Lenny’s Podcast: Asha Sharma

Lenny’s Podcast: Asha Sharma

📝 CONTENT INFORMATION

Content Type: Podcast Review
Title: 🎙️ Lenny’s Podcast: Asha Sharma
Podcast: Lenny’s Podcast
Episode: How 80,000 companies build with AI: products as organisms, the death of org charts, and why agents will outnumber employees by 2026 | Asha Sharma
Host: Lenny Rachitsky
Guest: Asha Sharma (Corporate Vice President of Microsoft’s AI Platform)
Duration: 1 hour 25 minutes

📓 Podcast Episode Info here

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The future of AI belongs to companies that embrace products as living organisms that learn and evolve, shift from graphical interfaces to code-native interactions, and prepare for a world where AI agents will outnumber human employees by 2026, fundamentally transforming organizational structures from hierarchical charts to dynamic work charts.

💡 ONE-SENTENCE TAKEAWAY

The shift from “product as artifact” to “product as organism” represents a fundamental transformation in how we conceive of and build software, requiring new approaches to product development, organizational structure, and business strategy in an emerging agentic society.

📖 SUMMARY

In this eye-opening episode, Lenny Rachitsky sits down with Asha Sharma, Corporate Vice President of Microsoft’s AI Platform, to explore the revolutionary changes sweeping through the technology landscape as AI continues to evolve at breakneck speed. With her unique vantage point overseeing AI infrastructure for over 80,000 companies, combined with her previous experience as COO at Instacart and VP of Product at Meta, Sharma offers unparalleled insights into where AI is headed and what separates successful AI companies from those struggling to adapt.

The conversation begins with Sharma’s groundbreaking concept of the shift from “product as artifact” to “product as organism” a fundamental transformation in how we conceive of and build software products. She explains how AI models are becoming living entities that improve with interaction, creating a new form of intellectual property for companies. The discussion then explores the movement from graphical user interfaces to code-native interfaces, the rise of post-training as the new pre-training, and the emergence of what Sharma calls an “agentic society” where AI agents will eventually outnumber human employees.

Sharma shares practical insights on how companies successfully implement AI, revealing a clear three-stage pattern: first achieving organization-wide AI fluency, then applying AI to optimize existing processes, and finally using AI to drive growth. She notes that companies that fail typically implement AI for AI’s sake without measurement, observability, or a clear blueprint.

The conversation delves into the changing nature of product development roles, with the traditional separation between PM, engineering, and design dissolving into “full stack builders” who understand the entire product development loop. Sharma also introduces her season-based planning framework as an alternative to traditional roadmapping in rapidly changing environments.

Throughout the episode, Sharma provides concrete examples from Microsoft’s work with thousands of AI customers, illustrating how organizations are transforming their operations and what distinguishes the most successful implementations. She concludes with reflections on her biggest leadership lesson from working with Microsoft CEO Satya Nadella and what motivates her work in AI.

🔍 INSIGHTS

Core Insights

Products are evolving from static artifacts to living organisms that learn and improve through interaction, creating a new form of intellectual property
The competitive advantage in AI is shifting from pre-training foundation models to post-training optimization with proprietary data
Organizational structures will transform from hierarchical reporting relationships to dynamic work charts as agents become more prevalent
Traditional graphical user interfaces are becoming obsolete as code-native interfaces enable better composability with LLMs
Successful AI implementation follows a clear pattern: fluency, process optimization, and growth inflection
Traditional product development roles are converging into “full stack builders” who understand the entire development loop
Planning in AI requires a season-based approach rather than rigid long-term roadmaps
Platform fundamentals (reliability, performance, privacy, safety) remain critical despite rapid technological change

How This Connects to Broader Trends/Topics

Democratization of AI development as post-training becomes more accessible than pre-training
Transformation of work and organizational structures in response to AI capabilities
Evolution of user interfaces from visual to text-based interactions
Convergence of product development disciplines in response to AI complexity
Shift from feature competition to infrastructure and platform differentiation
Growing importance of adaptability and learning capacity in products and organizations

🛠️ FRAMEWORKS & MODELS

Product as Organism Framework

A paradigm shift in how we conceptualize and build products:

From Static to Living: Products are no longer static artifacts but living organisms that evolve and improve with interaction
Metabolism of Product Teams: The KPI becomes the team’s ability to ingest data, digest rewards models, and create outcomes
Continuous Learning Loop: Products that think, live, and learn through ongoing interaction and feedback
New Company IP: These evolving products represent a new form of intellectual property that differentiates companies

The Three Stages of AI Adoption

A roadmap for companies implementing AI successfully:

Stage One: AI Fluency: Everyone in the organization becomes AI fluent, using co-pilots and AI tools in daily workflows
Stage Two: Process Optimization: Apply AI to existing processes to improve efficiency (e.g., reducing fraud resolution time from 15 days to 10)
Stage Three: Growth Inflection: Use AI to drive growth through improved customer experience, co-creation of new concepts, and exponential task completion
Failure Pattern: Companies that fail typically implement AI for AI’s sake without measurement, observability, or a clear blueprint

The Agentic Society Model

Sharma’s vision for how organizations will transform with AI:

Marginal Cost Approaching Zero: As the cost of good output approaches zero, demand for productivity and output will grow exponentially
Agents as Scaling Solution: Agents will scale to meet this demand, far outnumbering traditional software
From Org Chart to Work Chart: Organizational structures will shift from hierarchical reporting relationships to task-based workflows
Human-Agent Collaboration: Humans will bring their own agent stacks to work, expanding their capabilities and skill sets

Season-Based Planning Framework

An alternative to traditional roadmapping in rapidly changing environments:

Identify the Current Season: Determine what secular changes are happening in the industry (e.g., prototyping, models, agents)
Establish Shared Ethos: Ground everyone in understanding customer problems and what winning looks like
Set Loose Quarterly OKRs: Create flexible quarterly objectives rather than rigid long-term plans
Build in Slack: Leave room for unplanned changes and the “slope” of technological evolution

Full Stack Builder Model

The evolving role of product developers in the AI era:

Convergence of Disciplines: The traditional separation between PM, engineering, and design is dissolving
Loop Over Lane: Success depends on mastering the entire product development loop rather than specializing in one lane
Velocity Through Integration: Full stack builders can move faster by eliminating handoffs and communication barriers
Cross-Functional Expertise: Product builders need to understand cost, rewards, system design, and UI/UX

💬 QUOTES

“All of a sudden these are these living organisms that just get better with the more interactions that happen. And in many ways, I think this is the new IP of every single company. And it’s a completely different way to build product and to even think about, you know, products that think and live and learn, which is kind of exciting.” - Asha Sharma on the shift from product as artifact to product as organism
“We’re approaching this world in which the marginal cost of a good output is approaching zero. We’re going to see exponential demand for productivity and output. The way that you scale to that is with agents. When all of that happens, the org chart starts to become the work chart. You just don’t need as many layers.” - Sharma on the emergence of an agentic society
“I believe we will see just as much money spent on post-training as we will on pre-training and in the future more on post-training. We talked a little bit about Nathan Lambert’s study where his review was that you know when a model hits 30 billion parameters it makes more sense to kind of fine-tune and optimize that.” - Sharma on the shifting economics of AI development
“If you think about like a stream of text just connects better with LLMs. And so I think that there’s a bunch of trends that are kind of working in the favor for like the future of products being about composability and not the canvas.” - Sharma on the shift from GUIs to code-native interfaces
“I think it’s all about the loop not not the lane here. And so I think that whatever function you are, you have to be obsessed with trying to understand like the efficiency or the cost of of the product, the actual rewards or you like you know system design that you’re going after, the actual UI UX, how that actually manifests for agents or people.” - Sharma on the evolving role of product builders
“Planning right now is just crazy. How does anyone plan a road map when there’s just like, okay, JPT5’s out? We think about it as what season are we in? Season one might have been prototyping of AI and then it was all around models and reasoning models and now it’s the advent of agents.” - Sharma on adapting planning processes to rapid technological change
“Where companies fail is that they’re doing AI for AI sake. They have a ton of projects that they’re kicking off at the same time without a blueprint to understand how it actually worked from what their stack looks like and they aren’t aren’t treating it like a real investment.” - Sharma on patterns of organizational AI adoption
“The number one learning that I had was look like WhatsApp didn’t win because it had stickers or stories or dark mode. It won on a few premises because one was the phone book. It was the reliability and how fast it was. It was the privacy. And so it wasn’t the hundreds of features. It was all in kind of the infrastructure and the platform.” - Sharma on the enduring importance of platform fundamentals
“I’ve learned that optimism is a renewable resource. I think that his ability to generate energy and to use his optimism to kind of renew everybody’s dedication to the mission is unbelievable and I think it’s such an important part of the culture.” - Sharma on leadership lessons from Satya Nadella
“Some of the most consequential products in the world required a bunch of kind of deterministic like logical sets of inputs and like sparks of creativity and imagination and judgment and vision that could not be achieved without humans.” - Sharma on the enduring value of human creativity in an AI world

⚡ APPLICATIONS & HABITS

Embrace AI Fluency

Make AI tools integral to daily workflows:

Use co-pilots and AI assistants for routine tasks
Encourage experimentation with AI across all teams
Build confidence through hands-on experience with AI tools
Share successes and failures to accelerate organizational learning

Focus on the Loop, Not the Lane

Develop cross-functional expertise in product development:

Understand the entire product development cycle from concept to deployment
Learn about cost structures, reward models, and system design
Gain familiarity with UI/UX considerations for AI products
Break down silos between traditional product development roles

Implement Season-Based Planning

Adapt planning processes to rapid technological change:

Identify the current “season” of AI development in your industry
Establish a shared understanding of customer problems and success metrics
Set flexible quarterly objectives rather than rigid long-term roadmaps
Build slack into schedules to accommodate unexpected changes

Prioritize Platform Fundamentals

Focus on the invisible elements that create great products:

Invest in reliability, performance, privacy, and safety
Measure and optimize for core user experience metrics
Resist the temptation to prioritize features over fundamentals

Develop Organizational AI Readiness

Prepare your organization for the agentic society:

Assess current processes for potential agent integration
Identify tasks that could be enhanced or automated by agents
Develop governance frameworks for human-agent collaboration
Create training programs to build AI literacy across the organization

📚 REFERENCES

Microsoft Azure AI Platform
Nathan Lambert’s study on model efficiency and post-training economics
“Thinking Machines” – Framework for treating causes rather than symptoms
“Tomorrow, and Tomorrow, and Tomorrow” – Lessons on long-term creative endeavors
“The Lean Startup” – Adaptable methodology for rapid iteration
“Reinforcement Learning: An Introduction” – Technical foundation for post-training
“Designing Interactions” – Principles for evolving beyond traditional GUIs
WhatsApp case study on platform fundamentals
GitHub Copilot – Code completion and developer productivity
Dragon AI – Medical documentation and physician efficiency
Instacart – AI in grocery delivery and logistics
Home Depot – Retail transformation and customer experience

⚠️ QUALITY & TRUSTWORTHINESS NOTES

E-E-A-T Assessment

Experience: Excellent. Asha Sharma demonstrates exceptional first-hand experience as Corporate Vice President of Microsoft’s AI Platform, overseeing AI infrastructure for over 80,000 companies. Her insights come from direct involvement with Microsoft’s AI strategy and implementation across diverse industries.

Expertise: Excellent. Sharma shows deep expertise in AI development, platform strategy, and organizational transformation. Her frameworks for product development, AI adoption, and organizational design demonstrate sophisticated understanding of both technical and business aspects of AI implementation.

Authoritativeness: Excellent. As a leader at Microsoft with previous experience as COO at Instacart and VP of Product at Meta, Sharma has established authority in technology platforms and AI implementation. Her perspectives are backed by experience with thousands of AI implementations across diverse industries.

Trust: Excellent. Sharma provides balanced insights about AI development, acknowledging both opportunities and challenges. Her frameworks are grounded in real-world implementation experience rather than theoretical speculation, and she openly discusses both successes and failures in organizational AI adoption.

Quality Assessment

The podcast provides concrete frameworks that listeners can implement in their organizations
Sharma shares specific examples from Microsoft’s work with thousands of AI customers
The conversation balances visionary thinking with practical implementation guidance
The host asks thoughtful follow-up questions that probe deeper into key concepts
The discussion acknowledges uncertainties and limitations in AI development
The content is well-structured with clear transitions between topics
The insights are relevant to product builders, leaders, and organizations at various stages of AI adoption

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