The Flywheel by M13

What if the future of security is no alerts at all?

Lizzy Nammour started on a three-person data security team at Airbnb, trying to track 10 years of data that engineers kept collecting faster than anyone could map—and she built Teleskope so companies can fix data risk automatically instead of drowning in alerts.

In this episode of The Flywheel, M13 Managing Partner Karl Alomar sits down with Lizzy Nammour, founder and CEO of Teleskope. They break down why AI turned data security into an urgent problem: how assistants like Claude surface sensitive files faster than any employee could search for them, why so many DSPM tools ended up as shelfware, and why knowing about a risk you can't fix leaves you liable for it. Nammour explains why the next era of security works like a copilot, with humans always in the loop.

If you're a founder, security leader, or operator trying to understand AI data security, DSPM, or why more alerts don't make a company safer, this is your map.

⏱️ Chapters

• 00:00 — The future of security may be no alerts at all
• 01:02 — From big data to GDPR to AI: how data security got here
• 02:25 — Why security has always meant incident response
• 03:49 — Why data security has to be preventative
• 04:42 — Too many alerts: why companies ignored DLP
• 05:13 — The Airbnb origin story
• 07:14 — What AI changed about sensitive data
• 08:57 — When AI can find your payroll
• 09:24 — Where AI helps: reasoning with business context
• 10:29 — How the CISO role changed with AI
• 12:14 — AI scale-ups with no security
• 13:07 — Why Teleskope: risk without context
• 14:39 — Why DSPM became shelfware
• 16:15 — What customers are saying
• 17:15 — What happens if you don't solve this
• 18:19 — Five to 10 years out: proactive, automated security
• 19:25 — The company Teleskope is building
• 19:58 — Why the CISO job got harder

🛡️ In this episode

• Why incident response comes too late for data security: "your data was already breached, what do you do next?"
• Why companies gave up on early DLP tools, and the Slack channel holding "hundreds of thousands of alerts" that nobody reads
• How a three-person data security team at Airbnb tried to secure 10 years of data, and why GDPR exposed the gap between reports and remediation
• How AI assistants like Claude generate sensitive artifacts on laptops and in the cloud, moving data "much faster and in a much more obscure way than a human could"
• Why AI makes sensitive data easier to find: ask Claude for a company's payroll with Google Drive connected and it answers faster than any person could search
• Where AI helps most: deciding whether data is risky in the context of the business, the way a data security engineer would
• Why the CISO conversation moved from blocking ChatGPT two years ago to securing the AI adoption the board now demands
• Why context decides risk: a tax firm storing Social Security numbers is business as usual, while Airbnb should never store them
• Why DSPM tools became shelfware, and why customers "don't want to know they have a problem unless they can fix it"
• The exposure most companies miss: at one Teleskope customer, the CEO's W-2 was accessible to everyone at the company
• Why every company has a data security initiative now, when three years ago the market still needed convincing

About Teleskope

Teleskope finds sensitive data across a company's systems, decides what's risky in the context of the business, and fixes it automatically, so companies can adopt AI securely. Learn more at https://teleskope.ai

About M13

M13 invests early in outlier founders building at the structural shifts that create new markets. The Flywheel is where M13 talks with those founders. Learn more at https://m13.co

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What happens when the software spending your money doesn't have an ID?

Jonathan Awad built Baselayer into the identity network one in five US financial institutions uses to verify businesses—more than $1 billion in fraud losses prevented—and he's now extending it to the AI agents those businesses deploy.

In this episode of The Flywheel, M13 Managing Partner Karl Alomar sits down with Jonathan Awad, co-founder and CEO of Baselayer, as the company announces a $35 million Series A led by M13. They break down what trust actually costs in B2B: why a bank still takes days to verify a business it can't see in person, why Meta fronting your ad spend is credit, and how fraudsters who were never sophisticated got dangerous the moment they got a model. Awad explains the transitive property behind agent identity. 

If you're a founder, operator, or investor trying to understand KYB, agentic commerce, or where fraud goes next, this is your map.

⏱️ Chapters

• 01:34 — Don't banks already know what a business is? 
• 02:09 — Rural credit unions, manual review, and what COVID broke 
• 03:57 — The uncle, the personal guarantee, and the default that followed him home 
• 06:10 — The core insight: consumer credit got decades of infrastructure, business credit didn't 
• 07:50 — What KYB actually is, and why Meta fronting your ad spend is credit 
• 09:30 — How a bank decides: five days, ten days, and the questions that escalate 
• 11:14 — Why Baselayer was built as a consortium from day one 
• 12:21 — Address clusters and the fraud patterns customers spot first 
• 13:42 — Building for agents: SDKs, bot detection, and delegated identity 
• 16:48 — "What conversations are we not in?" 
• 17:37 — Why everything in financial services takes 24 months 
• 18:27 — Instant settlement, no chargebacks, and money you can't get back 
• 19:11 — Your PII costs $20, and what a model does with it 
• 22:27 — Five years out: agents acting on behalf of people and businesses 
• 24:35 — Why Baselayer wins: the three Ps and the transitive property of trust

🔐 In this episode

• Why one in five US financial institutions uses Baselayer to onboard and underwrite businesses, and what a $35 million Series A led by M13 funds next
• Why a bank can take a day to several weeks to verify a business it can't see in person, and what questions it asks at $2,000 versus $2 million
• Why Meta letting you run ads and billing you 30 days later is a credit decision, and why every marketplace and supplier makes the same one
• The origin story: a personal guarantee that followed Awad's uncle into his own credit, and the infrastructure gap that caused it
• Why Experian, Equifax, and Dun & Bradstreet never built for small business, and what changed after 2020
• How a fraud consortium works: every profile request feeds a signal back to 2,300-plus institutions, including address clusters that route new applications straight to review
• What agentic identity means in practice: an SDK that tells merchants whether a visitor is a bot or an authorized agent, and a credential that carries a business's verified identity to the agents it deploys
• Why instant settlement through stablecoins and FedNow removes chargebacks and refunds, and what that does to fraud economics
• Awad on why most fraudsters were never sophisticated, and what changed when everyone got a model in their pocket
• The transitive property behind agent trust: if an institution trusts the business and the business has no history of deploying bad agents, the agent inherits that trust

About Baselayer 

Baselayer is the risk and identity infrastructure platform used by more than 2,300 financial institutions, Fortune 500s, and government agencies to onboard and underwrite businesses, consumers, and the agents they deploy. Learn more at https://baselayer.com

About M13 

M13 invests early in outlier founders building at the structural shifts that create new markets. The Flywheel is where M13 talks with those founders. Learn more at https://m13.co

▶️ Subscribe 

Subscribe for more conversations with the founders building at the structural shifts that create new markets, and follow The Flywheel on Spotify and Apple Podcasts.

What if the worst year for global trade in a decade turned out to be your best year ever?

Alex Yancher built Passport into the cross-border commerce engine shipping to 150 countries for brands going international, and a year after Liberation Day, with tariffs dominating every headline, the business had its strongest year yet.

M13 Partner Brent Murri sits down with Alex Yancher, founder and CEO of Passport in this episode of The Flywheel. They break down what changed one year after Liberation Day: imports from China falling from roughly 13%–15% to 9%, the tariff inflation that never showed up, and why reshoring is still anecdote instead of data. Yancher—who closed M13's investment the week the world locked down—explains why he went asset-heavy when every board deck said asset-light, and what seven months of AI agents did to his margins. If you're a founder, operator, or investor trying to understand tariffs, cross-border commerce, or where AI is reshaping logistics, this is your map.

⏱️ Chapters

00:00 — Closing an investment while the world shut down
01:44 — March 2020: a term sheet, a lockdown, and a firm that didn't retrade
03:22 — Why Alex started Passport: surfboards, diamond rings, and the hair in cross-border
05:51 — One year after Liberation Day: what changed in global trade
07:38 — Decoupling from China, and the reshoring that hasn't happened
09:25 — The tariff inflation that never arrived
10:30 — Why the hardest year in trade was Passport's best
11:01 — Going asset-heavy when the whole market said asset-light
12:48 — Vertical integration: catching a "spring gift box" before customs does
14:26 — 37 automations, 1%–2% of margin, and a two- to three-year cost advantage
16:51 — The moat AI can't replicate: entities in 14 countries
17:57 — Free shipping or a lower price? What $1 billion in sales data says
19:51 — The white-label ceiling and the acquisition that broke it
22:32 — Cannibalizing cross-border to build in-country

📦 In this episode

• Why M13 closed Passport's round the same week the US went into lockdown—and why Yancher says that one decision set the tone for the next six years
• What shifted a year after Liberation Day: imports from China down from roughly 13%–15% to 9%, the US–China deficit cut in half, and imports from Vietnam and Mexico way up
• Why tariff-driven inflation landed in the low single digits—a 25% tariff on Vietnam was partly canceled out by a 20% fall in the Vietnamese currency against the dollar
• Why reshoring still hasn't shown up in the manufacturing data, and what Yancher is watching for
• Why he asked his board to buy an asset-based logistics company in a market obsessed with asset-light—and how three warehouses in LA, New York, and Chicago moved NPS, churn, and a 15% price drop
• What seven months of AI agents produced: 37 manual processes automated, 1%–2% of margin accretion, and nightly QA across every country, customer, and permutation
• The parts of the business AI can't replicate: entities in 14 countries and direct relationships with regulators like UK HMRC
• How more than $1 billion in sales data answers a real merchandising question: in Canada, at identical margin, does free shipping or a 10% price cut convert better?
• Why Passport leaned into in-country fulfillment even though it cannibalized cross-border—and how that went from 3% to roughly 30% of the business

About Passport
Passport helps consumer brands sell and ship internationally—logistics, duties and taxes, compliance, returns, and growth—across 150 countries. Learn more at https://passportglobal.com

About M13
M13 invests early in outlier founders building at the structural shifts that create new markets. The Flywheel is where M13 talks with those founders. Learn more at https://m13.co

▶️ Subscribe
Subscribe for more conversations with the founders building at the structural shifts that create new markets, and follow The Flywheel on Spotify and Apple Podcasts.

What if "what's the fastest path to $100 million in ARR" is the wrong question entirely? Nami Baral, founder and CEO of Niural, spent two and a half years building an AI-native global payroll stack before going to market because she isn't building for $100 million, she's building for $100 billion.

M13 partner Morgan Blumberg sits down with Nami Baral, founder and CEO of Niural, where they break down why global payroll is one of the sleepiest, yet most complex markets in software and why AI agents finally made the full stack buildable. Nami—who joined Twitter pre-IPO and sold her last company to Acorns—explains how Niural built its own payroll tax engine, went live with Aetna and Guardian, and grew revenue 287% on the way to a $31 million Series B. If you're a repeat founder, an operator in a regulated industry, or an investor mapping vertical AI, this is your map.

⏱️ Chapters

00:00 — The question that isn't $100 million
00:28 — Why M13 invested in Niural
01:33 — Doing it again: from Twitter to Acorns to Niural
02:46 — Why global payroll is the sleepiest market in software
03:19 — Deel for international, ADP for domestic: the vendor sprawl problem
03:54 — Why AI agents made the full stack buildable
04:30 — Every company in the world could be a Niural client
04:45 — Two and a half years of building before go-to-market
06:32 — What it takes to build a generational company
07:35 — The fastest and sturdiest path to $100 billion in ARR
08:37 — ASO, PEO, EOR: solving payroll end to end
09:41 — AI at the DNA, not a ChatGPT wrapper
10:12 — Aetna, Guardian, Kaiser: building the benefits layer
12:18 — The Chinese bamboo
13:00 — Payroll is one form of money movement
14:52 — ADP, Oracle, and Visa combined
15:39 — From a dial-up connection in Nepal to the Common App
18:52 — Why Niural built its R&D base in Nepal
23:04 — Hard weeks, and what M13 does that capital can't

💸 In this episode

• Why global payroll is one of the sleepiest markets in software—and why sleeping markets give founders a contrarian way to build value
• The case for spending two and a half years building before going to market, and how to pick investors patient enough to wait for it
• Why Niural owns its entire payroll tax engine instead of licensing someone else's, and what that unlocks for money movement across 150 countries
• How ASO, PEO, EOR, contractor payments, and international local entity payroll fit inside one system of record
• What "AI at the DNA" means versus AI bolted on as a ChatGPT wrapper—agents running fraud detection, transaction monitoring, KYB, and state compliance
• Why Niural built direct master plan relationships with Aetna, Guardian, Kaiser, and MetLife instead of reselling benefits
• The Chinese bamboo: four years of invisible root growth, then 90 feet in four weeks
• Why Baral is targeting ADP, Oracle, and Visa combined—a category that doesn't exist yet
• How a midnight dial-up connection in Nepal became a 125-person company with its R&D base there
• What M13 helped with that capital couldn't: brand voice, sales strategy, and standing out in a crowded market

About Niural
Niural is an AI-native global payroll and payments company building a single system of record for payroll, benefits, contractor payments, and cross-border money movement across 150 countries. Learn more at https://niural.com

About M13
M13 invests early in outlier founders building at the structural shifts that create new markets. The Flywheel is where M13 talks with those founders. Learn more at https://m13.co

▶️ Subscribe
Subscribe for more conversations with the founders building at the structural shifts that create new markets, and follow The Flywheel on Spotify and Apple Podcasts.

What if the most overlooked market in tech is the one every investor told you to run from? Polimorphic founder Parth Shah and M13 Managing Partner Latif Peracha explore how local governments are leapfrogging a decade of technology straight into AI and ask a bigger question: can AI reduce bureaucracy without reducing government?

Recorded live at M13's Annual General Meeting, Latif and Parth break down why off-the-shelf models score just 16% accuracy on dense government data while Polimorphic hits 99%, how AI can reduce the “time tax” of bureaucracy, and why the competitive whiteboard stayed almost entirely white. Parth launched Polimorphic in 2021 when investors said they'd rather he "have no idea than work with local government.” They’re in 30 states today, and when governments think AI, they think Polimorphic.

If you're deciding where to build, weighing whether your industry is ready to be disrupted, or studying how companies compound in slow markets, this one’s for you.

⏱️ Chapters

• 00:00 — Intro: the overlooked market hiding in plain sight
• 02:13 — The market every investor told him to avoid
• 05:01 — Government is a customer service organization first
• 05:29 — Running the company from a desk inside town hall
• 06:28 — The product: what Polimorphic actually does
• 08:01 — From 16% to 99% AI accuracy
• 09:04 — Cutting permitting from months to two weeks
• 09:45 — The whiteboard that stayed white: where's the competition?
• 10:21 — How Polimorphic grows: city to county to state
• 13:14 — Pricing per resident and the $200M North Carolina market
• 13:52 — The $1 trillion labor shift hitting local government
• 14:26 — 30 states and the Michigan ERP partnership
• 15:52 — Being a first-time founder
• 17:48 — What a great investor does when things get hard

🏛️ In this episode

• Why out-of-the-box LLMs hit just 16% accuracy on government data—legally dense, multilingual, fifth-grade reading level—and how Polimorphic gets to 99%
• How the “time tax” may be one of AI’s most consequential government use cases: Polimorphic cut permitting from three-plus months to two weeks by helping local officials eliminate the administrative work behind the wait.
• Why one county told its commission that Polimorphic delivers 30% of a department's staff value—work that would otherwise take three new hires
• The compounding growth loop: 20%+ of North Carolina's population covered, a $200M market in that one state at $5–6 per resident
• The macro thesis: state and local government carries a $2.2 trillion labor budget and is set to lose 30–50% of its staff in three to five years—leaving $600B–$1T of work that has to get done
• Why Polimorphic became North Carolina's de facto AI customer service platform, plus a new exclusive partnership with the ERP provider serving 95% of Michigan governments

About Polimorphic
Polimorphic builds AI-powered customer service, CRM, and workflow tools that help local governments serve residents faster—from answering questions to processing permits. Learn more at https://www.polimorphic.com

About M13
M13 invests early in outlier founders building at the structural shifts that create new markets. The Flywheel is where M13 talks with those founders. Learn more at https://m13.co

▶️ Subscribe
Subscribe for more conversations with the founders building at the structural shifts that create new markets, and follow The Flywheel on Spotify and Apple Podcasts.

Why does calling 911 still mean a dispatcher typing your emergency by hand, word for word? Prepared co-founders Mike Chime, Neal Soni and Dylan Gleicher dropped out of Yale to rebuild the emergency call—using AI to transcribe, summarize, and translate in real time across roughly 80 million calls a year.

Prof G Markets cohost Ed Elson sits down with Mike Chime, co-founder and CEO of Prepared, and M13 Managing Partner Karl Alomar to break down what it takes to fix an overlooked problem: why the 911 call is still stuck on decades-old technology, how Prepared now works with about 1,000 agencies and touches roughly 80 million calls a year, and why solving a real problem beats chasing the hot trend. 

Mike—who left Yale on a Thiel Fellowship—explains how the company went from a dorm-room school-safety app to being acquired by Axon. Karl, who scaled DigitalOcean to a $5 billion IPO before joining M13 and wrote Prepared’s first check, explains what made him back college students a year before there was a company. 

If you're a founder, operator, student or engineer weighing whether to leave a safe path and build something that matters, this one's for you.

⏱️ Chapters 

• 00:00 — Why this conversation matters more than ever
• 04:05 — What is Prepared? Fixing the 911 call with AI 
• 06:20 — How Karl found Mike at Yale 
• 09:26 — What made a college kid worth investing in 
• 11:56 — From a dorm-room school-safety app to 911 
• 14:59 — The Thiel Fellowship and burning the boats 
• 16:31 — Should you drop out of college to start a company? 
• 20:02 — "The 911 thing": surviving the skepticism 
• 21:16 — Chasing the hot thing vs. solving a real problem 
• 25:10 — What scaling taught Mike about management 
• 28:12 — Has AI changed how you manage people? 
• 30:43 — Taking risks and betting on yourself 
• 32:21 — Advice for anyone on the fence

🚨 In this episode

• Why the 911 call is still stuck on decades-old tech—dispatchers typing what they hear, and callers who don't speak English waiting five or six minutes for a translator
• How Prepared uses AI to transcribe, summarize, and translate emergencies in real time, now across about 1,000 agencies and roughly 80 million calls a year
• The founder path most people romanticize: dropping out on a Thiel Fellowship, seven years of "the 911 thing," and the skepticism before Axon acquired the company
• Why solving a real problem beats chasing the hot trend—and how that shows up in what you build and who you hire
• Founder mode, management, and why AI changes org structure but not the need for people who believe in the mission

About Prepared 

Prepared helps 911 centers handle emergencies with AI—transcribing and summarizing calls in real time, surfacing what responders need, and translating for callers who don't speak English. Learn more at https://www.prepared911.com

About M13 

M13 invests early in outlier founders building at the structural shifts that create new markets. The Flywheel is where M13 talks with those founders. Learn more at https://m13.co

▶️ Subscribe for more conversations with the founders building at the structural shifts that create new markets, and follow The Flywheel on Spotify and Apple Podcasts.

What if moving $100 million across borders took 60 minutes instead of five days? OpenFX founder Prabhakar Reddy is rebuilding the world's cross-border payment rails to make real-time the default.

On this episode of The Flywheel, recorded live at M13's annual meeting in Montana, M13 Managing Partner Latif Peracha sits down with Prabhakar Reddy, co-founder and CEO of OpenFX. OpenFX is Prabhakar's fifth company. He's been a founder since 19, spent a stint as a VC at Accel, and previously built FalconX into a profitable $8 billion company where he's still a large shareholder. Now he's taking on a $2-trillion-a-day problem: moving money across borders in real time.

Prabhakar breaks down how OpenFX settles 98% of transactions in under 60 minutes, why the real competition is legacy banks and not other fintechs, and how the company grew more than 20x in a year while staying profitable with roughly 100 employees.

In this episode:

• Why cross-border money still takes three to five days—and what that delay costs when a currency drops 5% in a week
• How OpenFX uses stablecoins as invisible infrastructure to move money in minutes, without clients ever touching crypto
• The growth story: from $2 billion to roughly $50 billion in volume in about a year, and the internal target of $1 trillion
• Why the durable moat is solving the whole puzzle—compliance, licensing, market making, collection, and payouts—not just one leg of the trade
• Prabhakar's biggest worry as a CEO: not security or regulation, but staying fast enough to avoid becoming "another slow company"

Chapters: 

• 00:00 — The longest journey to Montana: meet Prabhakar Reddy 
• 01:40 — Fifth company, an $8B exit, and why he keeps going 
• 02:55 — Why cross-border money movement is broken, and what it costs 
• 04:40 — Moving money in minutes: stablecoins as invisible rails 
• 06:50 — 20x in a year: the growth story and the Wise comparison 
• 09:25 — Picking investors as a former VC, and why M13 got the exception 
• 12:25 — The moat: why banks, not fintechs, are the real competition 
• 16:00 — The Amazon-esque model: wholesale FX and 90% cheaper rails 
• 17:55 — What stablecoin regulation could (and couldn't) break 
• 20:05 — From security threats to the fear of becoming a slow company 
• 22:40 — Operating leverage: keeping opex flat while volume explodes

Follow The Flywheel on Spotify or Apple Podcasts for more conversations with the founders building at the structural shifts that create new markets. Learn more about OpenFX at openfx.com and M13 at m13.co.

What makes an AI voice worth talking to? Rime founder Lily Clifford thinks the next breakthrough in voice AI isn't a more natural-sounding voice—it's a conversation you actually want to keep having.

On this episode of The Flywheel, M13 Partner Morgan Blumberg sits down with Lily Clifford, co-founder and CEO of Rime. Before Rime, Lily was earning a linguistics PhD at Stanford, researching the acoustic physics of how humans produce speech. Lily’s since built Rime into voice AI that powers roughly 100 million phone interactions a month across healthcare and financial services, and has a clear, differentiated view of where voice goes next.

Lily breaks down why the next wave isn't a smoother-sounding voice, but conversations people don't want to hang up on and what that means for anyone building with voice right now.

In this episode:

• Why "quality of interaction" is a far higher bar than natural-sounding audio—and why there's no margin for error when someone expects a human
• What an independent study of roughly 100,000 calls revealed about why people stay on the phone longer with Rime than with Eleven Labs or Google
• Lily's take on why you can't actually build on today's most impressive frontier voice models
• Why enterprises will start building their own voice stacks—and the building blocks that are still missing
• Why a linguistics-first team treats "impossible" problems (like how to pronounce the word "live") as the whole point

Chapters: 

• 00:00 — Why "natural" isn't the bar for voice AI 
• 01:00 — 25th Street Recording: where Rime made its first recording 
• 04:00 — Quality of interaction vs. quality of voice 
• 08:29 — The Miravoice study: ~100,000 calls and the 10-second effect 
• 12:12 — Build vs. buy: why frontier voice models aren't buildable 
• 16:13 — The building-blocks problem and the enterprise "game of telephone" 
• 21:40 — Will enterprises build voice in-house? The SaaS parallel 
• 26:55 — A linguistics-first team, and leaving a Stanford PhD 
• 36:23 — Why chase an "impossible" problem 
• 41:00 — Language, vowels, and what makes us human 
• 46:15 — What keeps Lily going

Follow The Flywheel on Spotify or Apple Podcasts for more conversations with the founders building at AI's frontier. 

Learn more about Rime at rime.ai and M13 at m13.co.

What comes after large language models? Skyfall AI founder Sam Pasupalak thinks the next breakthrough is AI that can run a business autonomously. 

On this episode of The Flywheel, M13 Partner Morgan Blumberg sits down with Sam Pasupalak, co-founder and CEO of Skyfall AI. Before Skyfall, Sam co-founded Maluuba, one of the first deep-learning labs for natural language understanding, acquired by Microsoft in 2017. He's spent 15+ years at the frontier of AI and he has a clear, differentiated view of where it goes next.

If you're a founder, operator, or engineer trying to understand the future of AI after the LLM era, this is your map. Sam breaks down why the next wave isn't smarter co-pilots, but autonomous businesses—and what that means for anyone building right now.

In this episode:

• What "world models" and continual learning are, and why Sam thinks they are the real breakthrough
• Sam’s hot take on why so many AI "wrapper" companies will struggle to survive
• How early we actually are in the AI cycle (think Yahoo and AOL, before Google and Amazon)
• Why 20 million software engineers and a billion white-collar workers signal a shift bigger than the Industrial Revolution
• The founder traits it takes to build through a platform shift—and the sacrifice Sam says it demands

Chapters:
00:00 — The future isn't AI co-pilots
00:40 — From Maluuba to Skyfall: who is Sam Pasupalak?
07:45 — Why Skyfall takes a different path than the LLM wrappers
09:50 — World models and continual learning, explained
19:17 — The coffee-shop test: why an LLM can't run a business
22:47 — The hot take: why AI wrapper companies will struggle
24:21 — How early we are: the Yahoos and AOLs vs. the Googles and Amazons
38:13 — Founder traits for this era: build a research-first company
39:46 — Hot takes on OpenAI, Anthropic, Altman and Amodei
48:43 — Being both a warrior and a monk

Follow The Flywheel on Spotify or Apple Podcasts for more conversations with the founders building at AI's frontier. Learn more about Skyfall AI at skyfall.ai and M13 at m13.co.