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Swan AI founder Amos Bar-Joseph breaks down why ARR per employee is replacing traditional valuation metrics in an AI-native world. "Optimizing for valuation deviates you from the right path and makes you optimize for investors rather than building the right company." "The right metric to optimize for in an...

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13,767 views • 9 days ago •via X (Twitter)

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"Internally, we don't set ARR metrics." - Replit CEO Amjad Masad Amjad says focusing on ARR is "optimizing for the wrong thing." Instead, they look at metrics like sentiment and how much money their customers are making: "In the age of AI, I don't think about ARR the same way a SaaS business does, because you're selling tokens. You can push a bug and get better ARR because [customers] are spending more tokens." "We're increasingly looking at how much money people are generating when they're building a business with Replit, because now you can hook in Stripe and other payment providers." "When people get angry, they'll tell the agents they're angry. We look at that a lot." "It's really about what you measure. We're trying to measure the quality of the output, as opposed to whatever ARR metric there is."
1:16

"Internally, we don't set ARR metrics." - Replit CEO Amjad Masad Amjad says focusing on ARR is "optimizing for the wrong thing." Instead, they look at metrics like sentiment and how much money their customers are making: "In the age of AI, I don't think about ARR the same way a SaaS business does, because you're selling tokens. You can push a bug and get better ARR because [customers] are spending more tokens." "We're increasingly looking at how much money people are generating when they're building a business with Replit, because now you can hook in Stripe and other payment providers." "When people get angry, they'll tell the agents they're angry. We look at that a lot." "It's really about what you measure. We're trying to measure the quality of the output, as opposed to whatever ARR metric there is."

TBPN

48,232 views • 6 months ago

AI this AI that, Listen to what an AI expert Ansh Mehra has to say on this who teaches AI across the world. “Even the best AI Diffusion Models in the world cannot produce consistent videos in 4K for more than 30 Seconds and this movie is for IMAX, so it’s not possible, you are confusing CGI for AI.” Anything is Running on the Internet Right now.
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AI this AI that, Listen to what an AI expert Ansh Mehra has to say on this who teaches AI across the world. “Even the best AI Diffusion Models in the world cannot produce consistent videos in 4K for more than 30 Seconds and this movie is for IMAX, so it’s not possible, you are confusing CGI for AI.” Anything is Running on the Internet Right now.

KBP Reviews🏹

29,692 views • 1 month ago

$CRWD earnings today are really about whether CrowdStrike is monetizing AI security fast enough to justify a valuation ~100x FCF. The opportunity is clearly expanding with AIDR ARR growing 250% sequentially and pipeline topping $50M but market already understands that story. CrowdStrike has a VERY HIGH bar to clear and now needs to prove the AI security ceiling is rising faster than the multiple investors are already paying for it.
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$CRWD earnings today are really about whether CrowdStrike is monetizing AI security fast enough to justify a valuation ~100x FCF. The opportunity is clearly expanding with AIDR ARR growing 250% sequentially and pipeline topping $50M but market already understands that story. CrowdStrike has a VERY HIGH bar to clear and now needs to prove the AI security ceiling is rising faster than the multiple investors are already paying for it.

Shay Boloor

49,204 views • 19 days ago

$MSFT is trading at a decade-low valuation because the market is focused on the near-term cost of AI while underpricing Microsoft’s role as one of the most important enterprise AI platforms in the world. The bigger shift is that Microsoft may move from only monetizing users to monetizing work where companies eventually pay not just for employee licenses but for the AI work being performed around each employee. That creates a ton of upside if Microsoft can monetize agents, tokens, workflows, security, governance and productivity across the enterprise stack in the new AI economy.
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$MSFT is trading at a decade-low valuation because the market is focused on the near-term cost of AI while underpricing Microsoft’s role as one of the most important enterprise AI platforms in the world. The bigger shift is that Microsoft may move from only monetizing users to monetizing work where companies eventually pay not just for employee licenses but for the AI work being performed around each employee. That creates a ton of upside if Microsoft can monetize agents, tokens, workflows, security, governance and productivity across the enterprise stack in the new AI economy.

Shay Boloor

607,101 views • 2 months ago

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch
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What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

GREG ISENBERG

84,760 views • 3 months ago

⬆️He is a child prodigy from Kerala. He travels round the earth, giving lectures to schools, business forums and many other platforms as to how to use AI and master its skills. . He is an official advisor to the Kerala government and Dubai Government to building AI platforms for them in different fields. He runs a company, wherein his father is his employee. In whatever field you are, just sharpen the AI skills for retaining or upgrading one's skills in employment and be an employee who cannot be sacked by an employer for redundancy by boosting one's efficiency manifold.
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⬆️He is a child prodigy from Kerala. He travels round the earth, giving lectures to schools, business forums and many other platforms as to how to use AI and master its skills. . He is an official advisor to the Kerala government and Dubai Government to building AI platforms for them in different fields. He runs a company, wherein his father is his employee. In whatever field you are, just sharpen the AI skills for retaining or upgrading one's skills in employment and be an employee who cannot be sacked by an employer for redundancy by boosting one's efficiency manifold.

Pankaj Parekh

64,061 views • 1 year ago

"Imposter syndrome is fake. You either worked for it or you didn’t" Raw and honest conversation with: Kathryn Cross🤰🫄 (Anja Health, YC-backed and 776 Backed), over 4M raised, 1M+ ARR rajya (Coral AI, bootstrapped), Built to $1M+ ARR without raising a single dollar Highlights from the conversation: - Bootstrapped to $1M ARR vs. raising millions—which path is actually better? - The "have you raised?" question became a way to instantly dismiss bootstrapped founders, even when they had more revenue than VC-backed companies - Building outside SF kept them connected to real consumers ...and much more Full conversation in comments!
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luba yudasina

14,805 views • 8 months ago

a16z's David George says the real economy has barely begun adopting AI, while the top 1% in one dataset already spend $7,000 per employee per month: "Part of the thing that we're monitoring, which makes us extremely bullish about AI, is just actual diffusion into the real economy." "The median company in the US is spending $12 per employee on AI per month. The top 1% of the dataset that we've seen is spending $7,000 per employee on AI per month." "Not only have we had limited diffusion beyond coding, but if you just look at the shape of who is consuming tokens and actually getting real value out of AI today, we're super early. The most cutting-edge banks are probably doing 1% of headcount cost on AI tools." "The reason this makes me very bullish is these are the fastest-growing companies we've ever seen, of all time. They're adding more revenue per month than the mega-cap tech companies." "And yet it's probably on the back of adoption of 10 million users, maybe 20, maybe 30 max. And I think it's going to transform the way we do a lot of work." David George
1:36

a16z's David George says the real economy has barely begun adopting AI, while the top 1% in one dataset already spend $7,000 per employee per month: "Part of the thing that we're monitoring, which makes us extremely bullish about AI, is just actual diffusion into the real economy." "The median company in the US is spending $12 per employee on AI per month. The top 1% of the dataset that we've seen is spending $7,000 per employee on AI per month." "Not only have we had limited diffusion beyond coding, but if you just look at the shape of who is consuming tokens and actually getting real value out of AI today, we're super early. The most cutting-edge banks are probably doing 1% of headcount cost on AI tools." "The reason this makes me very bullish is these are the fastest-growing companies we've ever seen, of all time. They're adding more revenue per month than the mega-cap tech companies." "And yet it's probably on the back of adoption of 10 million users, maybe 20, maybe 30 max. And I think it's going to transform the way we do a lot of work." David George

a16z

108,794 views • 3 days ago

This is crazy... The first AI-native platform that builds, optimizes, and scales your e-commerce business is here. Runner AI takes you from one prompt to a fully functional, self-optimizing store in minutes. Here's how it works:
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This is crazy... The first AI-native platform that builds, optimizes, and scales your e-commerce business is here. Runner AI takes you from one prompt to a fully functional, self-optimizing store in minutes. Here's how it works:

Angry Tom

12,268 views • 6 months ago

In an interview with the Wall Street Journal, $DUOL CTO Severin Hacker speaks about how Duolingo is becoming an AI native company. To him, the fundamental question is: "what can you build now, with AI, that you couldn't previously build?" For Duolingo this is the Lily AI tutor.
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In an interview with the Wall Street Journal, $DUOL CTO Severin Hacker speaks about how Duolingo is becoming an AI native company. To him, the fundamental question is: "what can you build now, with AI, that you couldn't previously build?" For Duolingo this is the Lily AI tutor.

Nikolias Goninus

16,378 views • 7 months ago

Mark Cuban said what every CEO in America needs to hear. "There are only two types of companies in this world, those who are great at AI, and everybody else. If you don't know AI, you are going to fail. Period, end of story." Mark Cuban didn't stop there. "Whether you are an employee, you're going to have to understand how AI impacts your job and how you can use it to be better at your job. Same if you're a student. And if you're a CEO, you can't just say, okay, I'm going to get my tech guys to understand it and educate me on it. You have to understand it yourself because it will have significant impact on every single thing that you do. There's no avoiding it." This is coming from someone who built and sold the Dallas Mavericks, who made his first fortune selling a company to Yahoo for $5.7 billion in 1999, and who has spent the last two years going deeper on AI than almost any investor his age. He is describing the Innovator's AI Dilemma, entrepreneurs are right now building AI-native companies designed to displace every major incumbent. If a CEO tears down their company to rebuild it AI native, investors revolt but if they do nothing, AI-native startups eat their market and investors revolt anyway. Either path leads to shareholder lawsuits and there is no comfortable middle. The companies that survive will be the ones where leadership, not just the tech team, genuinely understands what the technology can do. The ones that don't will look back at this moment the same way Blockbuster looks at 2005.
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Mark Cuban said what every CEO in America needs to hear. "There are only two types of companies in this world, those who are great at AI, and everybody else. If you don't know AI, you are going to fail. Period, end of story." Mark Cuban didn't stop there. "Whether you are an employee, you're going to have to understand how AI impacts your job and how you can use it to be better at your job. Same if you're a student. And if you're a CEO, you can't just say, okay, I'm going to get my tech guys to understand it and educate me on it. You have to understand it yourself because it will have significant impact on every single thing that you do. There's no avoiding it." This is coming from someone who built and sold the Dallas Mavericks, who made his first fortune selling a company to Yahoo for $5.7 billion in 1999, and who has spent the last two years going deeper on AI than almost any investor his age. He is describing the Innovator's AI Dilemma, entrepreneurs are right now building AI-native companies designed to displace every major incumbent. If a CEO tears down their company to rebuild it AI native, investors revolt but if they do nothing, AI-native startups eat their market and investors revolt anyway. Either path leads to shareholder lawsuits and there is no comfortable middle. The companies that survive will be the ones where leadership, not just the tech team, genuinely understands what the technology can do. The ones that don't will look back at this moment the same way Blockbuster looks at 2005.

StockMarket.News

163,468 views • 4 months ago

We’ve raised $320M at an $11B valuation, led by Addition. AI is changing how companies are built. Teams are smaller, global from day one, and using agents more. We’ve spent 10 years building the financial rails for that world. We’re now building the intelligent layer on top.
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We’ve raised $320M at an $11B valuation, led by Addition. AI is changing how companies are built. Teams are smaller, global from day one, and using agents more. We’ve spent 10 years building the financial rails for that world. We’re now building the intelligent layer on top.

Jack Zhang

2,135,093 views • 2 months ago

.Intercom was on the brink of death—five straight quarters of $0 net new ARR, approaching negative growth. Then they went all‑in on AI. 6 weeks after GPT‑3.5 launched, they shipped a working prototype of Fin (Intercom’s AI agent). Fin grew from $1M → $12M ARR in 12 months. Now at mid–8 figures and >300% growth, it's on track to cross $100M ARR in less than 3 quarters, and will be growing faster than every public software company by year-end. In our real-talk conversation, Eoghan McCabe shares the brutal reality of transforming a late-stage, plateauing SaaS business into an AI-first company. What you'll learn: 🔸 Why Eoghan believes most late-stage companies won’t survive the AI transition 🔸 The “founder mode” transformation that required firing 40% of staff and resulted in 98% employee satisfaction 🔸 Why having “nothing to lose” is the ultimate advantage in AI transformation 🔸 How they transformed from a plateauing SaaS business to an AI-first company growing at 300%+ 🔸 How Intercom’s pricing evolved from “the most hated in SaaS” to a model for every AI company 🔸 How 12 years of therapy and a period of “ego death” shaped Eoghan’s leadership approach Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Great Question — Empower everyone to run great research: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 DX — The developer intelligence platform designed by leading researchers:
1:11

.Intercom was on the brink of death—five straight quarters of $0 net new ARR, approaching negative growth. Then they went all‑in on AI. 6 weeks after GPT‑3.5 launched, they shipped a working prototype of Fin (Intercom’s AI agent). Fin grew from $1M → $12M ARR in 12 months. Now at mid–8 figures and >300% growth, it's on track to cross $100M ARR in less than 3 quarters, and will be growing faster than every public software company by year-end. In our real-talk conversation, Eoghan McCabe shares the brutal reality of transforming a late-stage, plateauing SaaS business into an AI-first company. What you'll learn: 🔸 Why Eoghan believes most late-stage companies won’t survive the AI transition 🔸 The “founder mode” transformation that required firing 40% of staff and resulted in 98% employee satisfaction 🔸 Why having “nothing to lose” is the ultimate advantage in AI transformation 🔸 How they transformed from a plateauing SaaS business to an AI-first company growing at 300%+ 🔸 How Intercom’s pricing evolved from “the most hated in SaaS” to a model for every AI company 🔸 How 12 years of therapy and a period of “ego death” shaped Eoghan’s leadership approach Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Great Question — Empower everyone to run great research: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 DX — The developer intelligence platform designed by leading researchers:

Lenny Rachitsky

182,396 views • 1 year ago

.Surface Labs is building the AI workforce for Marketing Ops. Surface converts 30% more leads with agents that prevent ad fraud, optimize forms, and route leads to sales. Their agents now manage 50k+ leads per month. Congrats on the launch & Arjun Sahai!
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.Surface Labs is building the AI workforce for Marketing Ops. Surface converts 30% more leads with agents that prevent ad fraud, optimize forms, and route leads to sales. Their agents now manage 50k+ leads per month. Congrats on the launch & Arjun Sahai!

Y Combinator

106,429 views • 1 year ago

Podcasts that celebrate companies scaling from $0 to $50M ARR rarely interrogate *WHAT* internally enabled that hypergrowth. So I did. Genspark hit $50M ARR in <5 months and recently raised at $1.25B valuation led by Joseph Floyd at Emergence Capital In this episode with cofounder/COO Wen Sang: • Why they killed AI search at 5M users • How their agents ship finished work, not drafts • What breaks internally when you grow this fast This episode is a playbook on speed. Link in comments 👇
1:07

Podcasts that celebrate companies scaling from $0 to $50M ARR rarely interrogate *WHAT* internally enabled that hypergrowth. So I did. Genspark hit $50M ARR in <5 months and recently raised at $1.25B valuation led by Joseph Floyd at Emergence Capital In this episode with cofounder/COO Wen Sang: • Why they killed AI search at 5M users • How their agents ship finished work, not drafts • What breaks internally when you grow this fast This episode is a playbook on speed. Link in comments 👇

Jay Kapoor

108,413 views • 9 months ago

Meta's chief AI officer says a swarm of agents out-produced a team of 100 engineers, and the machinery behind it is markdown files and cron jobs: "Like I think we've seen internally in Meta cases where if you can develop the right agentic loop and you have the right eval or the right metric for the agents to optimize, you can have a swarm of agents accomplish more than like a team of 100 engineers." "Very, very handily. Actually, very, very easily." "And so I think figuring out what the world looks like with lots of these agentic coordination problems, I think that is one of the most interesting problems today." "It just comes down to skills, markdown files, cron jobs." "I think it's always funny how mundane everything is once you really dig into it." If he is right, writing the metric now matters more than writing the code, and one judgment call about that metric stands in for 100 engineers' worth of output. Wang says Meta has already run this internally. - Alexandr Wang (Alexandr Wang), Chief AI Officer at Meta, with Garry Tan (Garry Tan) at Y Combinator (Y Combinator) Startup School 2026.
1:02

Meta's chief AI officer says a swarm of agents out-produced a team of 100 engineers, and the machinery behind it is markdown files and cron jobs: "Like I think we've seen internally in Meta cases where if you can develop the right agentic loop and you have the right eval or the right metric for the agents to optimize, you can have a swarm of agents accomplish more than like a team of 100 engineers." "Very, very handily. Actually, very, very easily." "And so I think figuring out what the world looks like with lots of these agentic coordination problems, I think that is one of the most interesting problems today." "It just comes down to skills, markdown files, cron jobs." "I think it's always funny how mundane everything is once you really dig into it." If he is right, writing the metric now matters more than writing the code, and one judgment call about that metric stands in for 100 engineers' worth of output. Wang says Meta has already run this internally. - Alexandr Wang (Alexandr Wang), Chief AI Officer at Meta, with Garry Tan (Garry Tan) at Y Combinator (Y Combinator) Startup School 2026.

Karl Mehta

26,779 views • 1 month ago

Elon Musk in a new SpaceX employee meeting on why it’s important for Grok to succeed: "We gotta win here on the AI hardware and AI software. That is the most important message I wanted to convey today; is that we must win on AI, because the future is overwhelmingly AI and Robots. We won’t ultimately be able to control the AI, it’ll be too smart for that. But just like if you have a super genius child, you can still instill in that child the values and beliefs that you think are good and right. So that’s why it’s incredibly important that we succeed with Grok." Very strong message.
0:48

Elon Musk in a new SpaceX employee meeting on why it’s important for Grok to succeed: "We gotta win here on the AI hardware and AI software. That is the most important message I wanted to convey today; is that we must win on AI, because the future is overwhelmingly AI and Robots. We won’t ultimately be able to control the AI, it’ll be too smart for that. But just like if you have a super genius child, you can still instill in that child the values and beliefs that you think are good and right. So that’s why it’s incredibly important that we succeed with Grok." Very strong message.

Nic Cruz Patane

156,857 views • 1 month ago

🎙️ Want a full breakdown on why cost per token is a key metric for AI infrastructure TCO? We’re operating in a power-constrained world. Demand for intelligence is scaling faster than infrastructure can keep up—so metrics like cost per GPU hour or FLOPs per dollar don’t tell the full story. When it comes to TCO, Cost per Token is the metric that captures end-to-end performance across GPUs, CPUs, networking, software, and ecosystem making it the key driver of real profitability and scalability in AI. If you’re not measuring efficiency at the token level, you’re missing what actually drives cost. Watch the deep-dive conversation with Dr. Gerro Prinsloo , Nader Khalil and Carter Abdallah, live on the NVIDIA YouTube channel.
1:05

🎙️ Want a full breakdown on why cost per token is a key metric for AI infrastructure TCO? We’re operating in a power-constrained world. Demand for intelligence is scaling faster than infrastructure can keep up—so metrics like cost per GPU hour or FLOPs per dollar don’t tell the full story. When it comes to TCO, Cost per Token is the metric that captures end-to-end performance across GPUs, CPUs, networking, software, and ecosystem making it the key driver of real profitability and scalability in AI. If you’re not measuring efficiency at the token level, you’re missing what actually drives cost. Watch the deep-dive conversation with Dr. Gerro Prinsloo , Nader Khalil and Carter Abdallah, live on the NVIDIA YouTube channel.

NVIDIA

21,970 views • 5 months ago

Wix at 1x ARR is not a SaaS bloodbath. It is a reckoning for businesses that were always just sales and marketing operations dressed up as software. 1. The client-server to SaaS shift was more disruptive than anything AI is doing to software right now. This is incremental. The market is finally pricing it that way. 2. SaaS was never that hard to spin up. When the moat is a sales team and a logo, the multiple compresses. That is not a surprise, that is gravity. 3. The companies that survive this are the ones with complex customer relationships and data sets that cannot be recreated. Wix does not have that. Base44 might not either. 4. Vibe coding tools doing $170M ARR getting priced at $2.1BN is the same mistake in reverse. Getting real workflow change adopted at scale takes longer than the ARR curve suggests. 5. The best software companies right now are not pure software companies. The ones with physical world integration and proprietary data loops are harder to replicate and harder to compress. 6. Everyone who raised at 20x ARR on a sales-led motion took on leverage they did not call leverage. It cuts just as hard on the way down. The bloodbath is not random. It is selection pressure doing exactly what it is supposed to do. Will Quist
1:22

Wix at 1x ARR is not a SaaS bloodbath. It is a reckoning for businesses that were always just sales and marketing operations dressed up as software. 1. The client-server to SaaS shift was more disruptive than anything AI is doing to software right now. This is incremental. The market is finally pricing it that way. 2. SaaS was never that hard to spin up. When the moat is a sales team and a logo, the multiple compresses. That is not a surprise, that is gravity. 3. The companies that survive this are the ones with complex customer relationships and data sets that cannot be recreated. Wix does not have that. Base44 might not either. 4. Vibe coding tools doing $170M ARR getting priced at $2.1BN is the same mistake in reverse. Getting real workflow change adopted at scale takes longer than the ARR curve suggests. 5. The best software companies right now are not pure software companies. The ones with physical world integration and proprietary data loops are harder to replicate and harder to compress. 6. Everyone who raised at 20x ARR on a sales-led motion took on leverage they did not call leverage. It cuts just as hard on the way down. The bloodbath is not random. It is selection pressure doing exactly what it is supposed to do. Will Quist

Slow Ventures

10,068 views • 2 months ago