Wes Bush

Product-led coach helping founders scale from 6 to 8-figures in product-led revenue. Author of the bestselling book, Product-Led Growth.

Over the last 9 years, I’ve worked hands-on with 400+ SaaS companies, helping them unlock $1B+ in self-serve revenue through Product-Led Growth (PLG).

Six years ago, PLG was a choice.
Today, it is table stakes.

Recently, Brex published a list of the 50 fastest-growing software companies.
ALL of them have a PLG motion.

If you want to win in the SMB or Mid-Market space, you cannot survive without PLG in 2026.

What separates the winners is not whether they have PLG.
It is how strong their PLG motion is.

The best PLG motions showcase value in minutes and expand rapidly without adding headcount. Weak PLG motions get stuck in linear growth.

That is what I help founders fix in three ways:

➡️ Is PLG is right for you?

I wrote the bestselling book, Product-Led Growth: How to Build a Product That Sells Itself, to help you determine if PLG is the right strategy for your business. 

You can read it for free at ProductLed.com/Book - my treat!

➡️ What’s the best approach to scale up PLG?

After two years of research and working with hundreds of companies, I discovered the 9 essential elements that separate PLG success stories from the rest. 

I break it all down in The Product-Led Playbook.

You can grab a copy at ProductLed.com/Playbook, or watch my free masterclass to see the entire process in action: ProductLed.com/Masterclass

➡️ Ready to scale up?

If you’re ready to scale and need hands-on help, our ProductLed Implementers are here. They aren't consultants - they are experienced PLG operators who want to roll up their sleeves and work alongside your team to help you address your biggest growth bottleneck, whether that's getting more users to sign up, upgrade, or get to value.

Since we only work with a handful of clients at a time and guarantee growth, you must apply to work with us at ProductLed.com

Let’s turn your product into a growth engine and scale your business the product-led way. 🚀

CEO & Founder

We help you build a successful product-led business.

Start with our free ProductLed Masterclass 👉 productled.com

We asked 60,000 people: “who’s the best PLG operator you know?” 

and we researched who are (or were) behind the world’s best PLG companies (think early-days Slack, Notion, Miro, Etc.). 

Here are the results (2026 world rankings): 

#1 - Elena Verna
#2 - Giancarlo 'GC' Lionetti
#3 - Merci Victoria Grace
#4 - Melissa Tan
#5 - Wes Bush
#6 - Lauryn Motamedi
#7 - Laura Schaffer
#8 - Casey Winters
#9 - Jacob Bank
#10 - Kieran Flanagan
#11 - Leah Tharin
#12 - Ben Williams
#13 - Salvador Torres
#14 - Anton Osika
#15 - Carrie McMahon
#16 - Patrick Campbell
#17 - Esben Friis-Jensen
#18 - Ramli John
#19 - Bryan Ng
#20 - Kyle Parrish
#21 - Melissa Ross
#22 - Josh Sookman
#23 - Tommy Fink
#24 - Henrique Cruz
#25 - Casey Hill
#26 - Hila Qu 曲卉
#27 - David Barron
#28 - Mario Araujo
#29 - Kyle Poyar
#30 - Brian Balfour
#31 - Kate Syuma
#32 - Shaun Clowes
#33 - Meghan Gill
#34 - Michael T.
#35 - Shuo Wang
#36 - Eitan Helman
#37 - Raaz Herzberg
#38 - Shane Murphy-Reuter
#39 - Hiten Shah
#40 - Sam Crowell Richard
#41 - Jon Noronha
#42 - Etgar Bonar
#43 - Yaakov Carno
#44 - Aggelos Mouzakitis
#45 - Andrew Capland
#46 - Adam Fishman
#47 - Ravi Mehta
#48 - Jiaona "JZ" Zhang
#49 - Albert Cheng
#50 - Maor Shlomo
#51 - Francois Bondiguel
#52 - Sian Townsend
#53 - Brian de Haaff
#54 - Jules Walter
#55 - Amol Avasare
#56 - Arik Marmorstein
#57 - Dave Boyce
#58 - Sean Ellis
#59 - Rachel Hepworth
#60 - Jessica Gilmartin
#61 - Aaron Cort
#62 - Adam Robinson
#63 - Jason Fried
#64 - Alex Turnbull
#65 - Yasser Elsaid
#66 - Guillaume Moubeche
#67 - Nicolae Rusan
#68 - David Okuniev
#69 - Jan Oberhauser
#70 - Karim Atiyeh
#71 - Mickey Alon
#72 - David Barrett
#73 - Gary Brewer
#74 - James Hawkins
#75 - Nils Schneider
#76 - Daryna Kulya
#77 - Kurt Mackey
#78 - Koen Bok
#79 - Drew Banin
#80 - Patrick Thompson
#81 - Chris Bach
#82 - Melissa Kwan
#83 - John Foreman
#84 - Ryan Singer
#85 - Krithika Shankarraman
#86 - Pedro Magriço
#87 - Nick Chong
#88 - Harley Finkelstein
#89 - Hugh Molotsi
#90 - Ilan Rabinovitch
#91 - Jim Gochee
#92 - Neil Rahilly
#93 - Dylan Fox
#94 - Steffen Tjerrild
#95 - Ben Edelstein
#96 - Mauricio Idarraga
#97 - Carina Fonseca
#98 - Nick Rico
#99 - Fredo Tan
#100 - Rodrigo Fernandez
#101 - Justin Gallagher
#102 - Adam Jarczyn

Couldn’t tag everyone on this post. In the comments! 

Who do you know that’s not on this list, but should be? 

Let me know below 👇

Genspark AI reached $155M ARR in 10 months but I still got stuck getting to  value when I onboarded.

Here’s my full teardown of their PLG motion, and what I would change to get to $1B if I was in charge of self-serve growth 👇

DM me to get the full 10-minute Loom of me onboarding and giving feedback.

Oh, and Wen S. is coming on the pod. Stay tuned.

Reverse-engineering AI companies hitting $100M ARR ≤ 12 months

Genspark AI - PLG Teardown

Right now, founders have two choices.

One, they can pretend like they're living under a rock with their product, even if they have strong product-market fit right now. Or two, they can realize that product-market fit evolves faster than ever before with AI.

Let’s say you’re running a $45M business.

Growth is steady.
Churn is manageable.
Customers are happy.

On paper, everything looks solid.

But AI is compressing time.

If someone enters your category and delivers the same outcome in a fraction of the time, the equation changes fast.

The issue is → is your product still the fastest path to value?

That’s where the hard decision shows up.

Do you keep optimizing the current product?

Or do you build the version that makes your current product feel slow?

I’ll be direct: You should build the version that replaces you.

Design the AI-native experience that gets users to value dramatically faster.

Because if you don’t, someone else will.

And when they do, growth shifts quickly.

You’ll notice churn creeping up.
You’ll run experiments.
You’ll tweak onboarding.
You’ll adjust pricing.

But if the core workflow is slower, optimization won’t save you.

So the choice becomes simple.

Ultimately, these are the two choices you have to make:

1. Adapt or die: You must decide to disrupt yourself.
2. Be disrupted: Continue as you are and face the consequences.

That’s the decision.

I’m going to be blunt.

This was the best interview we’ve ever recorded.

Because Wen S. (COO + co-founder of Genspark AI) doesn’t talk in startup clichés.

He explains, in plain language, how a 50-person team hit $100M ARR in 9 months, then $155M ARR by month 10… with almost no marketing until after $100M.

Here are the ideas I can’t stop thinking about.

1) People want the job done. Period.

Wen said something that reframed the whole category for me:

People look for information because they need to finish something.

That’s why Genspark shifted the goal from “answer questions” to “finish work.”

2) Their “secret sauce” is boring, but effective: output quality.

Genspark put 99% of their energy into one thing: make the output so good you can ship it.

Work you can use right away.

That obsession is what made word-of-mouth do the heavy lifting early.

3) They refused to spend on marketing.

Most startups pour fuel on distribution early.

Genspark avoided ads on purpose.

Only after they crossed $100M ARR did they say: “Okay. This must be real. It’s time.”

4) They made a Super Bowl ad in 10 days (10x’d growth).

Yes, they ran a Super Bowl spot.

But the part that matters is they made it happen in ~10 days.

Script and creative generated using Genspark.
Shot fast.
Shipped fast.

Traffic jumped 10x overnight.

5) “Focus on one thing” is becoming bad advice.

In the old world, building multiple products meant multiplying teams and coordination.

In the AI-native world, you can reuse the same context across outputs.

People don’t want 12 tabs and 8 subscriptions. They want one place where context carries across everything.

6) How 50 people ship like 500.

Wen shared that 90%+ of their code is written by AI.

Instead of a classic org chart (frontend team, backend team, DevOps, design), they built a platform and kept ownership tight.

7) Why AI companies are hitting warp speed growth.

Adoption comes from two directions at once:

Bottom-up: individuals try it and get value immediately.
Top-down: leadership feels urgency and formalizes adoption.

If you aren't growing at warp speed, attend WARP Week from March 2nd to March 7th.

Save your spot: https://lnkd.in/gQfq4hYd

8) The real proof is revenue.

A consultant who used to spend weeks coordinating research and building decks now pays $25/month, gets the grunt work done in days, keeps more margin, and serves 5–6x more clients.

9) Their only advice to founders: obsess over the product.

If you have a great product, people will notice.

Stay paranoid. Keep shipping. Keep raising the quality bar.

That’s the game.

👉 Listen or watch the episode:
https://lnkd.in/geRJEhTk

“60 seconds to value? That’s impossible for my product.”

I hear this every time I talk about WARP.

Here’s what I tell them:

Cursor writes complex code in seconds.

Midjourney creates complex art in seconds.

Lovable builds entire applications in seconds.

These aren’t simple products. The difference is who does the work.

Old way: “You do the work and get X result.”

New way: “Tell me what you want done. Boom, it’s done.”

Even if you can’t hit 60 seconds today, what if you went from 3 days to 5 minutes?

That’s still a massive moat. And the window to capture it is now, while users are still actively trying new tools.

Wait until everyone has settled on their defaults and the cost of winning that same user goes up 10x.

That’s what we’re building in WARP Week.

Starts Monday. Link in comments.

I spoke with a $50M ARR founder yesterday.

He said something I haven’t stopped thinking about:

“We have product-market fit right now. But that’s PMF pre-AI.”

Let that sink in.

Most users aren’t deeply loyal.

They’re accepting the friction because there hasn’t been a dramatically better option.

If someone builds a tool that delivers the same outcome in 60 seconds instead of 30 minutes, switching becomes obvious.

And here’s the part founders don’t like to admit:

That company is being built right now.

Today.

Very likely by someone who understands your category inside out.

I’ve been studying what I call WARP speed companies.

The ones going from zero to $100M ARR in under 12 months.

They share a pattern.

Zero friction to value.

Users reach their first meaningful outcome in 60 seconds or less.

That’s the new bar for product-market fit in the AI era.

The question is no longer “Do people want this?”

It’s “How fast can they experience the outcome?”

Because speed to value is becoming the moat.

So the real question is simple.

If AI is going to compress your workflow, will you be the one compressing it?

Or will someone else?

10 months ago, Wen S. launched Genspark.

Today, it’s at $155M ARR with ~50 people.

For the first time, he shared exactly how they did it:

1. They didn’t build a better chatbot.

They rebuilt “work.”

Instead of answering questions, Genspark focuses on finishing tasks: decks, models, briefs, decisions.

2. They let individuals pull the product in.

Adoption started bottom-up.

Employees used it because it was immediately useful. Then leadership stepped in to formalize it.

3. They refused to spend on marketing until after $100M ARR.

No big ad budgets.

No artificial fuel.

If it grew, it had to be because the product earned it.

4. They made a Super Bowl ad in 10 days.

After product-market fit was obvious, they took a high-leverage swing.

Traffic jumped 10x overnight.

5. They built a 50-person team that ships like 500.

Over 90% of their code is AI-written.

Weekly releases.

70+ models orchestrated under the hood.

As Wen said, the entire team’s obsession is output quality.

Not features!

The formula is clear → Get users to real value fast. Make the output so good they can ship it. Keep raising the bar every week.

Individuals adopt.

Companies follow.

Growth compounds.

That’s what AI-era product-market fit looks like.

🎧 Listen to the full breakdown: https://lnkd.in/geRJEhTk

Founder @ Drilldown | Custom-designed business dashboards for B2B SaaS that deliver insights in one click, eliminating the need for complex spreadsheets in just 60 days.

We help B2B SaaS teams build an owned data system - so metrics align across the organization and growth decisions are based on evidence.

This might resonate:

As B2B SaaS companies scale, it becomes harder to understand on where growth comes from and what to do next.

Most teams already have dashboards, data integrations, and the technical ability to build new ones. 

Yet they still struggle to agree on what metrics mean, explain drivers of revenue, or produce reliable forecasts.

What you need is one set of accurate, explainable numbers that your team can rely on.

Our work focuses on the data system underneath dashboards: the definitions, structure, and logic that determine whether analytics can support decision-making at scale.

If you’re trying to make decisions based on data but the numbers don’t line up, this explains why: https://thomasvarekamp.substack.com/p/you-have-dashboards-why-are-decisions

Thomas Varekamp

I used to love Power BI. 

Now I hate it.

Not because it’s a bad tool. But because of how most teams use it.

Power BI lets you build dashboards.

But to build dashboards, you need to model the data first. And that’s the trap.

Most people do datamodelling inside the BI tool. That’s a huge mistake.

Why?

Because as your business grows, your data grows too.

What starts as a simple report becomes a spiderweb of logic.

- impossible to trace.
- brutal to maintain.
- easy to break.

Here’s what to do instead:

1. build a proper data warehouse.
2. manage your single source of truth there.
3. use Power BI only as a presentation layer.

A good warehouse gives you:

- version control → roll back mistakes.
- traceability → find and fix issues fast.
- quality checks → know the data is right. 

When you separate logic from visuals:

- people trust the numbers.
- teams make faster, better decisions.
- you don’t have to rebuild everything later.

It’s cleaner, smarter, cheaper.

Don’t duct-tape your data stack.

Build it right from the start.

Drilldown | a data science team in your pocket for B2B SaaS.

I saw a horror story on Reddit that highlights the biggest AI risk in 2026 (and beyond)

A company let an AI data agent answer leadership's business questions for three months.

The output was complete fabrication:

- Sales targetted territories based on fake revenue.
- CFO presented decks filled with incorrect metrics.
- Managers made decisions based on hallucinations.

If you plug AI directly into your data without providing sufficient context, it will simply guess the answer. 

It will sound confident, but it will likely be completely wrong.

The missing piece here is the foundational work; data modeling + metadata + governance that explicitly tells the AI:

"This is how we calculate Net Revenue."
"This is the definition of a qualified lead."
"This is how we measure churn."

It might not be the most exciting part of data management, but as we rely on AI to do more of the heavy lifting, it is absolutely essential.

Business intelligence is being commoditized way faster than anyone realizes.

Workflows that used to require a team of 5 are now being handled by 1 marketer using Claude Code.

Here is exactly where BI is heading in the next 12 months:

1. 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗮𝗻𝗱 𝗱𝗿𝗮𝗴-𝗮𝗻𝗱-𝗱𝗿𝗼𝗽 𝘁𝗼𝗼𝗹𝘀 𝗮𝗿𝗲 𝗮 𝗱𝗲𝗮𝗱 𝗲𝗻𝗱.

→ using code and letting AI handle it is much faster, cheaper, and robust. 

Learning Power BI now? You're in for a big surprise.

2. 𝗦𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁𝘀 𝗮𝗿𝗲 𝗼𝗻 𝘁𝗵𝗲𝗶𝗿 𝘄𝗮𝘆 𝗼𝘂𝘁 

→ no one will manually update cells and write formulas in Excel. Just hook your data to AI and ask for the insights.

3. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝟵𝟵% 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 

→ AI will do the heavy lifting. Humans will transition from "analysis" to "validation", only checking outputs.

4. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗴𝗲𝘁𝘀 𝗼𝘂𝘁𝘀𝗼𝘂𝗿𝗰𝗲𝗱 𝘁𝗼 𝗔𝗜 

→ machine learning is code-heavy. AI writes code perfectly. Humans will just set the framework. AI does the rest.

5. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 "𝗲𝗮𝘀𝘆" 𝗷𝗼𝗯.

→ AI can now auto-generate connectors, map schemas across sources, and self-fix broken pipelines in real-time. 

So, where is this going?

I think we will see companies either:

1. hire a single data person & upskill employees.
2. have external agencies handle it.

(or both)

Just as computers replaced most mathematicians, AI is doing the exact same thing to BI.

Only this time 1000% quicker.

If you disagree, you simply aren't paying attention to what's happening right now. 

I'm shocked how fast things are progressing.

Data teams will look completely different in 2 years.

The standard data analysis workflow used to work like this:

1. you have a question.
2. you ask the analyst.
3. you wait a week.
4. you get a report.

And then you figure out if you actually got what you wanted.

Now?
1. you ask the question.
2. you get the answer in seconds.

Think of this:

Marketing can ask “what is our best channel?”
…without opening 20 dashboards.

The CEO can ask “where to focus next quarter?”
…without going through slide decks.

Sales can ask “which deals need attention?”
…without digging through the CRM.

Everyone will be able to use business insights, instantly. 

So what will data teams do? 

Their primary value no longer comes from structuring and analyzing data. 

It will come from:
→ building foundations AI can understand.
→ helping others formulate questions.
→ translating insights into actions.

The titles will have to catch up with the work.

Expandi hit $10M ARR bootstrapped. Here’s the data system we built to help them grow faster.

From the outside, Expandi was an unstoppable force:

→ 0 to $7M ARR in under 3 years.
→ fully bootstrapped.

But as they scaled, internal problems were growing.

- marketing couldn't measure LTV. 
- finance struggled to reconcile $ vs. € ARR. 
- teams burning hours updating spreadsheets.

We partnered with them to install The SaaS Intelligence System.

The execution plan:

𝟭. 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘇𝗲

We identified growth blockers and defined clear goals for every department. 

No vanity metrics.

𝟮. 𝗕𝘂𝗶𝗹𝗱

We implemented complex ARR logic (New, Expansion, Churn) into a centralized warehouse they own.

𝟯. 𝗔𝗱𝗼𝗽𝘁

We put the whole team (Marketing, Customer Success and Finance) on shared metrics. 

Before: no realtime insights.
After: trusted metrics for decision-making.

Swipe to see how we built their data system.

Drilldown | Helping B2B SaaS leaders regain control by turning data into direction.

How we built Expandi's data system

We are entering the era of the 1-person data team.

I spent the past week testing AI data agents. It made me to rethink everything:

- data science.
- data analysis.
- data engineering.
- analytics engineering.

AI can now execute all of it.

Which makes one thing very clear: most data tools with buttons are going to fizzle out.

Why pay for a rigid analytics platform when an AI can write, refactor, and maintain the underlying code directly?

My predictions:

1. 𝗖𝗼𝗱𝗲-𝗵𝗲𝗮𝘃𝘆 𝘀𝘁𝗮𝗰𝗸𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗱𝗼𝗺𝗶𝗻𝗮𝗻𝘁.

AI makes raw code the fastest, cheapest, and most robust way to build data systems. 

Writing and maintaining SQL, Python, and infrastructure is no longer the bottleneck.

2. 𝗗𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗰𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝘆.

Moving data from A → B is a solved problem. 

Just give the API docs to an AI agent and ask to build the pipeline. 99% of the time it will one-shot it.

(try Claude Code if you don't believe me)

3. 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘄𝗶𝗹𝗹 (𝘀𝘁𝗶𝗹𝗹) 𝗯𝗲 𝗸𝗶𝗻𝗴.

AI can write the code, but it doesn't know your complete business context. 

Deciding what to model, why it exists, and how it should be interpreted remains a human responsibility.

The next 12 months are going to be VERY interesting.

4 deals in pipeline. 2 calls. 

A 7-day update:

I spent a chunk of the week experimenting with Claude Code, and holy shit I'm impressed.

A single person can now easily fulfill all the roles of a data team.

Data work will look very different in 12 months. 

𝗢𝗙𝗙𝗘𝗥 𝗨𝗣𝗗𝗔𝗧𝗘:

Due to these new insights, I'm testing out new things we can do for our clients.

Technical obstacles are pretty much a thing of the past.

My idea is to put more focus on advanced data science work.

Meaning: building custom prediction models (churn prediction, ARR forecasting, etc.)

This used to take ages to build & validate, but not anymore. 

𝗪𝗘𝗘𝗞𝗟𝗬 𝗦𝗧𝗔𝗧𝗦:

Deals:

- 4 potential deals in pipeline.

Calls (7 days):

- 1 call with a previous client.
- 1 call with a new lead.

Substack (7 days):

- 24 new views.
- 1 new sub.

(will publish new article soon)

Linkedin (7 days):

- 40,259 new impressions.
- 74 new followers.

Let's see if we can improve this week 👊

Most dashboards & reports fail for one specific reason.

It rarely has to do with the tool or the data.

The problem is much simpler: metric definitions.

A dashboard full of metrics means nothing if no one agrees on what they mean.

I see this pattern constantly:

❌ sales trusts a spreadsheet over the dashboard.
❌ finance / Product report conflicting churn %.
❌ "active user" has 3 different definitions.

Before you build the dashboard, build the Metrics Dictionary.

It can be a simple Google Sheet that lists out your metrics + descriptions.

Here are the columns we include in it:

1. unit → is it €, $, %
2. filters → how to slice the data
3. calculation → how it's calculated
4. department → who cares / uses it
5. metric owner →who is accountable
6. refresh rate → how often it updates
7. description → what the metric means
8. data source → where the data comes from

I attached the template we use in the comments.

Copy it, tweak it, use it. ✌️

The data analyst role we know won’t exist in 3 years.

And that’s a good thing.

For years, analysts were valued for one output: dashboards.

Pretty charts, nice filters and providing a surface-level view of the business.

That is ending.

Agentic AI is changing how analysts work completely. 

They’re not just dashboard builders anymore. They’re end-to-end solution developers.

That means:

1. defining business logic up front.
2. modeling data to drive strategy.
3. delivering insights without 5 handoffs.

And yes, way fewer data people will be needed.

But the ones who remain?

They’ll have more trust, and more impact.

The analyst isn’t just the “report person” anymore.

They’re becoming orchestrators behind the insights.

In 2-3 years, all data teams will become AI teams. 

Here's why:

Data teams today spend countless hours in meetings explaining dashboards and writing SQL. 

Most of this work doesn't add real value.

AI can do this faster, better, and without human bias.

In the next few years, analysts will stop spending time on dashboards and answering questions.

They will be maintaining infrastructure for AI:

- designing data models.
- adding context to data.

Here's how that works:

1. business software generates data.
2. data warehouse ensures high quality data. 
3. AI pulls insights and executes tasks.

Data will be the most valuable resource in every organization. 

This will be the source of truth for AI.

taplio

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The Battle

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