wins and Canva loses type of equation. Right? I think AI could actually enable both Adobe and Canva to grow and to actually create disruptive experiences that both deepen the value prop.

I mean, generative fill, for instance, in Adobe is amazing. Right? It’s amazing for existing Photoshop users in a way that I think I personally would be willing to pay a lot more money for. But, you know, some of the AI capabilities are also gonna be amazing from the standpoint of creating disruptive capabilities that open these products up to new user bases, right, and even new use cases. And I think that’s where, you know, potentially the the startups or the newer companies that have more ability to to lean into the tech and and kind of build products without the need to support existing customer expectations, distribution models, etcetera.

You know, when you think about products like, you know, Gamma or Tome, for instance, disrupting, you know, how you create slides, you know, I think there is a possibility that, you know, while Google Slides and Microsoft PowerPoint are for sure going to implement Gen AI capabilities into their own products, these new upstarts implement them in a way that actually goes after novel use cases. They’re not competing for existing PowerPoint use cases, but they’re actually going after completely new ways of sharing, expressing information that arguably are are not even really about slides, right, so much as, you know, a better way to communicate visually.

I kind of think of it like Prezi, which I never personally liked that much as a product because it always made me dizzy, the the flying around the screen. But what they were doing was not going after traditional PowerPoints, but rather creating a new improved way of communicating content in an engaging way.

Yeah. So the short answer is yes. And it’s a spectrum of how much of an incumbent versus an upstart you are. And within Airtable, there is this cautious choice that we always have to make of, we still have a finite number of resources. Right? We may have raised over 1,000,000,000 in capital, but there’s always resource scarcity. I think it’s ultimately a choice of, if we wanted to cut corners, launch something completely separate and new, we could move really, really fast. I mean, maybe some startups are able to take even more aggressive shortcuts like not having to worry about security as much, right, I think for us.

Though it does slow us down versus you know, a truly cavalier new startup. But I think when we’re building stuff into the existing product into you know, for the existing customer base, I think it necessarily is a little bit slower than building it as a clean slate, cut all corners, new product entirely, but you gain the advantages, of course, of, you know, compounding the value that you already have. So I think it’s always a choice.

Yeah. Going back to the one of the early questions you asked, you know, I think it comes down to product go to market model alignment. Meaning, you can have a product that’s amazing for, let’s say, individual users or small teams. But let’s just say, like, hypothetical product x. Product x is purely for individual usage. There’s no team collaboration built in, at least from day one, and you get all this viral adoption. But, you know, at some point when you try to go into enterprise, you know, it becomes really tough to sell the value of x to a senior buyer within the enterprise if its value is only experienced by individuals.

Right? And then you have to do a productivity sale, which is to say like, you go and you say, well, like every person that uses X gets 10% more productivity, right, or saves two hours a week or whatever it may be, or they like it, and you should pay more money for this, right, or buy it for more people. And maybe that works. Although I think increasingly in this environment where every enterprise is trying to rationalize their tool spend, right, they don’t want to have a million different products.

I mean, in some enterprises I’ve talked to, they literally have thousands of SaaS products, and they’re trying to consolidate down 50 different collaboration tools into three. Right? For good reason. Right? It’s easier than to develop internal know how around that one product. Right? Instead of having to support 20 different ones. And, you you have one vendor relationship to manage, etcetera.

But, you know, for for product x, you know, if you’re coming in and and if the value that you’re selling is purely a function of we have x number of people who use us individually, and there’s no team or org wide value that becomes more than the sum of its parts or that matters specifically to the executive buyer, I think that’s gonna be a really tough position to be in if you’re trying to transition into the enterprise and especially, you know, sold enterprise. Right?

Which, you know, save for it lasting, I think every great enterprise product eventually has to be sold to a strategic buyer, right, in the enterprise, whether it’s the head of IT for ServiceNow selling ITSM or, you know, initially for for Salesforce head of sales. I think you have to to find that senior buyer who really has a business case for for adopting your technology.

So I think bundling, it already is very important for products that fit into that core productivity suite. If you’re a product that generally provides very horizontal, but in my mind, shallow value, right, or commoditized value, meaning, like, there’s a lot of different products that do the same thing. Right? So certainly, if you’re doing video conferencing, Zoom is great. There’s also other products that offer the same thing, whether it’s Teams or Meet. There were many other products like BlueJeans, etcetera, that that predated the current era of products.

For those products or if you’re doing whiteboarding or if you’re doing any kind of, like, free form or a very generalizable document editing, I think those products are screaming to be bundled. Right? Because they’re very broadly applicable. Every company wants some form of it. Maybe not for every employee, like does everybody need video conferencing guests? Does everybody need whiteboarding? Maybe not. Unclear to me at least. And but either way, you think of it as an aggregate decision that you make across the entire company. Right? So the CIO can say, look, I’m gonna go with Teams and not use Slack.

I’m gonna go with Office, and I’m gonna buy it all together as a bundle and not necessarily need every single specific product that otherwise would be unbundled because the benefits of each of those products just are not that differentiated. Like, I need one big bundle. It’s really about finding simplicity and cost effectiveness. And if some of my users would have preferred x y z, let’s just say, like, document editing product instead of Word, too bad because it’s close enough and I don’t hear a strong enough argument for the business impact of using one or the other.

Right? I think that’s different from making an ROI sale, which is by definition about differentiation of saying this is not a commoditized product. So I think as we move upmarket, it allows us to fight the bundling effect by creating differentiated value that can be sold in in a business ROI to a senior buyer.

Candidly, I’ve I’ve tried the former, and it doesn’t work. While there’s a lot of excitement around AI in general, and and, you know, maybe you’ll get conversations that are just more exploratory in nature. Right? But interest and excitement alone don’t close deals. Right? A real business case, real justification of budget, of value closes deals. Right? And what I found is I think it can be a conversation opener in the sense that if we come in and say, Look, AI has the ability to disrupt a lot of key pieces of this specific use case, let’s say that marketing supply chain end to end process, right?

So I think when we come in, we tease a point of view of how AI can specifically help in a certain use case. I think that’s a lot more interesting and actionable than just talking about AI as this very abstract thing. Because then we’re we’re back to square zero of enterprises are still trying to figure out what is this AI thing and what can we use it for. So we are nowhere near the tornado of every enterprise just knows they want AI in heaps and is very ready to go and throw their own resources at deploying AI into every corner of the company.

Right? Maybe that’ll happen. Maybe the train will arrive, but it’s my sense from my conversations that we’re not close to that yet.

In my crash course on enterprise that I’ve gained over the past ten years of, you know, doing the PLG thing and then shifting into an enterprise centric model, I think a million dollar logo is really the threshold of being kind of a real enterprise account. Now, you know, you could argue, like, there’s a earlier stage milestone before that that’s still meaningful. Like, I would think of even like 250, 500 as a meaningful milestone. But the truth is, what I’ve come to appreciate is that anything less than 1,000,000 is really a speck of spend for a really large enterprise.

Right? And frankly, even a million is just a starting point, I mean, you’ve got really large enterprise vendors, ServiceNow or Salesforce that literally are doing multi decamillion or even $100,000,000 deals with with some of their largest enterprises. And that’s where you’re really powering a really critical part of how the enterprise runs. Right? So I think a million is kind of the real threshold at which you’re a truly battle tested or, like, significant value delivery enterprise vendor.

I think it happened fairly early for us. I think probably 2019, maybe a year after we got our unicorn valuation. You know, that was four years after launch, admittedly, and we had gotten a lot of PLG adoption. We were well in the mid to high tens of millions in in revenue, I believe. So certainly, it wasn’t like we got a million dollar contract right away, and especially given the nature of how our product was adopted. Like, we got there because we had this groundswell of organic momentum within these larger enterprises.

And by then, you know, there were many Fortune five hundreds that already were using Airtable to the tune of thousands of active users who are easy to monetize.

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It’s interesting now. When you operate a company at scale, you start to realize how much macro plays into enterprise behavior and therefore your own execution behavior. Right? When interest rates were low and every company was going on a little bit of a binge of spending across the board, but including software where maybe there was a rational argument for it during COVID, every company had to digitize their workforce really quickly, shift into remote work, figure out how to enable their workforce to be effective even when they weren’t in office and they had to disrupt how they worked.

So there was kind of this massive groundswell of adoption and just dollars being thrown at a lot of different products. Right? I think now there is a very understandable rationalization of, wait a second, we had to go during the mad rush over the past few years of adopting tech, spending on tech, and also less budget sensitivity. We adopted all these products, and now we’re feeling a little full. We’ve gorged ourselves, and we have to go in and understand what product is adding what value, which ones are duplicative, and which ones are actually meaningful and need to say.

I think the thinnest or the shallowest analysis that enterprises are doing is the active to paid ratio calculation. So how many of our paid seats, if you have a 100 paid seats, are we using 50 of these actively? And which departments do those occur in? Does this department really need x y z product? So I think that is one of the analyses. But I think the harder work is actually saying, well, even if they’re active or not, are we getting business ROI from this product? You could have a collaboration product or a chat product that has very high activity and yet, maybe arguably, is actually costing you in terms of productivity because everybody’s just kind of going and using the product.

But is it actually enabling real and better work results to get done? Right? There’s this funny quote I heard from one enterprise CIO, which was, look, if I added up all the supposed time savings that every collaboration product claims, my employees would have negative hours of work to do every week. Like, say five hours with this tool, say ten, with say five, it adds up to more than a full work week. So the math can’t possibly be true. And so I think instead, the more that you can ground into business outcome and what are the best measures for us to show that to the customer rather than just tell.

Right? So I do think it requires a different mode of thinking, especially from the early PLG collaboration and active usage only centric world to how you do post sales engagement.

It’s that everything looks easier from the outside. You know, when you’re a startup founder, it can feel very lonely because you feel like, oh, man, everything’s so hard, and there’s so many things we have to figure out. I mean, you look at other companies, and if you’re only reading what they put out on press releases and the big announcements and so on, everything looks like it’s just so easy. And I think when you come to appreciate when you invest in companies, you’re kind of plugged into the community, it’s like everybody’s got challenges, Right?

It’s never this perfectly smooth sailing the whole way at least. Right? Maybe there are easier and harder phases. But I think I’ve just come to both empathize with every other fucker going and and figuring things out, especially from the early phases, but also normalize that that experience for myself.

We were at a very unique inflection point as Airtable and believed and still believe that there is a massive market ahead of us. Right? And so, I think what really matters in venture is what is the upside? What’s the IRR potential for this investment? And if you’re in a market where the biggest you can ever be is a $100,000,000 revenue business, Right? And then you’re gonna plateau. Then, you know, it’s hard to come in and and get excited from an early stage returns profile standpoint if you’re already investing at, you know, basically a valuation where that’s priced in.

You know, maybe you get a two x, three x on it. I think when we raised that that round, we were very much and still are at this early phase, this point of inflection where things were starting to work. We were starting to monetize. We were seeing a very quick ramp on the revenue growth curve. I think we went from, you know, 1 to 10,000,000 in revenue in a little over a year. We were well on track to multiple tens of millions. The nature of the adoption, we had everyone from cattle farmers to law firms to nonprofits to agencies, but also really large enterprises starting to adopt Airtable and paying for us.

So it just felt like a very large TAM that, you know, could easily or could potentially have a 100 x even from from that point.

It’s this idea that if you just find product market fit, all else is is gravy. Right? I think, like, product market fit is just the beginning, and there are so many more hard parts of building a business. But I feel like there’s this mentality or myth that the hardest part is that first phase where you’re chasing product market fit. And once you see the takeoff trajectory, everything’s a downhill battle from there. And in truth, at least in my experience, that’s when the real fun and learnings, but also the challenges really start to emerge, like, to build a real business and to scale it.

It’s really about thinking on on longer time scales. You know, we were always a very patient company. Airtable took two and a half years to build the product before we even launched. But what I wish I had done more of was not only have that patience be willing to think on ten year long time horizons, but actually be more measured about holding ourselves to certain goals and milestones along that way. So I remember LinkedIn supposedly had put out a business plan before they started. I think Reid Hoffman had written this plan and had, like, MAU counts and revenue goals and and just in in full detail and in exactly the right chronological order, they spelled out.

Here’s what we’re gonna be five years in. Here’s what we’re gonna be seven years in. It’s like the MBA’s dream of a business plan where it sets like the next ten years of growth expectation and product execution expectation in. And usually that never works. Right? Like, when you try to predict the path of a company, it’s like everything’s volatile. There’s so many things out of your control. But apparently, in their case, it actually worked surprisingly well. Right? Like, they were able to hold themselves to it.

So some of that is in a good way, a self fulfilling prophecy where if you hold yourself to an expectation of we have to be able to figure or get to this milestone by x date, right? If you’re SpaceX or if you’re Tesla getting off the ground, you can’t just say, we’ll figure it out when we figure it out, right? You’re burning cash. You have limited time to get a product out the door. You have to make that product good enough to get sales. And then you have to follow that, you know, in the case of Tesla, first product out with a second product that’s not a loss leader.

I think that kind of discipline could be applied more to to even software companies. So just holding yourself accountable to this longer term plan of, you know, what is the sequencing and at what point do you need to make that transition, for instance, from PLG to enterprise sold? What does that entail? What are the revenue milestones, etcetera, that you can hold yourself to?

I feel pressure to drive durable growth in this business. I think valuations are always an outcome metric, right? It’s the output metric that is a function of all of your execution efforts. And to some extent, if you focus too much on valuation, you’re trying to chase the tail and not the dog, right? It’s hard to directly impact valuation. Mean, maybe you could do a better job of pitching the company out there and that helps. But I think the hard work that goes into building a great business takes a long time, right?

Airtable, at least, was a company that we spent a lot of time building our initial product before we even raised our first round. We then spent a lot of time getting product market fit before we went and raised that unicorn round. Right? This was not a let’s go and get the valuation first and then justify the business after. We very much focused on trying to build a really great product and a business. And that’s what we’re doing now, right? I think to some extent, everybody has had a reset in terms of valuation expectations, revenue multiples, like what’s normal in this new era.

And certainly, like, if we do well at executing on the durable growth playbook, there will for sure be a breakeven point where we are worth our our previous valuation and more. Right? You know, the great companies are the ones that durably compound. And no matter what the revenue multiple is, revenue multiples can compress from, you know, 20 x to 10 x to five x. But I think the one thing to focus on, which is in your control, whereas those multiples are not always, it’s really focusing on that durable growth.

So if you can consistently grow and efficiently grow, inevitably, you will justify any valuation that you pick. Right? It’s just easier said than done.

You know, I think I care less about being remembered and instead about doing a good job. Meaning, I think ultimately, you know, what I’ve learned is there’s much more to this than just building a a product. Right? You know, it’s it’s fun and easy in some ways to to build a good product. I think to build a great company and one that, you know, is not only a great business, but also is, you know, a place where great things are built are are done, that’s a very different challenge.

And so I am learning to become a company builder. And while it’s it’s less about having a legacy attached to my name around that, I think it’s something that that I hold a lot of both pride and accountability around.