Rupa Chaturvedi

Human Centered AI Institute

Founder

Vybe Snacks

Stanford University

Adjunct Lecturer (Product Design)

Miraj Sustainable Racing Club

California's first sustainable racing club

Founder & President

Reforge

Co-created the "Generative AI Products: How to get from Idea to MVP" course for product managers, design and user research leaders, consultants and founders. Understand AI by getting hands-on with this practical course taught by industry experts.

Partner

Uber

Head of Merchant & Ads Design

First Round Capital

Product & UX Design Mentor

Google

UX Design Manager

Amazon

I manage and lead a team of UX and Personality designers focused on Conversational AI within the Alexa Shopping group in Amazon. Collectively, my team and I conceptualize, deliver design and strategy to help launch customer experiences that span upper funnel, inspiration and product discovery; product evaluation and research, and post purchase needs of our customer. We are fierce advocates for our customers and leverage multiple sources of data from qualitative to quantitative to guide our intuition and measure our efficacy. The impact we have generated is a combination of customer experience vision, strategic frameworks and mental models as well as design systems for interaction with Alexa by voice and devices in the context of shopping.

Sr UX Designer, Alexa Shopping

Lead UX Designer for Augmented Reality (AR) experiences at Amazon

Sr UX Designer

Sentient Technologies

Helped launch an AI-enabled shopping product that will change the way shoppers shop online. 
Conceptualized and designed multi-channel user experiences for a breakthrough visual search technology product - redefining how consumers shop online.

Helped launch a breakthrough AI enabled online shopping product within 5 months.
Contributed from conceptualizing the UI and product strategy/features to final implementation through whiteboard sketches, wire-frames, high fidelity prototypes and design specifications.

Followed a thorough user centered design process to gather insights into shopper behavior and expectations as well as iterative design based on feedback from user testing.

Head of Design - Visual Intelligence

Head of Merchant & Ads Design at Uber, Adjunct Lecturer at Stanford University

I'm building a world-class AI education institute that puts humans at the center of the AI revolution. Our mission is to empower professionals worldwide to create AI solutions that deliver tangible value for both businesses and end users. We're committed to making AI education accessible, enabling people from all backgrounds to help shape technology's future.

Hard truth for UX designers: if your role looks the same in 2 years, that’s a red flag.

If your value is defined by producing UI artifacts (wireframes, mockups, and redlines) you are in a precarious position. AI models are already exceptionally good at pattern matching and interface generation.

But if your value is defined by system design, you have never been more necessary.

There is a shift from "designing for the user" to "designing for the system."

When an AI agent acts on behalf of a user, it doesn't need a UI. It needs:

- Behavioral Specs: What is the acceptable range of autonomy for this model?
- Adaptive Context: How does the system pivot when the user’s intent is ambiguous?
- Human-in-the-Loop Protocols: When must the human step in for oversight?

These are design responsibilities. They require moving upstream to shape what the system learns and how it decides.

This is exactly why I developed the Advanced AI & Agentic Design curriculum. We stop looking at screens and start looking at system behaviors. We focus on defining constraints, designing for non-deterministic outcomes, and building trust in autonomous flows.

The designers who survive this shift will be the ones who can define the guardrails of the systems we are building. Learn more here: https://bit.ly/3MjqTzM

Design & AI Leader | Stanford Lecturer | AI enablement for UX orgs | ex-Uber, Google, Amazon

Google's Head of Product announced a shift from "writing-first to building-first culture."

Microsoft's CPO said "prompt sets are the new PRDs."

Product management went through a transformation in the past 18 months that UX designers are just starting to feel.

When you can see a shift happening in an adjacent discipline, you have visibility others don't. The question is whether you use it strategically.

I've been mapping what the PM precedent signals for UX roles—and how designers can position themselves while the industry is still figuring out what these roles look like.

Check out my latest newsletter for more on this:

This is the AI UX Brief - my weekly newsletter on designing intelligent systems, leadership in the AI era, and navigating the messy reality of AIUX. F

Open article: Redefining UX Value: The Shift from Execution to Influence by Rupa Chaturvedi • 5 min read

Redefining UX Value: The Shift from Execution to Influence

Meta, Google, OpenAI, Anthropic - they're paying AI PMs up to $600K+.
Why? Because these PMs aren't writing PRDs anymore. They're architecting intelligent systems - designing model behavior, shaping evaluation frameworks, defining how AI thinks and decides.

The role has evolved from coordination to creation. And the market is paying accordingly.

Designers have a unique opportunity here. System thinking, behavioral specification, mental models - these are already in our DNA. Now it's about applying them to intelligence architecture.

Product's transformation is our preview. They're 12-18 months ahead on this curve, and their playbook is completely visible. We can see exactly which capabilities the market values and how to develop them.

In this week's issue, I break down what designers can learn from Product's transformation - the specific capabilities commanding these salaries and strategies for positioning themselves effectively.

Open article: The $600K Question: What AI PMs Know That UX Designers Don't (Yet) by Rupa Chaturvedi • 6 min read

The $600K Question: What AI PMs Know That UX Designers Don't (Yet)

I got a message this week from a UX Director at a FAANG company 6 weeks after completing MXDAI:

“Org changes have been constant here, and the structure is getting flatter every quarter. The expectations on designers have shifted too. My team and I are being pulled into more conversations about how the AI behaves, not just how it looks on the surface. PM and Eng expect us to have a clear point of view there, and if we don’t, we’re not really influencing the product. MXDAI helped me step into those conversations with more confidence. It’s made a noticeable difference in how I show up and how my team gets included.”

What resonated with me is how common this has become.

Across companies, designers are being asked to take on a level of strategic influence that isn’t written into the job description but is now core to how teams move forward. 

As orgs continue to flatten, leaders and ICs are figuring out how to stay relevant in a landscape where expectations rise even as layers disappear. The people who can bring clarity to messy AI questions and ambiguous decisions are the ones carrying a different kind of weight.

This is the work we focus on inside MXDAI.

We’re currently the number one AI design course on Maven, and now you know why.

Maven also extended the promo for one more day today, in case you’ve been considering joining the next cohort - take advantage of the 25% tuition discount - offer expires tonight/

If you’re facing similar changes or want to get ahead of what’s coming, feel free to reach out.

cc Maven

There’s been so much talk lately about PMs vibe-coding prototypes and PRDs being replaced with “just build something and show me.”
But here’s what I keep coming back to: designers need these skills just as much as PMs do, maybe more.

Our stakeholders are adopting AI tools at a pace that’s hard to ignore. The workflows between design, product and engineering are shifting fast. If designers don’t build fluency in this space, they’ll get left behind.

Vibe coding isn’t about learning to code. It’s about learning to build.

When you can build with AI tools, even scrappy prototypes, you collapse the distance between an idea and something you can actually interact with. You get to explore AI behaviors and edge cases hands-on instead of trying to explain them through diagrams and long documents.

And as you experiment with different tools, you start developing a deeper understanding of the underlying tech, the constraints, the limitations and what’s truly feasible. That knowledge changes how you design. It sharpens your instincts. It gives you language and clarity your stakeholders instantly recognize.

This is also where designers make a shift. You stop worrying about perfect wireframes and start focusing on how to validate your assumptions. You move from being the person who “hands things off” to becoming a strategic partner who brings real, testable workflows to the table.

Designers who lean into this aren’t just speeding up their process. They’re shaping how AI products come to life.

If you’re ready to invest in these skills, take a look at MXDAI: https://bit.ly/48o50YD 
We learn by doing. You’ll explore a range of AI tools, build real workflows, and leave with a portfolio-worthy project that reflects how AI design actually happens today.

NOTE: Currently, Maven is offering a flat 25% discount on the next cohort and its valid only for this. week.

Brian Balfour

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

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 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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