Survive the AI Knife-Fight: Building Products That Win

[on-hold music] All right.

I need everybody to take a deep breath here because, um, I'm about to stress you out. And, uh, but hopefully at the end, I'll, uh, relieve that stress a little bit with some ideas and solutions for you.

So I need everybody to just think for a second. Reflect on the past forty-five days, and think about all the possible things that have gone on in our industry and all the product launches.

Let me highlight just a few for you. Uh, Notion launched a Granola, Glean, and ChatGPT competitors. Figma launched a Canva, Framer, Illustrator, and Lovable competitor. Atlassian launched a Granola, Glean competitor, plus Claude integrations.

Anthropic launches a Glean competitor with Claude integrations. Google launches Codex, Lovable, and many other competitors. OpenAI bought [audio cuts out], a Cursor competitor, launches Codex, and a lot more. Right? This is just one little microcosm of the entire tech industry.

But if you look around at all the different categories of software right now, the same exact thing is happening. And I haven't even mentioned the horde of startups, well-funded startups, uh, that are getting funded in every single one of these spaces as well.

And among all of this chaos, we have companies that are essentially collapsing in months rather than years. Chegg was one of the first ones to go that declined over ninety percent in the matter of months.

And of course, Stack Overflow was one of the early victims as well when ChatGPT launched. So this gets to the number one question that we all need to be answering, right?

A lot of people at this conference are talking about how product is doing more engineering, engineering is doing more product work, design's doing more product work, all the tactical, all the technical, all of those different infrastructure.

But none of that matters. None of it matters unless you answer this question: What do I build and why will it win? And the interesting thing about this is this was always the job of product.

It just happens to be that over the years, it got marred in all of this, uh, project management, agile process, all of this type of stuff. But this is what always separated great product managers from good product managers and product leaders.

This is Sean Clouse. He's the chief product officer at Confluent. He was formerly chief product officer at MuleSoft. He was the first head of growth at Atlassian as well.

And I thought he encapsulated it well. He said, "You're constantly trying to get ahead. You're trying to find the angle, the question that has not yet been asked that gives you an insight that is not being actioned by other people."

It doesn't just have to be an insight. It has to be an insight that others are not actioning. Because if you find that insight and others aren't actioning it, that is your competitive advantage.

Now, the problem is, is that this question has gotten ten X harder. This is a rough map of, uh, Gettysburg, and I thought it was a good analogy because this was one of the bloodiest battles in the Civil War.

And this kind of represents the map that we are all playing in in the competitive environment right now. We have fast, huge moving incumbents like Microsoft, Google, and Meta.

There are these new huge horizontal platforms like ChatGPT and Anthropic that are eating up major use cases. We have foundational shifts in the technology landscape, not on a yearly basis, on a monthly basis.

And there are hordes and hordes of startups being funded, including five or six in every single capa- category that has traction by YC, every single cohort. This is you sitting in the middle of all of this, right?

And the question is, is how in the world do you find a seam among all of these players to potentially find some traction and win? That's the question we have to answer before any of the other stuff like technology, infrastructure, or even what our roles are in the organization.

I'm Brian. I'm founder and re-- founder and CEO of Reforge. And, uh, if you notice, I have a little bit more [REDACTED:physical_attribute] and [REDACTED:physical_attribute] from this picture because I've been around in tech for about twenty-five years, been doing startups the whole time.

I played in some pretty competitive environments. I helped HubSpot [lip smacks] launch their CRM almost a decade ago. And, uh, at that time, that was a crazy competitive category. People thought we were bonkers.

My guess is if I took a raise of hand, probably over fifty percent of your companies are using that CRM today. Now, that was a competitive environment. But what we're...

I'm experiencing now and what we're all experiencing is probably ten X that. And so, uh, a little history about Reforge is that we've been around for about ten years.

We've helped thousands of product teams, including all the ones you see here, over a hundred thousand professionals. I hope some of you have been part of Reforge in the past.

And the way that we've done it is that we've built a community of over four hundred experts on the front lines to decode all of their best practices. We started by doing that with forty-plus expert-led courses, including our AI courses.

But a couple years ago, we started to take a shift and started to encode all of this knowledge into AI agents. Our first one, Reforge Insights, which acts like your AI product researcher.

Our second one, called Compass, is your AI project manager that takes care of all of those low-level, low-value tasks that involve product management automated for you. We have two more coming later this year.

But back to this question. How do you win in the intense environment in the history of technology? I spent a few months with Ravi Mehta thinking about this exact question.

He created our AI strategy course. He was the former chief product o- product officer at Tinder. He also was a product leader at Facebook, Microsoft, TripAdvisor, and a bunch more.

And the way that we start to answer this question is actually we need to think about the traps. And the two most common traps are, of course, one, how do you like reinventing the AI wheel?

You do not need to build custom models and infrastructure in order to answer this question. And on the opposite side is the other trap, there's the other trap, which is just implementing, copying and pasting basic AI features like chatbots into your product.

The answer actually lies in the middle.

Which is treating AI like a series of Lego blocks, where you assemble differentiated AI features and products by integrating the best available AI capabilities with your product's data and functionality.

Your competitive advantage will come from what is uniquely yours. These three things: your data, your functionality, and your understanding of unmet customer needs, not the AI itself. So let's think about the anatomy of a winning AI product.

What are the major building blocks? What are the major Lego pieces, and how do you stack them together, connect them to create something differentiated? Well, we can start to talk about this, the AI capabilities, because there's a ton of Lego pieces that are emerging every year, whether it's the pre-trained AI models or the abilities to perform tasks,

audio processing, imaging process, all of these new capabilities that feel magical now that we couldn't do before. But the thing about all of these Lego blocks is you just don't have access to them.

Everybody else has access to them as well. So even though AI products and features, of course, use one of these Legos as its core Lego blocks, this is not where differentiation and competitive advantage comes from.

That starts with one of these pieces, your data. Because your data is what provides context to AI, AI model to generate a unique output. The more unique your data is, the more unique output you can generate for customer.

And there's a bunch of different types of data. There's real-time data that the models might not have incorporated into their training set. There's user-specific data. There's domain-specific data like we've seen emerging in la-- in, uh, legal, in healthcare.

There's human judgment da-data around curation as well as reinforcement data. Now, the question about data is: How do you actually combine multiple categories of data together to form some uniqueness?

As well as it's not about the quantity of your data, it's about the marginal value of your data over everybody else, especially the big models. So how much additional value does your data add over what is already trained in the models?

The third piece is your functionality, because this determines how the AI behaves, and it gives your AI product superpowers. There's multiple types of Lego blocks around your functionality: specialized workflows, unique algorithms, business rules, integrations, whatever it is that's baked into your product.

Now, the key about assembling all these pieces is that they work like a system, and you have to connect the system in order to build that competitive differentiation. Let's start with this.

Your data is what provides and informs the AI's understanding. It's what helps you gener-- helps the AI generate a unique output. And that unique output, as a result, is what helps you build an additional repository of unique data so that that continues to f- to flow in a flywheel.

On the other side of the spectrum is your functionality. Your functionality in your product is how your product controls the AI actions, how it interacts with AI when it calls it to create a delightful user experience.

And in addition, AI's addi-- AI's, uh, increasingly able to call tools in the functionality of your product itself, and those two things work together as a system as well.

So let's take all of this theory, and let's put it into practice. Let's talk about a product, Granola. Just by a raise of hands, how many people have either tried or used Granola today?

Okay, pretty decent amount. That's probably like forty percent of the room. A year ago, that would've been zero. And I think this is an interesting case because they entered a space that already had a horde of other AI note-takers, whether that was Fathom, Otter, Fireflies, there was a ton of them.

Um, but somehow, they found a seam, and they've ga- garnered forty percent of your attention in this room and about fifty million in funding. So let's go back to those three fundamental questions in those Lego bricks.

What was uniquely theirs? Their data, their functionality, and their understanding of their unmet customer need. I'm gonna start with the last one. So at the time when they entered the market space, this is, uh, this is just a sample of people who are already in market, including all of the incumbents like Zoom and Meet that have AI-native

note-taking, uh, capabilities. But they were all approaching it from the perspective of the product is going to do something for the user. It's gonna replace the full job. They want somebody else to take my meeting notes.

What they realized is actually there's a whole other set of customer needs that have been unmet, which is, "I don't want you to take all of my notes. I just want you to help me take better notes.

Empower me around this specific task and user." And that's what they built the product around. Now, in order to start, they used off-the-shelf capabilities. No unique models, no custom training, nothing.

They used Deepgram for transcription. They used Anthropic and OpenAI for some of their other functionality. But the uniqueness came in how they assembled the Lego blocks, starting with, on the left-hand side, with Granola's data, right?

Their context includes both the notes that you take as well as the transcription that they generate. They use the AI Lego block to generate a unique output, which is they enhance better notes.

Those notes, over time, form a repository that, uh, starts to enable all sorts of other features that they've layered on, like chatting across meetings, uh, their project workspaces, all their downflow actions.

So they have this nice flywheel of unique context in data that's starting to spin. That was partially enabled by the right-hand side of the Lego blocks, their functionality. They used a Mac app so that they could detect when meetings started to access the system sound for transcription by being right there at the user ha-- uh, the, the

user moment that they needed it to enable the AI to do those things. And they've also plugged into other tools and integrations like the calendar to get metadata about the meetings, such as attendees.

So they assembled these Lego blocks to meet, in that unique way, to meet that unique customer need. Now the question is, is Granola gonna survive? I've got no idea, right?

It's incredibly competitive landscape because the real- the realization is, is that you can't stop here. You can't stop by just assembling your initial set of Lego bricks. You have to sequence over and over again.

You have to take those first three Lego, Lego bricks, leverage them into another unique set that you assemble, and you see Granola doing this. Now that they've enabled this, they've started to create project and team workspaces and start to, uh, enable a new set of unique use cases off of the initial layer that, um, they did.

They've started to integrate downstream actions like, uh, connecting to your CRM and HubSpot. Uh, I just saw them the other day experimenting with a company wiki that auto-updates itself.

So they continue to sequence these things into a unique set of building blocks. The question is, is will they keep up? I don't know. Jamin Ball, a partner at Altimeter Capital, recently wrote a newsletter, and he said, "The real moat is just a sequence of smaller moats stacked together.

Each one buys time. What you do, uh, with that time, how fast you execute, how quickly you evolve, determines whether you stay ahead. If the moat used to be six to 12 months, today it's two to three weeks."

So to recap, to win in AI, besides being stressed out, [chuckles] right, is to answer what are your unmet customer problems. That's always been a part of product, right? The second is what AI capabilities can solve those problems in novel ways?

What proprietary data can power those solutions? And then what superpowers can our product give to AI? How do you assemble those three foundational Lego blocks? All right. Thank you.

If you're interest- if you're an AI engineer, we are hiring. Our team will be outside. We can play with products with instant distribution to 300,000 people. And if you need help with anything else, just check out reforge.com.

Good luck. [upbeat music]