Cameras, code and creation
David Peterson

I was catching up with a good friend from my Airtable days over the weekend, and we were discussing the impact of AI on “no code” tools. The question: will AI just obviate the need for “no code” tools entirely? This friend of mine now runs an Airtable consulting shop, where he architects, implements and maintains systems for an incredibly impressive array of Fortune 500 customers. Suffice it to say, he understands the processes that make these businesses work better than most of the employees. But given all the AI hype these days, I was surprised to hear just how bearish he was on the impact of AI in the enterprise, at least in the short term.

Part of the reason for his skepticism is simply that, despite spending hours and hours brainstorming potential AI-powered workflows with his clients, they’ve yet to come up with any truly game-changing ideas. The distance between sexy AI demo and AI-powered production workflow is still quite far.

Part of it is likely a bit of motivated reasoning on his part, as well. He runs an Airtable consulting shop, of course! Who wants to predict their own demise…

But another big part of it is the fact that he and I know from our own experience at Airtable that the limiting function for no code has never been the technology itself, but something much more fundamental: human desire.

Here’s what I mean. The vision for Airtable was always to democratize software creation by providing people with a toolkit they can use to build the systems they need. And I think Airtable strikes a decent balance between power and simplicity. But a hard lesson we learned along the way is that most people don't want to build their own tools. If there is any single explanation for the challenges Airtable has faced growing to $100s of millions in revenue, it is this. There just aren’t as many “builders” out there as we thought. Sure, we could have made Airtable a bit easier to digest, but the bottleneck to growth was never the complexity of building with Airtable. It was the inherent desire to build in the first place.

Now, AI is promising to lower the barrier even more and truly democratize software development. Generate code with a prompt! Build entire apps in minutes! The possibilities seem endless. But I can't help but wonder if we're falling into the same trap. Even if the cost of creating software approaches zero, will that suddenly ignite a widespread desire to build? Will everyone suddenly become a software developer? If my experience from Airtable is any guide, then I suspect not.

Another example is education. A decade ago, MOOCs (massive open online courses) were the hot new thing, promising to revolutionize education. Elite university courses at your fingertips! Learn anything, anytime, anywhere! But the revolution never quite materialized. Completion rates were dismal. MOOCs undeniably expanded access to education. But turns out, most people struggle to stick with online coursework, no matter how prestigious the institution or how compelling the content. The bottleneck wasn't access to information, but intrinsic motivation.

Now, with the rise of AI, MOOCs are experiencing a resurgence. This time, the promise is even grander. Forget static lectures – you have a personalized AI tutor, a genius at your fingertips, ready to answer any question and tailor the learning experience to your specific needs. But what if the fundamental problem remains? What if, even with the most sophisticated AI tutor, most people still lack the drive to consistently engage with challenging material over an extended period? How can we ignite that spark?

That being said, perhaps I’m just looking at this the wrong way. I’ve cherry-picked two examples - MOOCs and no-code. Let’s consider a third example - the camera. For decades, cameras were getting better and cheaper, but never quite ubiquitous. And, as a result, they largely remained the domain of power users. Smartphones changed all that. Suddenly we all had state-of-the-art-cameras in our pockets. Did this create millions of Hollywood filmmakers? No. But it did lead to a surge in visual creativity. Instagram, Snapchat, TikTok…these platforms all emerged by leveraging the ease of photo and video capture to enable novel forms of creative expression. If back in 2008 you argued that the ubiquity of smartphone cameras wouldn’t lead to millions of new Hollywood filmmakers because most people don’t want to put in the work…you would have been right. But you would have missed the shift that really mattered.

Perhaps this is the right framework to think about the future of education and software development (as well as all the other functions impacted by AI). Will AI lead to millions of new students sitting in virtual classrooms scribbling notes as professors opine on a chalkboard (as was imagined with MOOCs)? Probably not. Will AI lead to millions of new software developers as they exist today? No, I don’t think so. (That doesn’t mean the coding copilot companies are uninteresting, by the way. There will still be millions of software developers out there.) But it seems undeniable that there will be a platform that leverages the ease of code generation as a first-order primitive (just like Instagram did with the smartphone camera) to make software creation truly ubiquitous. What will that look like? Will it look like Airtable or something else? I don’t know. But if that’s what you’re building…I’m excited to hear about it.

David

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AI vs. Quantum. The rapid advancement of GenAI in the last few years has enabled it to encroach on complex areas, previously thought of as the “home turf” of quantum computers – physics, chemistry, and materials science. This rapid progress has raised questions about the future role of quantum computing. “Scott Aaronson, who directs the Quantum Information Center at the University of Texas, says machine-learning approaches are directly competing against quantum computers in areas like quantum chemistry and condensed-matter physics. He predicts that a combination of machine learning and quantum simulations will outperform purely classical approaches in many cases, but that won’t become clear until larger, more reliable quantum computers are available. “From the very beginning, I’ve treated quantum computing as first and foremost a scientific quest, with any industrial applications as icing on the cake,” he says.”

HOW TO STARTUP

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What not to share. Jack Altman, Founder of Lattice, shared some great advice for founders on what not to talk about with investors. Jack recommends that when founders are not fundraising, they should avoid sharing specific numbers with VCs and set expectations upfront in their conversations with them. “I think it’s fine as a founder to meet investors when you aren’t fundraising, but one piece of advice I got that helped me a lot when I was running Lattice: if you’re truly not fundraising, don't talk about your numbers. I would open up the meeting saying something like “hey really excited to be chatting with you. I’m genuinely not fundraising right now, so would appreciate if we could steer clear of specifics around our metrics or our customers, but excited to talk about the market and company at a high level and to get a chance to learn from what you’ve seen.” Doing this prevents that awkward recoil moment when the VC asks you for info you don't want to share and then you have to act cagey, and I just found it led to a more genuine discussion.”

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Forwrd.ai will be hosting a webinar on ‘Building Predictive AI Models to Improve Efficiency’ on December 4th. Register here to join.

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