Brian Balfour

Reforge

Reforge is a platform for AI-native product teams.  We've unlocked the performance of 100K+ top professionals from Meta, HubSpot, Canva, Atlassian, Microsoft, Dropbox, New York Times, and more.

↳ Started Reforge in 2016 to encode knowledge from the world's top operators.
↳ Grew the company to $30M+ ARR
↳ Co-creator of Product Growth, Retention + Engagement, Growth Strategy, Product Strategy, AI Foundations and AI Strategy courses. 
↳ Leading company evolution into a platform for AI-native product teams starting with Reforge Insights

Founder/CEO

Long Journey Ventures

Venture Partner

Multiple Startups

I am currently an advisor and/or investor to Pipefy, Loom, Wonder School, Gametime, HelpScout, Shareaholic and more.  Former companies include Grabcad (Acquired by Stratasys), Namo Media (Acquired by Twitter).

Growth Advisor/Investor

Harvard Business School

Guest lecturer on Growth for Mark Roberge and Frank Cespedes entrepreneurship class at Harvard Business School.

Guest Lecturer

HubSpot

Working with an amazing team, we helped take HubSpot from a single product to a multi-product company and establish a product-led growth motion. 

↳  Established the product growth function. We blend consumer marketing tactics, engineering, product, and data to drive rapid growth of engaged users.  
↳  Led growth of new products such as the HubSpot CRM and HubSpot Sales Hub growing to hundreds of thousands of users.

VP Growth, Sales Product Division

Trinity Ventures

Boundless Learning

*  Lead user acquisition, customer development, and product teams
*  Established company's initial direct to consumer marketing strategy including targeted online and offline campaigns
*  Established and executed ongoing customer development efforts
*  Raised approx $10M in VC funding

Co-Founder, CMO

Viximo Inc.

***Acquired by Tapjoy***

Viximo is one of the largest social game distribution companies working with premium social game developers and international social networks.  I co-founded Viximo with Sean Lindsay in Fall of 2007.  During my time at Viximo I held multiple executive roles taking on new challenges as the company grew. 

*  Raised over $10M in funding from North Bridge Venture Partners and Sigma Partners
*  Steered company through pivot to successful product/market fit
*  Established Viximo's social gaming distribution business signing deals with major distribution sites including IMVU, Tuenti, VZnet, Bebo, MyYearBook, and major social game developers including LOLapps, Casual Collective, Game Insight, Playrix, and more.
*  Developed Viximo's initial direct to consumer strategy on platforms such as VZnet, growing to over 100K DAU's in under 2 months
*  Directed company's B2B marketing strategy

Founder, VP of Growth

Zoom Information

Managed the development and implementation of social features into Zoominfo.com

Product Manager

Unique Productions

***Acquired By Multiple Parties***

* Founded an event entertainment company at the age of 20
* Bootstrapped to profitability in first year
* Signed clients such as General Motors, Toyota, CDW Inc, Major League Baseball, National Football League, Chicago Bulls, and more

Founder, President

Founder/CEO @ Reforge, Venture Partner @ Long Journey Ventures, Previously VP Growth @ HubSpot

Growth, Customer Acquisition, Marketing, Product Management, Social Networking, Social Gaming, Game Mechanics, Online Communities, Startups, Entrepreneurship,

Introducing Reforge Build: AI prototyping that starts from your product, not from zero. Try it here: https://reforge.com/build

AI app builders are built for founders starting from scratch, not product teams building from an existing product. Using a 0 to 1 tool, for 1 to N work means:

❌ Generic prototypes that miss the mark
❌ Describing your brand colors to AI for the 47th time
❌ Prototyping variant A, then starting completely over to explore variant B
❌ Switching between 5 tools to get from idea to validated feedback
❌ You end up fighting the tool instead of working with it.

Reforge Build is fixing this. It’s AI Prototyping built for Product Teams. 

You can try it here: https://reforge.com/build

➤ Prototypes that look like your product
➤ AI that remembers your customers, feature set, and strategy
➤ Plan mode that structures ideas before generating
➤ Generate multiple variants, not just one output
➤ Built-in collaboration with sharing, commenting, and remixing
➤ Validate with customers w/ AI Interviews and Surveys (coming soon)

The difference?

AI App Builders
Start From Scratch → Build An App → Deploy + Host

Reforge Build
Start From Your Product → Explore Variants → Collaborate → Validate

If you’re a product team not building your first app, stop using tools that assume you are.

Other tools are built for starting from scratch. We are building for the reality of product teams.

Reforge Build is now available with a free tier (link 👇)

Like & Comment “Let’s Build!” and I’ll DM you a free month of premium and additional credits. Hit that repost button, and I’ll DM you two free months.

Founder/CEO @ Reforge, Advisor @ Long Journey Ventures, Previously VP Growth @ HubSpot

We are in the midst of finalizing our 2026 strategic bets for Reforge.  I keep coming back to Company strategy vs Product Strategy. There is a difference and they get confused.

Ravi Mehta defined this in a Reforge block post called “The Strategy Stack” 

Company strategy defines things like:
Target Audience
Differentiation
Defensibility
Core Value Prop
Company Level Bets
Company Goals

These are things that cross-cut across Product Strategy, Marketing Strategy, Technical Strategy, Sales Strategy, etc. It’s the job of each function like product, marketing, etc to deliver the company strategy in their respective areas.

Product Strategy eloquently defined by Fareed Mosavat and Casey Winters defines your portfolio of product bets to bring the company strategy to life:

Product Market Fit Work
Core Feature Work
Growth Work
Scaling Work
Product Market Fit Expansion

The portfolio mix needs to shift depending on your company’s strategic initiatives and competitive environment. Each of the bets should have a measure of success that captures their progress.

In the past year, we saw many companies shift a larger part of their portfolio to PMF Expansion to try and capture some of the leverage and opportunity that AI has provided. You’ve seen this as the product portfolios of many companies expanded dramatically.

Reforge itself launched four (!) new products in the span of 9 months with a pretty small team:

💡 Reforge Insights - aggregates, analyzes, and helps act on fragmented customer feedback.
🎤 Reforge Research - captures new product insights w/ AI interviews and surveys.
👷 Reforge Build - AI prototyping that starts from your product vs scratch.
🚀 Reforge Launch - Launch, manage, and analyze AI features.

Does the PMF Expansion blitz continue? Or do teams shift the portfolio to the other areas to fill in the PMF Expansion they landed in the past year?

As I think through 2026 for Reforge, a principle that is ringing in my ears is something Shaun Clowes said earlier this year on Unsolicited Feedback.

“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.

Remember, it doesn't just have to be an insight, it has to be an insight that others are not acting on. Because if you find that insight and others are not actioning it, that's your competitive advantage.”

In other words, where are others likely to head? And what’s the opposite of that? This is where “alpha” is found. Tough to find in this market.  Even tougher to action.

How are you thinking through 2026?

Product Advisor | Previously EIR @ Reforge, CPO @ Tinder, Product @ Facebook, TripAdvisor, Xbox.

Visiting Partner, a16z speedrun. Product & Growth Advisor for PLG Companies.

Co-founder & CEO of SuperMe

CPO @ Confluent, fmr CPO Mulesoft @ Salesforce, Head of Growth @ Atlassian

Oof. I feel like I’ve been on an AI hamster wheel. 🐹 I think a lot of others have as well. I’m experimenting with a couple of ways to get out of it…

The AI Hamster Wheel

1. New AI improvements come out.
2. I add them to my list to try out and implement.
3. But then I have a long list of existing work/goals that consume my time.
4. More new AI improvements come out.

The result? My list only grows and my adoption can remain surface-level in a lot of places. No time to truly step back and think about how I might do something completely differently.

And I think I’m probably in the top 10% in terms of my own adoption of AI.
So, how do you break out? I think you need dual tracks:

1. Continuously try new things in your day to day work flows.
2. Create periodic dedicated containers to go deeper and take higher risk exploration swings.

#2 is extremely hard. I’m a Founder/CEO. I’m a father of two young, very demanding boys. I’m in my early 40’s, trying to maintain my health.

But honestly, it feels like if I don’t do it, I’ll be that irrelevant 45-year-old in tech talking about the good ol’ days at HubSpot from two decades ago.

So in January, I’m organizing a long weekend with about 6 friends. We are going to stay in a nearby Airbnb. In the morning and early afternoon we are all going to go deep on something. In the afternoon/evening we are going to do a show and tell.

My hypothesis on this:

- I get to go deep on something but also learn from others. More juice in the squeeze.
- I think the group environment will help on accountability.
- Changing my physical environment will help me get in a different head space than my day to day.

The same principle applies to teams. I think you need dual tracks:

1. Your core team needs to be continuously trying things in their day to day.
2. You need to create a separate, dedicated container to create focus and take higher risk swings.

Some teams have done hack weeks, etc. I don’t think it will be enough for most. The most effective orgs I’ve talked to have a completely separate team, dedicated to experimenting ways to do things with AI.

The second team is needed because:
a. It creates dedicated time, people, and space to take bigger swings.
b. You can create the right environment to take riskier swings
c. You can choose the right people for the task (because not everyone is well suited for this)

To getting off the hamster wheel!

The goal of implementing AI on product teams isn’t to produce more code. It’s to produce more product value that your customers adopt.

The historical constraint was engineering. As a result, I think there has been an over focus on adopting AI to get non-technical team members to ship things to production.

The problem is that the things they are shipping are small, light changes. The type of changes that with the right infrastructure, AI Agents can do today, or will be able to in the very near future.

Is getting your team to push those changes really going to help change the outcome for product teams: meaningful product that customers adopt?
Teams think they are creating leverage when in reality, your opportunity cost is just blowing up.

Is the top tier Designer of the future leveraging AI going to be someone that is decent at design and pushing basic code changes? Or is it the designer who has learned how to use AI to be a top-tier designer?

Is it a PM becoming a mediocre junior engineer with AI? Or is it a PM that learns how to use AI to make more judgment calls on priorities, tradeoffs, nailing positioning and differentiation?

Product is a system between PM, Design, Product. Turning everyone into shipping code to production might relieve one constraint in the system but it just pushes the constraint to another place.

This is the “PM is the new bottleneck” narrative that is forming. It’s the result of not applying AI efforts evenly across the system.

Most great features start half-baked. 

A fuzzy idea from a customer call. A vague customer complaint. A gut feel about what's broken. A "what if" from your shower.

But AI does best with clear directions.  Detailed requirements.  Do you really want to create pages of requirements before you see a single pixel? 

Plan mode in Reforge Build fixes this. It helps you take that half-baked idea and turn it into a detailed AI prototype.

You bring the raw ingredients. Plan Mode does the cooking.

Drop your favorite baked good in the comments, and I'll DM you a code for a free month so you can do some cooking. 

My favorite Apple Pie with a scoop of vanilla ice cream 😋 

Here is how Plan Mode works:

1. First, it looks at your existing context in Build.
2. Then it asks you questions. Not generic questions. Specific ones based on what you're building like a top notch thought partner.
3. It creates a lightweight mockup like if you and a colleague were sketching on a whiteboard.
4. Next, it creates a detailed blueprint.
5. With that blueprint in hand, AI builds your prototype. Not a random interpretation of vague instructions. 

There are a bunch of examples of this where find it helpful:

😵‍💫 From Vague Customer Feedback To Clear Improvements
When I get vague customer feedback like "That dashboard is too messy and confusing" it helps me clarity, create options, and explore. 

🚿 From Shower Thought to Shipping A Features
Many great ideas simply start with - "What if users could do X."  Plan mode helps shape that raw idea. 

📉 From Metric Problem To Solution
"Activation dropped 12%." Rather than creating generic onboarding, Plan Mode will ask clarifying questions, propose options, and create something targeted.

We've been building Reforge Build specifically for product teams.  Their workflows, their challenges, their constraints.  

You can capture your product/design in one click with our extension and start prototyping immediately.  

Drop your favorite baked good in the comments and I'll DM you a code for a free month so you can do some cooking.

Kyle Ledbetter and Andy Keil (founders of Dreambase.ai) have built more product than any other 2 person team I've seen.  

I first heard them on a podcast a couple of months ago and was fascinated by how they were building.  We wrote a full post on their process. 

There are a few unique things they were doing:

☁️ AIR (AI Requirements Doc)

First, they would collapse their entire product vision into one markdown document to feed to AI.  

It's not traditional documentation; it's specifically designed to make AI effective.  Giving it full context on product strategy, user flows, design principles, etc for every conversation. 

🧑‍🍳 Bake Off

When it's time to bring that to life, they do a bake off across multiple AI tools to get a variation of ideas.  (note, you can do this natively in Reforge Build now)

🧱 Starting with/ The Data

Rather than starting with UI, they start with the data schema.  This is something Ravi Mehta has also written about and shown how to do in Reforge Build. 

This creates better outcomes with AI because as Kyle says:

"You can't hide behind vague product requirements when you're defining a database schema. What actually is a "user"? How do workspaces relate to projects? Can a user belong to multiple workspaces?"

🙇‍♂️ Reorganizing Around Learning

Last, they split responsibilities based on what type of work cadence is needed to get it done effectively.  

One is fully dedicated to coding with AI.  The other is fully dedicated to anything touching customers (demos, research, feedback, support). They are constantly tightening how one feeds the others.  

I liked this description on why:

"When I'm working with Cursor or Claude, I'm in what I can only describe as a bare-knuckle boxing match with the LLM. I'm close to getting something working. One more prompt. One more test. I know I'm almost there.

You can't do that effectively if you're jumping into customer calls every 30 minutes. You need deep, uninterrupted time. "

Full post in the comments.

Dreambase.ai Co-founder

Building Dreambase.ai ⚡ Database-first analytics for AI-native teams ✨ Pickleball player 🏓

Dreambase.ai

"Most AI prototypes are toys, not tools." - Ravi Mehta  
This is an excellent step-by-step on how to build data-driven prototypes...

Ravi talks about how most teams start with design first in their prototypes.  But this typically leads to "vibe prototyping" their way to demos that impress for 30 seconds but crumble in user testing.

Instead, you need to start with the data...

Step 1: Generate the Data First 
Don't jump into your prototyping tool. Start with Claude/ChatGPT and create structured JSON data with:

Real object relationships (users, items, comments)
Authentic content (actual hotel names, real photos via Unsplash)
Believable user interactions (message threads that sound human)

Step 2: Build from Data 
Feed that JSON into your prototyping tool with a simple prompt: "Generate [feature] from this data"

Now the AI focuses on UX only—not juggling design, data, and content simultaneously.

Step 3: Swap & Test 
Need to test a Thailand trip instead of Paris? Generate new data with the same schema. Swap the file. Done.

Your prototype instantly updates. No rebuilding from scratch.

The teams shipping real products don't need another app builder. They need prototypes that:

-> Look like their actual product (not a generic template)
-> Use real customer data patterns (not Lorem ipsum)
-> Test actual hypotheses (not just look pretty)

This is 1→N work, not 0→1.  

Full post here: https://lnkd.in/gsU5Kvaw

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