Asha Sharma (CVP of AI Platform at Microsoft)28 August 2025

How 80,000 companies build with AI: products as organisms, the death of org charts, and why agents will outnumber employees by 2026

5Frameworks

14Insights

Episode overview

Microsoft AI platform executive Asha Sharma explains why AI products are becoming continuously learning “organisms” whose defensible value lies in their data, reward, evaluation, and post-training loops. She argues that agents will reshape organizations from hierarchical org charts into task-oriented work charts, while successful teams become AI-fluent and organize around full-stack builders. The conversation also covers code-native interfaces, model ensembles, seasonal planning, platform fundamentals, and the growing importance of reinforcement learning and post-training.

Key ideas

AI products are shifting from static artifacts to adaptive organisms that improve through interaction, feedback, reward design, and continuous optimization.
Successful AI adoption progresses from workforce fluency, to improving existing processes, to using AI for growth and new categories.
Full-stack, polymath builders can shorten the hundreds of cross-functional touchpoints that slow conventional product organizations.
Interfaces will become more code-native and composable because text streams work naturally with language models and agents.
As agents proliferate, hierarchical org charts may become task-oriented work charts with fewer management layers and stronger observability.
Planning can be organized into variable-length industry “seasons,” loose quarterly OKRs, four-to-six-week squad goals, and deliberate slack for change.
Reliable platforms win through foundational qualities such as performance, availability, privacy, safety, data residency, and high-quality retrieval.
Companies will increasingly adapt diverse existing models through fine-tuning and reinforcement learning instead of relying solely on off-the-shelf or self-trained foundation models.

Transcript available · source text is retained privately and is not published

Frameworks in this episode

Innovation4 steps

Adapt the Model, Don't Build One

Post-training is the new pre-training — steer an off-the-shelf model to your outcome instead of pre-training your own

Read →

Strategy4 steps

Planning in Seasons

Replace rigid roadmaps with secular 'seasons', loose quarterly OKRs, and deliberate slack

Read →

Innovation5 steps

Product as Organism: the metabolic loop

Treat an AI product as a living system that ingests signals, tunes on rewards, and improves with every interaction

Read →

Strategy3 steps

The Enterprise AI Adoption Ladder

Three sequential stages that separate companies winning with AI from those spinning their wheels

Read →

Strategy3 steps

Win on the Platform, Not the Features

Durable products win on invisible infrastructure — reliability, reach, privacy — not on the feature list

Read →

People & resources mentioned

Attributed to the moment in the episode. Timestamps are approximate.

People · 6

Asha SharmaMentionschief vice president of product for Microsoft's AI platform where she oversees their AI infrastructure foundation models and agent tool chains
“Today my guest is Asha Sharma.”
▶ Watch at ~1:00approx.
Asha SharmaMentionschief vice president of product for Microsoft's AI platform where she oversees their AI infrastructure foundation models and agent tool chains
“Thanks for having me.”
▶ Watch at ~4:00approx.
Lenny RachitskyMentionstoday we've got another very special compilation episode something I've been pulling on more and more with the podcast and the newsletter
“Asha, thank you so much for being here and welcome to the podcast.”
▶ Watch at ~4:00approx.
Michael TruellMentionsMichael is co-founder and CEO of Any Sphere, the company behind Cursor.
“when I had Michael Terrell on the podcast, the cursor co, he talked a lot about how their big mode is the data”
▶ Watch at ~6:00approx.
Bret TaylorMentionsHe co-created Google Maps, co-founded FriendFeed and Quip, held senior roles at Facebook and Salesforce, and co-founded Sierra.
“We had a Brett Taylor in the podcast, founder of Sierra, and he had a similar prediction”
▶ Watch at ~18:30approx.
Satya NadellaMentionsDiscussed as Microsoft's CEO and as a leader with a strong appetite for learning and refining mental models.
“very few people get to work with Satya. He's quite a quite a successful leader.”
▶ Watch at ~39:30approx.

Resources · 35

GPT-5Mentionssoftware · OpenAI
“How does anyone plan a road map when there's just like, okay, JPT5's out?”
▶ Watch at ~0:30approx.
Messenger KidsMentionssoftware · Meta
“where she ran Messenger, Instagram direct, Messenger Kids, and remote presence”
▶ Watch at ~1:00approx.
Instagram DirectMentionssoftware · Meta
“where she ran Messenger, Instagram direct, Messenger Kids, and remote presence”
▶ Watch at ~1:00approx.
The Home DepotMentionscompany
“She also sits on the boards of the Home Depot and Coupang”
▶ Watch at ~1:00approx.
CoupangMentionscompany
“She also sits on the boards of the Home Depot and Coupang”
▶ Watch at ~1:00approx.
MicrosoftMentionscompany · Bill Gates and Paul Allen
“Asha's chief vice president of product for Microsoft's AI platform”
▶ Watch at ~1:00approx.
InstacartMentionscompany · Apoorva Mehta, Max Mullen, and Brandon Leonardo
“She was previously COO at Instacart”
▶ Watch at ~1:00approx.
MetaMentionscompany
“and VPR product at Meta where she ran Messenger, Instagram direct, Messenger Kids, and remote presence”
▶ Watch at ~1:00approx.
MessengerMentionssoftware · Meta Platforms
“where she ran Messenger, Instagram direct, Messenger Kids, and remote presence”
▶ Watch at ~1:00approx.
Lenny's NewsletterCoinednewsletter · Lenny Rachitsky
“if you become an annual subscriber of my newsletter, you get a year free of 15 incredible products”
▶ Watch at ~2:00approx.
InterpretMentionssoftware
“This episode is brought to you by Interpret. Interpret is a customer intelligence platform”
▶ Watch at ~2:30approx.
DXMentionssoftware · DX
“Today's episode is brought to you by DX, the developer intelligence platform designed by leading researchers.”
▶ Watch at ~3:30approx.
CursorMentionssoftware · Anysphere
“the data that they capture from people using cursor, selecting, accepting certain suggestions, not accepting other suggestions”
▶ Watch at ~6:00approx.
GitHubMentionssoftware · GitHub
“GitHub has very similar features that we're using kind of an ensemble of models”
▶ Watch at ~15:00approx.
SQLMentionssoftware
“databases kind of went from the desktop kind of down into SQL”
▶ Watch at ~16:30approx.
TerraformMentionssoftware · HashiCorp
“I think cloud was all about consoles and now it's about Terraform.”
▶ Watch at ~16:30approx.
GitHub CopilotUsessoftware · GitHub
“whether it's in the IDE whether it's in GitHub copilot whether it's in you know some new development environment”
▶ Watch at ~18:00approx.
SierraMentionscompany · Bret Taylor and Clay Bavor
“We had a Brett Taylor in the podcast, founder of Sierra”
▶ Watch at ~18:30approx.
Microsoft PowerPointUsessoftware · Microsoft
“everybody uses Word and PowerPoint.”
▶ Watch at ~19:00approx.
Microsoft WordUsessoftware · Microsoft
“everybody uses Word and PowerPoint.”
▶ Watch at ~19:00approx.
FinMentionsproduct · Intercom
“This episode is brought to you by Finn, the number one AI agent for customer service.”
▶ Watch at ~25:00approx.
SynthesiaMentionscompany
“top AI companies like Anthropic and Synthesia.”
▶ Watch at ~25:30approx.
AnthropicMentionscompany
“Finn is trusted by over 5,000 customer service leaders and top AI companies like Anthropic and Synthesia.”
▶ Watch at ~25:30approx.
SparkUsessoftware · Microsoft
“We use Spark to create prototypes.”
▶ Watch at ~27:00approx.
iPodMentionsproduct · Apple
“you know the iPod like you you go forward.”
▶ Watch at ~29:30approx.
WebvanMentionscompany
“there was web vans and web bands didn't work but Instacart did work because of a different way of thinking about it.”
▶ Watch at ~29:30approx.
ManusMentionssoftware · Monica
“Manis's breakthrough in the space was that they could like do these tool calling loops”
▶ Watch at ~34:00approx.
Porch GroupMentionscompany
“one of the first companies that I worked at was a company called Porch Group.”
▶ Watch at ~36:00approx.
WhatsAppMentionssoftware · Meta
“WhatsApp didn't win because it had stickers or stories or dark mode.”
▶ Watch at ~37:30approx.
ReactMentionssoftware · Meta
“the world is React components that are out there.”
▶ Watch at ~47:00approx.
For All MankindRecommendstv show
“For All Mankind. For all mankind, I like season 4.”
▶ Watch at ~50:00approx.
F1Recommendsfilm · Joseph Kosinski
“Formula 1, Saw Twice”
▶ Watch at ~50:00approx.
DeWalt PowerPackRecommendsproduct · DeWalt
“there's this new kind of new to me DeWalt kind of power pack and they use pouch cells.”
▶ Watch at ~50:30approx.
Brilliant Smart Home SystemUsesproduct · Brilliant
“We also are testing out this new brilliance smart home kind of system.”
▶ Watch at ~51:00approx.
Lenny's PodcastCoinedpodcast · Lenny Rachitsky
“You can find all past episodes or learn more about the show at lennispodcast.com.”
▶ Watch at ~57:00approx.

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Insights & moments

The myth-busts, hot takes, explainers, and tools worth keeping.

Hot Take· 4

Hot Take11:00

Build for the slope, not the snapshot

With something like 70,000 enterprise AI tools launched in a single year, Sharma argues it's impossible to know which one to bet on. Instead of getting locked into any single technology, companies should bet on a platform or app-server layer that lets them swap tools in and out. The mindset should optimize for where things are heading, not the current state.

Roughly 70,000 enterprise AI tools launched in one year
Don't get beholden to any single tool — the whole stack will change
Bet on a platform layer that lets you swap technologies in and out
Build for the slope (trajectory) rather than the snapshot (current state)

“something like 70,000 enterprise tools like in the AI space launched last year.”
Asha Sharma · 11:30

“Feel like you have to actually build for the slope instead of the snapshot of where you are.”
Asha Sharma · 11:30

#platform#tooling#enterprise#strategy

Hot Take20:30

The org chart becomes the work chart

Sharma predicts that as the marginal cost of good output approaches zero, demand for productivity explodes and you scale it with agents — both embedded (tools/software) and embodied (assignable workers). When that happens, the org chart becomes the 'work chart': tasks and throughput matter more than hierarchy, and organizations need far fewer layers. Humans still decide how AI is used, but teams organize outward and task-based rather than upward.

The marginal cost of a good output is approaching zero, driving exponential demand for output
You scale that with agents — embedded (tools) and embodied (assignable workers)
The org chart becomes the work chart; tasks and throughput beat hierarchy
Organizations need fewer layers; humans still decide how AI is applied

“we're approaching this world in which the marginal cost of um a good output is approaching zero.”
Asha Sharma · 21:00

“the work chart uh the org chart starts to become the work chart.”
Asha Sharma · 21:30

#agents#future-of-work#org-design#productivity

Hot Take39:30

Satya's lesson: optimism is a renewable resource

Sharma's single biggest leadership lesson from Satya Nadella is that optimism is a renewable resource. Despite 50 years of reasons not to succeed, his ability to generate energy and clarity and renew everyone's commitment to the mission — every day, in a fiercely competitive talent market — is what she finds most remarkable. She frames it as 'vibes': you have to follow a mission bigger than yourself.

Satya Nadella's standout trait is treating optimism as a renewable resource
He renews everyone's dedication to the mission daily through energy and clarity
The growth mindset is real, but generating energy in a competitive talent market is rarer
Sharma frames it as 'vibes' — a sense of duty toward a mission bigger than yourself

“optimism is a renewable resource.”
Asha Sharma · 39:30

“the only people that'll remember you working late are your kids.”
Lenny Rachitsky · 41:30

#leadership#satya-nadella#culture#motivation

Hot Take48:00

Bet on a model system, not one model to rule them all

Sharma is firmly in the 'model system' camp and believes in model diversity rather than one model to rule them all. Different models fit different use cases — Sonnet 4 is great for some, GPT-5 for others — and some tasks care about latency while others tolerate thinking time. She distinguishes this from an 'ensemble of models,' which she defines as multiple models you fine-tune and deploy independently.

Sharma believes in model diversity, not one model to rule them all
Different models suit different use cases (e.g. Sonnet 4 vs GPT-5) and latency needs
An 'ensemble' is multiple models fine-tuned and deployed independently
Terminology is still being invented as the field moves fast

“I'm much more in the model system camp. Like I believe in uh model diversity.”
Asha Sharma · 48:00

“I'm much more in the like model system uh rather than one model to rule them all.”
Asha Sharma · 48:30

#model-diversity#ensemble#architecture#llms

Explainer· 4

Explainer04:30

From product as artifact to product as organism

Asha Sharma argues products are no longer static artifacts you ship and dashboard, but living systems that improve with every interaction. Because models can now tool-call and act, the winning skill is a product team's 'metabolism' — ingesting data, digesting a rewards model, and tuning to outcomes like price, performance, or quality. She calls these self-improving products the new IP of every company.

Products shift from static artifacts to systems that get better with more interactions
The new KPI is a team's 'metabolism': ingest data, digest a rewards model, produce an outcome
You tune models toward specific outcomes — price, performance, or quality
Self-improving products become the core IP of a company

“all of a sudden these are these living organisms that just get better with the more interactions that happen.”
Asha Sharma · 06:00

“products that think and live and learn, which is kind of exciting.”
Asha Sharma · 06:00

#ai-products#product-strategy#post-training#ip

Explainer06:30

Why post-training is the new pre-training

Sharma explains that once base models are strong enough, the economic leverage moves from expensive pre-training to fine-tuning and reinforcement learning on your own data. She cites a Nathan Lambert study finding that past ~30 billion parameters the capex to train a model from scratch stops making sense, so you optimize on the loop instead. She predicts as much money will eventually be spent on post-training as pre-training.

Past ~30B parameters, training a model from scratch stops making economic sense
Leverage shifts to fine-tuning and reinforcement learning on proprietary or synthetic data
She predicts post-training spend will eventually match or exceed pre-training spend
Adapting an off-the-shelf model beats building your own for most companies

“you know once a model hits 30 billion parameters the capex to actually train a model”
Asha Sharma · 06:30

“I believe we will see you know just as much money spent on post-training uh as we will on pre-training and in the future more…”
Asha Sharma · 45:00

#post-training#fine-tuning#reinforcement-learning#economics

Explainer12:00

The rise of the full-stack builder: the loop, not the lane

Sharma notes that a normal org needs ~10 steps and 5-7 functions across 6-7 layers to ship a product — around 500 touch points, which is untenable when hundreds of new models and technologies arrive weekly. The answer is the full-stack 'polymath' builder who owns the whole loop. Whatever your function, you must obsess over cost, rewards design, and UX for both people and agents.

A normal org needs ~10 steps, 5-7 functions, and 6-7 layers — around 500 touch points to ship
That process is insufficient when 500 new models/technologies appear weekly
Full-stack 'polymath' builders own the whole loop and gain velocity and throughput
It's about the loop, not the lane — every function must obsess over cost, rewards, and UX

“when there are 500 models available a week or 500 new technologies, that is just insufficient.”
Asha Sharma · 12:30

“it's all about the loop not not the lane here.”
Asha Sharma · 13:30

#full-stack#roles#polymath#product-teams

Explainer16:30

From GUIs to code-native interfaces: composability over canvas

Sharma sees products moving from graphical UIs to code-native interfaces, the same historical pattern as databases going to SQL and cloud consoles going to Terraform. Because a stream of text connects better with LLMs, the future favors composability over the canvas. Product makers should rewire their mindset away from obsessing over UI and toward how something composes and how an agent can read it.

Interfaces follow history: desktop-to-SQL, cloud consoles-to-Terraform, now GUIs-to-code-native
A stream of text connects better with LLMs, favoring composability over the canvas
Builders overspend on UI and underspend on how things compose and scale
Design for how agents read and collaborate, not just how humans click

“databases kind of went from the desktop kind of down into SQL. I think cloud was all about consoles and now it's about Terraform.”
Asha Sharma · 16:30

“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…”
Asha Sharma · 17:00

#interfaces#agents#composability#product-design

Story· 2

Story15:00

How Microsoft's Dragon jumped to 83% acceptance with expert labeling

Sharma gives a concrete example of the fine-tuning loop with Dragon, Microsoft's AI product for physicians. Moving from synthetic fine-tuning to annotating 600,000 physician-patient interactions by experts, then continuously optimizing, drove the character acceptance rate from 30-60% up to about 83%. Crucially, this was done by a small cross-functional group, not a large organization.

Dragon is an AI product for physicians
Switching from synthetic data to 600,000 expert-annotated interactions transformed results
Character acceptance rate rose from roughly 30-60% to about 83%
A small cross-functional team, not a big org, drove the improvement by iterating in the loop

“we annotated 600,000 patient um physician interactions by experts and actually fed that into the model”
Asha Sharma · 15:30

“I think we're sitting between 30 and 60 character acceptance rate depending on the run to something like 83 3%.”
Asha Sharma · 15:30

#healthcare-ai#fine-tuning#data-labeling#case-study

Story37:00

Platforms win on invisible infrastructure, not features

Sharma's biggest career lesson is that platforms win on the invisible work, not the pixels. WhatsApp didn't win on stickers or dark mode — it won on the phone book, reliability, speed, and end-to-end privacy. Instacart is really a billion items updating 3,000 times a minute. She wishes she'd known sooner that data residency, availability, reliability, and the right tool selection are what actually matter.

WhatsApp won on the phone book, reliability, speed, and end-to-end privacy — not features
Instacart is a billion items updating 3,000 times every minute
Porch's breakthrough was a matching engine, not the home-report features
For AI platforms, data residency, availability, and reliability are the real moat

“WhatsApp didn't win because it had stickers or stories or dark mode.”
Asha Sharma · 37:30

“It's the data residency so the hospital in Germany that's fine-tuning a model can do so in confidence and the data isn't going to leave…”
Asha Sharma · 38:30

#platforms#infrastructure#whatsapp#reliability

Tool· 1

Tool49:30

Asha Sharma's most-recommended books

In the lightning round, Sharma names the books she recommends most. At work it's Thinking Machine, which she frames as treating the cause not the symptoms — like solving traffic through walkability and mobility rather than speed bumps. Personally, her Instacart CMO recommended Tomorrow and Tomorrow and Tomorrow, which she has reread multiple years running.

Work recommendation: Thinking Machine — treat the cause, not the symptoms
Systems example: solve traffic via walkability and mobility, not speed bumps
Personal favorite: Tomorrow and Tomorrow and Tomorrow, recommended by Instacart's CMO
She has reread the latter several years in a row

“It's probably Thinking Machine. Uh so it's all about treating the the cause not the symptoms.”
Asha Sharma · 49:30

“recommended to me Tomorrow and Tomorrow and Tomorrow, and I read it like last month and last year and the year before because I love…”
Asha Sharma · 50:00

#books#recommendations#systems-thinking

Takeaway· 3

Takeaway09:30

What separates companies that win with AI from those that don't

Sharma describes the pattern behind successful AI adopters: everyone becomes AI-fluent, they apply AI to an existing process to feel real P&L impact, then use it to inflect growth. Companies fail when they do 'AI for AI's sake' — many scattered projects with no blueprint, no measurement, observability, or evals, and no treatment of AI as a real investment.

Winners make everyone AI-fluent first, so no one fears the tools
They apply AI to an existing process to feel measurable P&L impact before scaling
Then they use AI to inflect growth via LTV, retention, or new categories
Losers run scattered projects with no blueprint, measurement, observability, or evals

“one is they are embracing AI and everybody becomes AI fluent.”
Asha Sharma · 09:30

“where companies fail is that they're doing AI for AI sake.”
Asha Sharma · 11:00

#ai-adoption#enterprise#measurement#strategy

Takeaway30:30

How to plan a roadmap in a world that changes weekly: seasons

Asked how anyone sets a roadmap when GPT-5 can drop overnight, Sharma describes planning by 'seasons' rather than fixed six-month semesters. A season is defined by secular changes in the industry or from customers and can last three months or a year; it grounds everyone on a shared north-star. Beneath that sit loose quarterly OKRs, four-to-six-week squad goals, and deliberate slack in the system for both the unplanned and the slope.

Fixed six-month semesters are too rigid when the frontier shifts weekly
A 'season' is defined by secular industry or customer changes and can last 3 months to a year
Seasons align everyone on a shared ethos and north-star metric
Loose quarterly OKRs, 4-6 week squad goals, and slack for the slope sit underneath

“think about it as what season are we in?”
Asha Sharma · 31:00

“we try to leave slack in the system not just for the unplanned but for the the slope.”
Asha Sharma · 32:30

#planning#roadmap#okrs#strategy

Takeaway53:00

Maximizing option value beats minimizing regret

Sharma shares how her life motto evolved from a minimize-regret framework to maximizing option value once she had a family. The shift gave new weight to family, health, trust, and relationships because rest and health compound into the future rather than trading off against extra work hours. Her aim at 70 is not to count regrets looking back, but to have accumulated skills, trust, and relationships that create adventures ahead.

Her motto evolved from minimize-regret to maximizing option value
The shift gave more weight to family, health, trust, and relationships
Rest and health compound into the future rather than trading off against work
The goal is looking forward to adventures, not counting regrets looking back

“it was all about maximizing kind of option value and it just gave the things that I naturally cared about like family and health and…”
Asha Sharma · 53:30

“when I'm 70 it's not about what do I look back on in my life and count the number of regrets. It's really about like…”
Asha Sharma · 54:00

#life-motto#option-value#decision-making#wellbeing

Key ideas

Frameworks in this episode

Adapt the Model, Don't Build One

Planning in Seasons

Product as Organism: the metabolic loop

The Enterprise AI Adoption Ladder

Win on the Platform, Not the Features

People & resources mentioned

People · 6

Resources · 35

Insights & moments

◆Hot Take· 4

Build for the slope, not the snapshot

The org chart becomes the work chart

Satya's lesson: optimism is a renewable resource

Bet on a model system, not one model to rule them all

✶Explainer· 4

From product as artifact to product as organism

Why post-training is the new pre-training

The rise of the full-stack builder: the loop, not the lane

From GUIs to code-native interfaces: composability over canvas

❝Story· 2

How Microsoft's Dragon jumped to 83% acceptance with expert labeling

Platforms win on invisible infrastructure, not features

✦Tool· 1

Asha Sharma's most-recommended books

▲Takeaway· 3

What separates companies that win with AI from those that don't

How to plan a roadmap in a world that changes weekly: seasons

Maximizing option value beats minimizing regret

Hot Take· 4

Explainer· 4

Story· 2

Tool· 1

Takeaway· 3