I often tell people that in the depths of startup chaos, I discovered a love for science.

Which was quite surprising for me. I was an English major, liberal-arts type, who loves conceptual discussions and always found measurement of any kind to be a bit distasteful.

But after further entrenching my literary mentality with a graduate degree, I inadvertently stumbled my way into a friend’s startup as the first business hire. It was chaotic. Now I know that this is kind of the point, but at the time, it was overwhelming and deeply unsettling. I’ll never forget the CEO sitting me down a couple of weeks in, to tell me: “Look, JY. You’re clearly a sharp guy. You ask lots of smart questions. But… we do not know the answers. We are figuring this out, too. We need you to figure out the marketing stuff. If you can’t do that, it’s not going to work out.”

He wasn’t bluffing or exaggerating for dramatic effect. He was a recent college graduate and the average age of our team was 23. So I needed to start figuring things out. We tried lots of things. Most of them didn’t work. Some of them did, so we did more of those.

But it wasn’t until 3 years later, that things actually clicked. A particularly smart friend (who was also struggling to launch a company) told me that I should really read Eric Ries’ book, The Lean Startup. I was skeptical because, by this point, I’d discovered the hard truth that not everyone who manages to convince an Inc. or Entrepreneur Magazine editor to let them write a guest column actually knows much about how to build or grow a company. So, another successful founder promising that their approach would fix everything? I’d heard it before.

I was wrong.

Reading that book felt like getting the cheat codes. After years of trying to figure things out on my own, this was the systemic approach I’d always assumed was out there… but after years of not finding it, I’d given up.

That book showed me what a startup is and how it should actually work.

Wait, what’s a startup?

What’s the goal of a startup?

Most people would probably say… growth? Get really big and make a lot of impact/money?

Most people (I will humbly submit) are wrong.

This has gotten confusing because the concept of “startups” has become a cultural force and multi-billion-dollar organizations with thousands of employees still use the term to describe themselves.

But a real, true startup is a specific thing. My go-to definition comes from Ries: “A startup is a human institution designed to create a new product or service under conditions of extreme uncertainty.”

That last part, the “extreme uncertainty” bit, is the key. And this is exactly what I’d been experiencing.

When you’re trying to launch a new product to a new audience, you’re trying to assemble a puzzle, except you don’t really know what the completed picture will look like. And you don’t know what shape the pieces are. Or if you even have the right pieces. And you’re usually trying to do it as fast as possible. While other people are maybe trying to steal your pieces.

Stressful, right?

This is why the whole central point of Lean Startup is that the single primary goal for a startup is quantified learning. Before you can do anything else, you have to crack the code. You have to put the pieces together.

So, no, a startup is not initially about growth. A startup is about learning.

Fortunately… there’s a tried-and-tested system that mankind has developed for trying to learn stuff. It’s called science.

Hurray, science!

No, I was not much of a scientist. I did get 2nd place in the regional science fair in 7th grade, testing the comparative speeds of different shapes and materials of parachutes for GI Joe action figures. But somewhere after that, the complexity of the subject and one particularly bad year with a negligent pre-calc teacher really disrupted my interest in the quantitative realm.

So, turning back to science in the world of startups was an act of desperation. We (still) did NOT know so many things about our business, and the whole methodology described by Ries and others in the (at the time) emerging field of “Growth” (with a capital G) showed that there was a structured way to approach this.

Fittingly enough, this actually brought everything back to the pre-7th-grade era science context (my sweet spot)…

The Scientific Method! I’m sure you know the concept, but do you remember the steps? Here, let me refresh your memory:

Maybe this all feels very obvious to you now, but I’d encourage you to think about how your team does marketing, especially for new products or experimental (hint, hint - it’s right there in the name!) initiatives. Do you… actually write down your hypotheses? Design an experiment? Do any kind of analysis or review, after a program is complete?

Yeah, me, neither. That’s why this was so powerful. By realizing that this timeless and very Lindy-validated approach could help put together our startup puzzle, I could feel everything click into place.

That month, I made a slide deck and convinced the rest of the leadership team to let me present at our next all-hands, to explain our new approach. I wish I could find the slides, but the big headline was:

MARKETING = SCIENCE.

We got way more structured in how we approached validating the right customers, finding the right channels, and allocating budget to drive the most impact.

Although that company didn’t have the glorious acquisition or exit that we’d hoped it would, this approach extended our runway, allowed us to raise more money, and helped a lot of our users to be healthier and happier, along the way. And this is the approach I’ve brought to every single company I’ve worked with since, from other just-getting-started ventures to multi-billion-dollar organizations that, well, yeah, I’m not sure if I’d really even call a startup anymore.

KNOW BEFORE YOU GROW.

So, what does all of this mean for you?

Well, for one, yeah, you should go ahead and read Lean Startup. It’s incredible. Even the highly divisive “Minimum Viable Product” approach makes way more sense, if you actually get the full context (it’s a mechanism designed to specifically accelerate learning).

But beyond that, my main point is that it’s important to know what phase you’re in.

Every company wants to be at the rocket-ship stage where the business is printing money and hockey-sticking all your metrics, up-and-to-the-right. But none of that stuff works if you don’t have the foundation right. The real startup phase is all about figuring out the basics.

As Elena Verna has said (as her First Law of Growth, in her very first newsletter!): You should not be focused on Growth, before you have Product-Market Fit! That’s trying to scale something before it’s stable. Great Growth teams use tons of data to optimize the various elements of a company’s growth engine, but you barely even know what to measure, when you’re first getting started.

So, to use a phrase that I’m trying to make happen: You have to know, before you grow.

(Or, is “You have to learn, before you earn” better? Tell me what you think.)

Either way: Make sure you’ve got a healthy fire going, before you try to pour gas on it. Artificial growth accelerators (like paying for a ton of traffic) can actually do more harm than good in the early stages, because they prevent you from seeing what is actually working and what needs to be fixed.

In the Growth and Scale-up phase, you’re optimizing all kinds of things. The Balfour Fits point out that you need to assemble (at least) four separate components to get to the $100M ARR range: Market, Product, Channel, Model.

But in the true startup phase, Ries says you’re really just testing two core hypotheses:

• The Value Hypothesis: Do people actually want this, and will they pay for it?

• The Growth Hypothesis: Is there a repeatable method for introducing the product to those people?

If you’re in charge of marketing, #2 is your job. But that’s the thing—every company has to figure out both of these, but the second cannot work without the first. So, even if your job title says “Marketing” or “Growth,” you better make sure that the value hypothesis has been confirmed, before you start spreading the word.

This is why Product Marketing is usually the most important component for an early-stage team—it’s the product/market crossover role. In more ways than one. While the Value hypothesis is generally something that the Product team will own and improve, a product marketer can help develop the clearest way of expressing that value and how people will perceive it.

But for both of these core hypotheses, it’s wild to me how many teams don’t have something sketched out, even in theory. Again: No one really knows for sure whether their theory will be right. But if you haven’t established your hypotheses, you have nothing to test against! The whole scientific method wheel doesn’t work, because you’re jumping to the “data collection” phase without having anything to prove or disprove.

You probably won’t be shocked to hear that this abbreviated approach isn’t nearly as effective.

So read the book. In it, you’ll see the simplified, more memorable structure that Reis recommends: Build, Measure, Learn.

But I’d encourage you to put on your lab coat and do the whole dang thing. If you’re trying to discover (or rediscover) the foundational model for your business:

1. Write out your observations and questions.

2. Research what people already know about this. So fast, with AI!

3. Write out your actual hypothesis. What do you think is the main value people will buy? How are you expecting to sustainably let those people know about it?

4. Test it with an experiment. This is the MVP on the value side, but you can do “Minimum Viable Tests” on the marketing side, too: What’s the fastest way to figure out if a growth channel works for you?

5. Analyze the data. Seriously. You have to analyze the data.

6. Report conclusions. Tell other people!

7. Repeat! 🥳

Science is great. Trust me, it comes highly recommended.

Everyone’s a startup, now?

The reason I’m writing this newsletter and trying to get everyone to understand how startup marketing works from a first principles approach is that a lot more companies are going to be needing this approach.

As I mentioned before, the term “startup” has become so broadly applied that it pretty much just means “Tech company.” Which has been problematic in the past, since young companies often try to apply later-stage playbooks from “other startups” (which aren’t actually startups) before they are really ready.

But now… well, is it fair to say that every company is operating “under conditions of extreme uncertainty”? I think so. The double disruption of AI that I’ve described for tech companies is actually an extension of the two core hypotheses that Ries described:

• AI is threatening the core value hypothesis of companies, because AI capabilities may replace software capabilities.

• AI is threatening the core growth hypothesis of companies, because AI is upending the previous media landscape and distribution channels.

So I’ve been encouraging founders and marketing leaders at early teams to learn to love science. But I don’t think it stops there. A lot of the fundamentals need to be reconsidered. It’s time to go back to first principles. You’ve got to know, before you can grow again. And I’ve got just the method for you to try.

Hurray, science!

I’ve spent 15+ years learning startup marketing the hard way (as in, by doing it wrong) and want to help people avoid the mistakes I made. And the biggest error I made as I was starting out was this: Imitation.

Unfortunately, this is an issue I’ve seen in every single team I’ve ever worked with. But I do think it’s preventable.

[Quick note: I’m using the term “startup” as a shorthand, here. This applies to any leader or team trying to introduce something new to the market—whether that’s a product, service, or entire company.]

Three failure modes: Copying Heroes, Rivals, & Past Success

I totally understand why it happens, but that doesn’t make it any less deadly. It’s appealing to both founders with minimal marketing experience and marketing leaders with tons of it. I generally see it in three forms:

→ Failure Mode #1: Copying your favorite companies.

This is particularly dangerous for founders or non-marketing folks who are just getting started.

If you’ve never done marketing before, it’s understandable that you’d look at the best of the best and think, “Yes. That. I’m gonna do that.” The superficial version of this is just copying campaigns from Apple or Nike. But there’s a deeper level, too: Reading books by iconic marketing leaders or CEOs and then trying to just drop their approach into your org.

→ Failure Mode #2: Copying your competitors.

This is appealing to both those unfamiliar with marketing (for similar reasons to Failure Mode #1) and to very experienced marketers, who love Competitive Intelligence and trying to figure out what their rivals are up to.

This sounds like, “It worked for them. They’re going to be taking our market share if we don’t do the same thing!”

→ Failure Mode #3: Copying your previous successes.

This is for the repeat founders or the hired CMO. This one is, “I’ve got a playbook for this.”

It’s the same mistake, 3 ways

All three failure modes actually come back to missing the same inescapable truth: Every company is unique. Not in a “You’re a special special snowflake” kind of way. In a “Success is extremely context dependent” kind of way.

Each failure mode gets more nuanced, but still falls apart for the same reason:

• Your favorite company: Probably a completely different industry, product, size, budget, objectives, everything. A trillion-dollar hardware company is an entirely different beast from a new software venture. Plus: What you can actually observe from the outside of a company may be entirely different from the internal dynamics that make it possible.

• Your competitors: Okay, these likely have a lot of those variables in common, but for the incumbents you’re trying to displace, their size and budget and objectives are still very different. And for peer competitors, their differentiation and unique advantages (your team, your audience, your target market) should be different… right? (If you’re not visibly distinct from your peers, you’ve got a bigger problem.) And if you’re already established, trying to imitate the tactics of up-and-comers is forfeiting the advantages you have against them. And just like the first failure mode: copying the visible tactics of a competitor is like trying to duplicate the tip of an iceberg without having any idea of what’s beneath it.

• Your previous playbooks: This is the most appealing because, in theory, it could translate 1-to-1! If you’ve done it before, you can do it again! In fact, this is the entire premise of many hiring plans: “We want someone who’s done it before.” But… this misses the giant confounding variable that makes this almost impossible for new products and companies: Time changes things. Even if you started the exact same company for the exact same market with the exact same team… Is launching a product in 2026 the same as launching in 2021? Much less 2016 or 2011?? Absolutely not.

Why does this keep happening?

Part of this is just instinctual: We learn from imitation and when you don’t know how to do something, you just follow along with someone else doing it.

And there’s also a degree of FOMO, or, to be fancy about it: Girardian Mimetic Desire. We want what others have. We want to be like those we admire. There’s status to be gained by doing what’s cool.

But there’s another underlying assumption at the root of this: That startups and new ventures work like big companies. And unfortunately… they do not.

Large organizations are literally built around a set of confirmed variables and context. Traditional strategy, forecasting, planning, and operations all work based on the understanding that previous performance can be used to model and then optimize future performance. Big companies have so much experience and data that they can build a repeatable machine to deliver consistent outcomes.

Bringing in a seasoned leader to apply their playbook can actually be a very smart move in this context, because the constraints are locked and predictable. If someone has run a hugely successful performance ads playbook for a chain of restaurants in one region… there’s a good chance that they’ll be successful in running that same performance ads playbook for a different chain of restaurants in the same region, or maybe the same chain of restaurants in a different region.

But this is, uh, not how a startup works. In a startup, you don’t know any of the variables for sure. Everything has to be uncovered and discovered. So, copying established companies or rivals or previous companies is just plugging in the same figures for what is almost definitely a very different equation. Even “Successful CMO at ABC Startup in 2016” does NOT translate to “Successful CMO at exact same startup in 2026.”

And it’s getting even worse. Not to get all “Now, more than ever…” on you (hate that phrase), but this Marketing-Strategy-via-Imitation really is even more dangerous/less viable, now.

If we actually consider the specifics of 2026 vs. 2016, the most obvious difference (even more than the end of ZIRP-era growth-at-all-costs) is… AI. And the wild double-disruption of this new technology means that replicating previous successes is even less likely. You cannot assume that any marketing strategy that applied in the last 20 years will transfer 1:1 to your strategy today.

What does this look like?

Let’s be practical, here. If you’re not going to just imitate what other organizations are doing for marketing… what should you do? That’s a bigger topic (and one I’m hoping to write more about in this newsletter, so, y’know, like/subscribe/share/etc, pls), but here’s a quick crash-course on some key examples of how this looks, on the ground:

• Choose the right customers for your company. My favorite behavior scientist, BJ Fogg, has a central tenet: “Help people do what they already want to do.” If the behavior you want is “People use my product/service,” then go find the people who already want what you offer. Sounds obvious, but it’s very rarely applied. Convincing people that they should want it is a core activity for bigger companies (and misguided smaller companies), but startups don’t have time for that. So don’t chase a market—go after the absolute best-fit people, who are exactly right for what you do.

• Build a growth engine around your constraints. Go read the Balfour Fits to understand the inherent constraints on your strategy. The good and bad news is that you don’t have to/get to pick your distribution channels or revenue model at random. A scalable system all clicks together. And the reason why “engine” is the #1 all-time favorite Growth metaphor is because a really good marketing system operates like that: It’s slow and difficult to assemble at first, but then it drives things forward for a long time. Even in turbulent times, the goal is a repeatable system that can eventually be scaled. But each engine is different, so make sure you understand what you’re actually trying to assemble.

• Choose the specific channel where you have an unfair advantage. “We need to be everywhere” is a death sentence for startups. Growth operates on a power law, so your goal is to find the one (maybe two) that works for your company and get really good at it. Go read Growth Levers by Matt Lerner (it’s super short!) and/or Traction by Gabe Weinberg & Justin Mares to learn how to do this.

The humility to learn for yourself

I’ll add a distinction, here: I’m not saying that you should ignore what other people are doing. If there’s a way for you to learn from the successes or failures of others, hopefully that will allow you to dodge those same mistakes.

After all: That’s kind of the goal of me writing this, right?

My point is that you shouldn’t just blindly copy what others are doing or have done in the past.

Probably the biggest insight from The Lean Startup is that the primary objective of this kind of new venture is quantified learning. Again: You’re trying to solve an equation with all the variables unknown. You cannot steal the answers from someone else. You cannot copy their test results.

But you can look for patterns!

I know the idea of “mental models” has become so overdone that people are tired of it, but the core insight from Charlie Munger is that 1:1 copying isn’t very useful, but you can start to notice similar things popping up. Look for things that recur across a lot of companies, a lot of leaders, a lot of examples… and you’ll start to see the trend line emerge.

And the longer the timeline you look at, the higher degree of signal your pattern recognition will have.

As I’ve been reflecting on this whole Post-Information Age thing, this is probably my biggest realization: Even though it feels like everything is changing, a lot more is staying the same. So yes, applying the playbooks from 5, 10, 20 years ago is risky. But there’s a lot to be learned by paying attention to the long history of how humans have made decisions, navigated uncertainty, and conducted marketplace interactions with each other.

That SEO playbook from a few years ago, before all the algorithms changed and ChatGPT created an existential threat to the discipline? Not so much.

Copy-pasting display advertising techniques that don’t work on mobile and ignore recent generations’ documented ad-blindness? No, thanks.

But… recognizing that people in every civilization in recorded history have preferred to transact with people they know and trust? Yep.

Or… knowing that increased efficiency has historically meant increased consumption of a resource, not less (Jevons paradox!)? Yeah, that could be in play.

Short-term precedent is worthless. Long-term precedent is priceless.

So, yes, I get it. I’m super sympathetic to any leader who gets inspired (or intimidated) by a cool campaign or stunt they saw someone else do. But I’ve seen this movie enough times to know: The only playbook that actually works in startups is knowing how to build your own from the ground up, every time.

That’s why, even though the “I already did this, we’ll just use my tactical playbook” marketing leaders (and the orgs that hire them) are doomed to fail, there are individuals who have a track record of incredible startup success. And the pattern I see from them is: A deep humility and desire to learn, and an excitement to build a custom engine for every organization.

They run their own race! I hope you will, too.

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What’s the Post-Information Age?

2 years ago, I described the problem:

“For most of history, information has been a scarce, precious resource. But as new “information technologies” have emerged, we progressed from information scarcity to information abundance. Now, as we continue our way through the 21st century, we’ve gone beyond abundance—we’ve now reached the point of information over-abundance. We now have information saturation.

In a world with too much information, the key skill will become the ability to filter it. To distill, to discern, to curate. To find signal within the never-ending waves of noise. Widespread access to Artificial Intelligence tools was the tipping point that finalized this shift and these tools can also be used to navigate it—but I believe this transition will push us toward values that the Information Age originally took away: communicating with a degree of context and connection that requires us to accept some constraints.”

(from ‘Welcome to the Post-Information Age’)

Since then, it’s only become more obvious how true this is.

These changes affect everyone, but for marketing leaders and growth-focused founders, this changes the foundation of what we do.

The Double Disruption

One reason that all of this feels so transformative is that the introduction of AI actually creates changes on two fronts: Disrupting the value our companies offer and disrupting the communication channels we use to introduce them to people.

• Invalidated Value Props: AI is an existential threat to any software product. Software ate the world, but now AI is eating software. Every product that offered improved efficiency vs. a more manual approach is now the less efficient alternative. And traditional services businesses that traded on expertise and light-weight specialization have to face the fact that LLMs like ChatGPT can offer comparable services, often for free. The fundamental offer that we’re presenting to potential customers needs to be completely rethought.

• Channel Collapse: 20 years ago, social media fragmented mainstream distribution—information that used to be broadcast via a handful of big network channels on TV and radio is now split across social media accounts give every person their own platform. Since then, advertisers have relied on major digital platforms for aggregated distribution, but now, every pillar of the digital marketing ecosystem is crumbling. SEO & SEM depend on people finding information via search engines. Traditional content marketing is collapsing under the weight of AI-generated slop. The cost of paid ads is exploding as the social networks enter the third phase of the platform evolution cycle and start charging for access to ‘your’ audience. Plus, all ad targeting and attribution are becoming less possible, in the face of increased privacy restrictions like GDPR and Apple’s walled-garden ecosystem.

The fundamentals that every marketing and growth leader has built their career on are shifting under our feet.

What’s changing? New ways to grow

What will be the key channels in this new era, and how should you approach them? This is what I’m seeing and will be digging into:

Human intelligence & Trust

In the midst of information saturation, trust is a premium currency. How can you build personalized connection with your intended audience? Through personality-driven distribution. This looks like:

• Founder-led content - It starts at the top. What’s the unique perspective and motivation that’s driving the company?

• Employee-generated content - The more the merrier. Each member of your team can add perspective and context.

• Creator partnerships - As you’re building your audience and trust, borrow the audience and trust of others. Work with individuals who already have the attention of your ideal customers.

Human-level connection points will maintain attention and credibility, even as the massive distribution channels crumble.

The options for communicating through these channels are almost endless, but I see three key types of stories that companies should be telling, through them:

• Grand Narrative (The Big Story) - If you’ve heard, ‘Your company should be a movement,’ this is it. This should be anchored in a massive change in society that your company is leading. Often built around owning a problem that you want to solve. Note: This can’t be faked. If the founder of the company has this kind of vision, clarify it and then lean into it. If not, there are other ways.

• Build In Public (The Company Story) - Instead of waiting to reveal the story after you’ve reached success, share the messy process along the way. Bonus points for sharing when things go wrong.

• Experience & Expertise (The Personal Story) - Everyone is navigating these changes, everyone is trying to figure it out. What are the practical things you’re learning along the way?

Each one of these can be mixed and matched across the channels, depending on your advantages and priorities. For instance: Founder content will likely focus on Grand Narrative & BIP; Employees will focus on BIP & Expertise; Creator partners will focus on Grand Narrative & Expertise.

Artificial intelligence & Leverage

Alongside the need for increased human connection, new AI tools create undeniable individual leverage. What changes, when one person can do the work of an entire team?

• “Satellite Apps” & Building Distribution - AI-powered software development (“vibecoding”) means that marketers can build micro-products (which I call “Satellite Apps”) that can capture attention and demonstrate value for potential customers, much earlier in the marketing funnel. How should these be built? Where do they fit in the existing landscape?

• Teams of Agents - How does the managerial skillset translate to overseeing AI workers alongside human workers?

• The Next Big Thing (TBD) - As Brian Balfour has pointed out, the emergence of new platforms always creates massive opportunities—but the new distribution framework on top of ChatGPT and other LLMs still hasn’t been decided. How can you monitor this situation and dive in at the right time?

If you understand how to create trust through human intelligence and create leverage through artificial intelligence, you’ll be ahead of the curve.

What doesn’t change - A precedent for “unprecedented times”

Beware anyone who claims that “We’ve never experienced anything like this.” They are likely not a student of history.

Yes, the size and scope of the AI ‘double disruption’ is massive. But humanity has developed an impressive toolkit for navigating change. So, as much as this newsletter will address what is changing, I’ll also be digging into what doesn’t change:

• Startup mentality and rapid-learning: “A startup is a human institution designed to create a new product or service under conditions of extreme uncertainty,” Eric Reiss said in his famous book, The Lean Startup. How can your organization (whether it’s early-stage or not) apply proven tactics of iterative development to solve the ‘double disruption’ riddle?

• Behavior Design and psychology: Our technology is evolving much faster than our brains. Despite the long-held belief that humans are fundamentally rational, behavioral economics points out a degree of “predictable irrationality” that actually drives what we do. How can your company use this model to make it easier for your customers to complete the actions that you both want?

Things are moving quickly, so we need to figure out: What can be borrowed and applied from previous generations? And what needs to be built from scratch?

Are you ready? It’s time to choose.

The word “intelligence” comes from Latin: inter + legere, which means: To choose between.

I’m writing this for senior leaders and decision-makers, because you have some choosing to do.

Are you going to lean in, to figure out how to continue to grow your organization in this new era?

And if you are, are you willing to sift through all the noise to figure out what applies for you, and what doesn’t?

Because I’ll tell you what you already know: I don’t have all the answers for you. No one does. The context of every company is unique, and you need to craft your own path.

But I am here to help. I’ll share the patterns I’m seeing across a wide variety of companies: what’s changing and what’s not changing. I will help you ask the right questions.

So if you’re ready to navigate this change, you’re in the right place.

Subscribe to get updates and leave a comment or email me hi [at] jwby [dot] co to let me know what you think.

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Welcome to the Age of Intelligence. Let’s explore it, together.