I write Lenny's Newsletter (the #1 business newsletter on Substack), and host Lenny's Podcast (top ten tech podcast globally). Previously, I spent seven years at Airbnb leading supply growth, conversion, trip quality, and community, and before Airbnb, I was an engineer and a founder (exited to Airbnb).

Lenny Rachitsky

My biggest takeaways from Boris Cherny (Head of Claude Code):

1. Coding is now “solved” for most use cases. Boris hasn’t written a single line of code by hand since November, with 100% of his work now authored by Claude Code. At the same time, he remains one of the most productive engineers at Anthropic, shipping 10 to 30 pull requests daily while leading the team.

2. Anthropic has seen a 200% increase in engineer productivity since adopting Claude Code. As Boris notes, “Back at Meta, with hundreds of engineers working on productivity, we’d see gains of a few percentage points in a year. Now we’re seeing hundreds of percentage points.”

3. AI is moving beyond writing code to generating ideas. “Claude is starting to come up with ideas. It’s looking through feedback, bug reports, and telemetry, then suggesting features to ship.”

4. The next roles to be transformed are those adjacent to engineering. Product managers, designers, and data scientists will see similar transformations as agentic AI expands beyond coding. “Any kind of job where you use computer tools will be next.”

5. Build for the model six months from now, not today. One of Boris’s key principles is to design products for future AI capabilities, not current ones. “It’s going to be uncomfortable because your product-market fit won’t be very good for the first six months. But when that model comes out, you’ll hit the ground running.”

6. Watch for “latent demand.” Claude Code was built by observing what people were already trying to do, and then making it easier. Cowork emerged when they noticed people using Claude Code for non-coding tasks like analyzing MRIs or recovering wedding photos from corrupted drives.

7. Don’t optimize for token cost. Boris advises companies to give engineers unlimited tokens during experimentation phases. “At small scale, the token cost is still relatively low compared to their salary. If an idea works and scales, that’s when you optimize it.”

8. Underfund headcount on purpose. When Boris puts one engineer on a project, they’re forced to let AI do more of the work. Constraint drives creative use of AI tooling, not just faster typing.

9. The most successful people in the future will be generalists. “Try to be a generalist more than you have in the past. Some of the most effective engineers cross over disciplines. The people who will be rewarded most won’t just be AI-native—they’ll be curious generalists who can think about the broader problem they’re solving.”

10. Always use the most capable model, not the cheapest. A less intelligent model often burns more tokens correcting mistakes than a smarter one spends getting it right the first time. Boris runs maximum effort on Opus 4.6 for everything.

Here's the full conversation: https://lnkd.in/gxMjk8Az

Deeply researched no-nonsense product, growth, and career advice

Claude design lead: The classic design process is dead.

Here's what's replacing it.

Jenny Wen leads design for Claude at Anthropic, was previously director of design at Figma, and a designer at Dropbox, Square, and Shopify.

In our in-depth conversation, we discuss:
🔸 Why the classic discovery → mock → iterate design process is becoming obsolete
🔸 What a day in the life of a designer at Anthropic looks like, including her AI tool stack
🔸 Whether AI will eventually surpass humans in taste and judgment
🔸 Why Jenny left a director role at Figma to return to IC work
🔸 The three archetypes Jenny is hiring for now

This conversation changed how I think about the future of design.

Listen now: https://lnkd.in/gXH8vmM9

Thank you to our wonderful sponsors for supporting the podcast: 
🏆 Mercury — Radically different banking: https://mercury.com/
🏆 Orkes — The enterprise platform for reliable applications and agentic workflows: https://www.orkes.io/
🏆 Omni — AI analytics your customers can trust: https://omni.co/lenny

Also available on:
• Spotify: https://lnkd.in/gaeTW3Hg
• Apple: https://lnkd.in/gUBY2Nt3

My biggest takeaways from Jenny Wen (Claude design lead at Anthropic):

1. The traditional design process is breaking down. The classic discover-diverge-converge loop that designers have relied on for years doesn’t work when engineers can spin up seven coding agents and ship a working version before a designer finishes exploring options.

2. Design work is splitting into two distinct modes. The first is supporting execution: consulting with engineers as they build, giving feedback, polishing in code. The second is setting short-range vision, now scoped to three to six months instead of multi-year roadmaps. The vision work is still critical because when everyone can build anything fast, someone needs to point the team in a coherent direction.

3. Build trust through speed, not perfection. Anthropic ships products early, labels them research previews, and then iterates publicly based on real feedback. Jenny argues that what actually degrades a brand isn’t launching something rough; it’s launching something rough and then going silent. If you ship fast, respond to feedback visibly, and keep improving, users will trust you more, not less.

4. The most overlooked hire in design right now is the cracked new grad. Most companies are hiring senior designers with deep experience. Jenny argues that early-career people with blank slates, fast learning curves, and no attachment to legacy processes may be uniquely suited to this moment. They don’t carry baked-in rituals that are now obsolete, and their lack of expectations can actually be an advantage.

5. Chat as an interface isn’t going away. Despite expectations that chatbots were a temporary stop on the way to richer UIs, Jenny sees chat as a permanently valuable interface because it offers infinite flexibility. But she expects a hybrid future where models increasingly generate UI elements on the fly for specific tasks (like the interactive widgets Claude recently shipped) while chat remains the connective tissue between them.

6. Jenny went from design director (12 to 15 reports) back to IC. She questioned whether middle management had a safe future and wanted hands-on time during a period of rapid change. The IC time is giving her hard skills she wouldn’t have gained while managing.

7. AI will likely get better at taste and judgment. Jenny says designers may be holding onto “taste” as a moat too tightly. But someone still has to be accountable for what ships, the same way an engineer is accountable for AI-generated code.

8. Figma is still essential, but for different reasons. Jenny says Figma remains the best tool for rapidly exploring 8 to 10 different design directions on a canvas, something that coding tools handle poorly because they’re too linear and create investment bias toward one direction. For micro-level visual and interaction decisions, spatial exploration still beats sequential iteration.

Watch our full conversation: https://lnkd.in/gunZXqq8

I've teamed up with the team at Maven to put together a series of LIVE workshops centered around the theme of "The AI-Native PM," featuring a stacked lineup of product leaders: Tomer Cohen, Wes Kao, Hamel Husain, Peter Yang, Marily Nika, Ph.D, Tal Raviv, Aman Khan, Hila Qu 曲卉, Ben Erez, Ethan Evans, Caitlin Sullivan, and many more.

The workshops are across 3 themes and all totally free:
1. AI workflows
2. Becoming more technical
3. Product sense & influence

A few years from now, these skills will be table stakes for PMs. If you want to learn where things are heading in a hands-on way, this is the perfect way

Open article: The AI-Native Product Manager by bit.ly

My biggest takeaways from Sherwin Wu:

1. AI is writing virtually all code at OpenAI. 95% of the engineers use Codex, and engineers who embrace these tools open 70% more pull requests than their peers, and that gap is widening over time.

2. The role of a software engineer is shifting from writing code to managing fleets of AI agents. Many engineers now run 10 to 20 parallel Codex threads, steering and reviewing rather than writing code themselves.

3. The average PR code review time has dropped from 10-15 minutes per PR to 2-3 minutes. Every pull request at OpenAI is now reviewed by Codex before human eyes see it, and Codex surfaces suggestions and catches issues up front. This allows engineers to focus on more creative and strategic work while dramatically increasing productivity.

4. The models will eat your scaffolding for breakfast. When building AI products, don’t optimize for today’s model capabilities. The field is evolving so rapidly that the scaffolding (vector stores, agent frameworks, etc.) that seems essential today may be obsolete tomorrow as models improve.

5. Build for where the models are going, not where they are today. The most successful AI startups build products that work at 80% capability now, knowing the next model release will push them over the line.

6. Top performers become disproportionately more productive with AI tools. AI tools amplify the productivity of high-agency individuals, so the gap between top performers and everyone else is widening. The ROI on unblocking and empowering your best people compounds faster than ever in an AI-augmented environment.

7. Most enterprise AI deployments have negative ROI because they’re top-down mandates without bottom-up adoption. Success requires both executive buy-in and grassroots enthusiasm. Sherwin recommends creating a “tiger team” of technically-minded enthusiasts (often not engineers) who can explore capabilities, apply AI to specific workflows, and create excitement throughout the organization.

8. The one-person billion-dollar startup is coming, but with unexpected second-order effects. As AI makes individuals more productive, we’ll see not just billion-dollar solo founders but an explosion of small businesses: hundreds of $100M startups and tens of thousands of $10M startups. This will transform the startup ecosystem and venture capital landscape.

9. Business process automation is an underrated AI opportunity. While Silicon Valley focuses on knowledge work, most of the economy runs on repeatable business processes with standard operating procedures. There’s massive potential to apply AI to these workflows, which are often overlooked by the tech community.

10. The next two to three years will be the most exciting in tech history. After a relatively quiet period from 2015 to 2020, we’re now in an unprecedented era of innovation. Sherwin encourages everyone to engage with AI tools and not take this moment for granted, as the pace of change will eventually slow.

My biggest takeaways from Brian Halligan

1. “When you have to eat a shit sandwich, don’t nibble.” Borrowed from Ruth Porat. The temptation during layoffs is to do a small one and grow into it, and then do another six months later. Just rip the bandaid off.

2. “If you want to kill a plant, have two people water it.” Have a DRI (directly responsible individual) for every problem you want solved. When you’re small, everyone’s in the room and aligned. At scale, no one knows what’s happening across org boundaries. Committees produce the same result as two people watering a plant: it either drowns or dies of neglect.

3. "Never waste a good crisis." The best improvements at HubSpot came after major problems forced them to rethink processes. After a devastating outage in 2019, they completely overhauled their deployment approach, resulting in much higher quality and reliability going forward.

4. Brian’s LOCKS framework for evaluating founders: Lovable (do people really, really want to work for them?), Obsessed (deeply passionate about the problem), Chip on their shoulder (almost all great CEOs have this), Knowledgeable (about their domain), and a Student (constantly learning, like an LLM).

5. The biggest skill gap in young CEOs is being able to give hard feedback. Many Sequoia founders Brian coaches are in their late 20s, have never managed anyone, and now need to layer early employees or transition co-founders into different roles. These conversations are deeply unnatural. Brian’s solution: peer groups of about 15 CEOs at the same stage. Misery loves company, and they learn faster from each other than from a coach.

6. Hire the spicy candidate, not the safe consensus pick. HubSpot used to run eight-person panels. When half scored a candidate 4/4 and half scored 2/4, they’d pick the boring all-3s candidate instead. They flipped this—started choosing the polarizing people with real weaknesses—and their hit rate improved. They also shrunk panels from eight interviewers to four.

7. Starting a company has never been easier. Scaling one has never been harder. AI make building trivial, so the number of companies is mushrooming. But that means unprecedented competition and noise. The bottleneck becomes distribution. Technical founders can build but rarely know how to distribute. Brian says the fastest learners will win. And distribution is about to shift—getting found in ChatGPT and Gemini will matter as much as getting found in Google.

8. Build your team like the 2004 Red Sox—a mix of homegrown talent and strategic outside hires. Companies consistently underrate their internal talent and overrate fancy resumes from big companies. If it’s a close call between promoting internally or hiring externally, give your homegrown talent the chance.

Listen now: https://lnkd.in/gC8PX9vn

When a team is underperforming, most people's first instinct is to blame the people. That's almost always wrong.

After 20+ years at Meta, Google, and Chan Zuckerberg Initiative — and advising leaders at Stripe, Anthropic, OpenAI, and more — this week's beloved guest author Molly Graham has learned that blaming people for structural problems is one of the biggest leadership traps there is.

In her powerful post, she shares a simple diagnostic tool she's used since leading wilderness expeditions in Patagonia at age 22: the Waterline Model.

The Waterline Model helps you answer one question: What's going on below the surface that's making things harder than they should be?

In other words, "snorkel before you scuba."

Read it here (and share it with your manager):

Open article: How to debug a team that isn’t working: the Waterline Model by lennysnewsletter.com

How to debug a team that isn’t working: the Waterline Model

Marc Andreessen calls him "the best AI CEO nobody knows about."

Elad Gil calls his company "the most successful, most quiet company in AI."

Qasar Younis is the co-founder and CEO of Applied Intuition—which brings AI to vehicles, like tractors, planes, submarines, mining rigs, cars, and more.

The company is valued at over $15B, with 18 of the top 20 global automakers (and the U.S. Department of Defense) as customers.

As Qasar shared, "not many people run a $15B+ physical AI company with revenue and free cash flow. And by not many, I think literally zero other people."

And Qasar's personal story is wild: Born on a farm in Pakistan. Emigrated to the U.S. and grew up in Detroit managing engine lines at GM. Harvard MBA. Became COO of Y Combinator (during the era that funded OpenAI, Cruise, DoorDash, and Coinbase). Then left to start Applied Intuition in 2017.

In a rare and in-depth interview, we discuss:
🔸 The counterintuitive reason he's stayed quiet and built in private
🔸 Why reading old books and cleaning your own office makes you a better founder
🔸 How to build a culture where the best idea wins, not the loudest voice
🔸 Why the best companies show traction early—and what to do if yours doesn't
🔸 How physical AI will transform farming, mining, and construction before it ever reaches your home

Listen now 👇
• YouTube: https://lnkd.in/gHzqY9-n
• Spotify: https://lnkd.in/gXQgxMvF
• Apple: https://lnkd.in/ghm3BEmu

Thank you to our wonderful sponsors for supporting the podcast: 
🏆 Omni— AI analytics your customers can trust: https://omni.co/lenny
🏆 Vanta — Automate compliance. Simplify security: https://vanta.com/lenny
🏆 Lovable — Build apps by simply chatting with AI: https://lovable.dev/

This week's post on using AI for your job interview has proven very popular, in particular the AI interview coach that's included in the post.

To take this even further, Noam Segal took all of the feedback he's received about the interview coach and made three major improvements:

1. Enhanced memory: It now tracks which questions companies actually ask, what patterns keep showing up, and what's working (or not) for you specifically.

2. Higher challenge: The coach will push back a lot more, stress-test your stories, and give you more hard truths.

3. More guidance: Every command now ends with a specific recommendation for a next step, vs. waiting for you to figure it out.

Download v2 of the coach here: https://lnkd.in/gZkCBR34

Fintech | Embedded Finance | E-commerce

I am a fintech enthusiast and product leader passionate about crafting simple solutions for complex problems. I have successfully launched several products in the payments and fintech space, consistently driving growth for early-stage startups and enhancing customer satisfaction for established ventures. Additionally, I run the Fintech Wrap Up newsletter, sharing the latest industry news and insights, and serve as a pro bono peer mentor in product management, demonstrating my commitment to giving back to the community.

Sam Boboev

Scaling AI in Banks Is a Governance Problem, Not a Data Problem

A successful AI transformation balances two things at once: near-term financial impact and long-term capability building. Miss either, and the effort stalls.
____

Here’s what actually works.

AI transformation does not start with a platform. It starts with a business strategy where AI is explicit, not implied. Domains and subdomains are selected based on value density, not technical curiosity. Execution then happens at scale, with reuse designed in from day one.

One large bank illustrates this well. Instead of shipping isolated models, it built reusable assets and an end-to-end analytics pipeline powering 50+ machine-learning models. Those components now support more than 150 use cases across the bank. Early impact: ~10% projected revenue uplift from hyper-personalization, lead optimization, and cross-sell. Same core assets. Different domains. Compounding returns.
____

This is the pattern most teams miss.

Transform one subdomain deeply. Move from MVP to industrialized use cases. Extract reusable components. Then plug them into adjacent subdomains. Build the AI stack in phases. Never all at once.
____

The real bottlenecks are not technical.

Teams fail when they optimize for models instead of value. High-performing banks run cross-functional squads where banking expertise, data, AI, and risk sit together from day one. Risk is embedded, not bolted on later. Adoption is designed, not assumed.

Sustained value requires central coordination. The strongest banks run an AI control tower with three clear mandates:

• Own the enterprise AI strategy and continuously reallocate capital based on realized value

• Standardize architecture, tools, and risk protocols while allowing selective federation

• Enforce reuse so assets built once are deployed everywhere

AI transformation is not a collection of experiments. It is a production system for decision-making, distribution, and growth.

That distinction is what separates pilots from platforms.

👉 Subscribe for more insights https://lnkd.in/d94JgWBU

Insights by McKinsey

Founder & CEO at Fintech Wrap Up | Payments | Wallets | AI

Payment Tokenization Explained

If you’re handling payments in 2026, understanding tokenization is a must.

Here's what caught my attention: 62% of merchants and 92% of financial institutions already use tokenization. But many teams still aren't clear on how it works or why it matters for their business.
____

What is Payment Tokenization?

Think of it as a security swap. Instead of storing actual credit card numbers (like 4532-1234-5678-3511), you store a random token (like 4532-8716-5413-2416). That token links to the real payment details stored in a secure, PCI-compliant vault.

When you process a transaction, you send the token. Your payment provider swaps it for the real card details behind the scenes. Simple concept, massive implications.
____

Why It Matters

-> Security: If you're breached, hackers get worthless tokens, not card numbers. The token can't be reverse-engineered.

-> PCI Scope Reduction: Tokens can reduce your PCI compliance scope by up to 90%. Less data = less liability.

-> Faster Checkouts: Returning customers do not need to re-enter their payment details. The friction disappears.

-> Multi-Processor Freedom: With the right tokenization strategy, you're not locked into a single payment provider.
____

The Three Types of Tokens

This is where it gets interesting:

1. PSP Tokens: Issued by your payment service provider. Great for getting started, but they lock you to that provider.

2. Network Tokens: Created by card networks (Visa, Mastercard, Amex). They boost authorization rates and reduce interchange fees, but require network-specific integrations.

3. Merchant Owned or Universal Tokens: Provider-agnostic tokens that work across all your processors and channels. Maximum flexibility, zero vendor lock-in.

Most sophisticated merchants combine universal and network tokens strategically based on their infrastructure and goals.
____

Whether you're scaling globally, managing subscriptions, or trying to reduce fraud, tokenization is foundational infrastructure. The question isn't whether to implement it, but how to do it right for your specific use case.

The payments landscape has shifted from single-processor setups to multi-processor strategies. Independent tokenization is what makes that transition possible without creating a data management nightmare.

👉 Subscribe for more insights https://lnkd.in/d94JgWBU

#paymenttechnology #fintech #tokenization

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