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I built this CEO dashboard for my client. And it changed how they run their entire business. 1/ Before this dashboard, they were making decisions in the dark. Revenue, profit, and marketing spend scattered across spreadsheets and platforms. - No single source of truth. - No real-time profit and...

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Lian Lim | Dashboard & AI Automation Expert

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Morgan Stanley just raised their 2027 AI capex forecast to $1.1 trillion and that number still doesn't include SpaceX or a lot of the other AI companies (Save this). When you factor those in, the real 2027 figure is probably closer to $1.5 trillion and AI lab inference revenue combined is tracking toward $300 billion in 2027. On its surface that ratio sounds alarming, spending $1.5 trillion in capex to generate $300 billion in revenue. But the framing collapses the moment you examine two things the bears consistently ignore, gross margins and the revenue trajectory. Gross margins on inference revenue are running at 60 to 70 percent. That means the $300 billion in inference revenue generates $180 to $210 billion in gross profit and that number compounds rapidly as utilization scales on infrastructure that is already built and paid for. The Capex is not being deployed against today's revenue but rather being deployed against a revenue trajectory that has shown no signs of decelerating. To understand how aggressive that trajectory actually is, consider that Morgan Stanley's $1.1 trillion hyperscaler forecast is nearly double what analysts projected for the same year just twelve months ago And they described the demand as inelastic, meaning it is not slowing down regardless of rising costs, tighter financing conditions or geopolitical risk. The AI industry ended 2025 tracking well over $200 billion in combined inference revenue and the growth rate since then has continued to accelerate rather than flatten. Anthropic alone scaled from negligible revenue to a $30 billion annualized run rate in approximately 18 months while OpenAI is tracking toward $280 billion in annual revenue by 2030 from $13 billion in 2025. There is also a structural reality in the capex number that the bears never account for. Roughly 35 percent of total AI spending goes toward training, building the next model generation which is not revenue-generating in the current period. That means only about 65 percent of the $1.5 trillion in capex is actually deployed against the inference infrastructure that earns revenue today. When you apply the 60 to 70 percent gross margin to the revenue that sits on top of that 65 percent figure, the economics look substantially better than the headline capex to revenue ratio implies. Every CEO who has been closest to this buildout has consistently underestimated it and Jensen Huang projected $1 trillion in AI capex two years ago and was called delusional. Dario Amodei said in early 2026 that AI revenues would reach the low hundreds of billions by 2028 and trillions before 2030 and given where Anthropic's own revenue trajectory is today, he is likely revising those numbers upward. The pattern here is consistent, every time someone models the revenue ceiling, the actual number breaks through it faster than expected. Come join Milk Road Pro for our full breakdown, the real unit economics of the AI inference buildout, how the capex to revenue ratio evolves over the next three years, and our entire AI thesis! Link below!
4:04

Morgan Stanley just raised their 2027 AI capex forecast to $1.1 trillion and that number still doesn't include SpaceX or a lot of the other AI companies (Save this). When you factor those in, the real 2027 figure is probably closer to $1.5 trillion and AI lab inference revenue combined is tracking toward $300 billion in 2027. On its surface that ratio sounds alarming, spending $1.5 trillion in capex to generate $300 billion in revenue. But the framing collapses the moment you examine two things the bears consistently ignore, gross margins and the revenue trajectory. Gross margins on inference revenue are running at 60 to 70 percent. That means the $300 billion in inference revenue generates $180 to $210 billion in gross profit and that number compounds rapidly as utilization scales on infrastructure that is already built and paid for. The Capex is not being deployed against today's revenue but rather being deployed against a revenue trajectory that has shown no signs of decelerating. To understand how aggressive that trajectory actually is, consider that Morgan Stanley's $1.1 trillion hyperscaler forecast is nearly double what analysts projected for the same year just twelve months ago And they described the demand as inelastic, meaning it is not slowing down regardless of rising costs, tighter financing conditions or geopolitical risk. The AI industry ended 2025 tracking well over $200 billion in combined inference revenue and the growth rate since then has continued to accelerate rather than flatten. Anthropic alone scaled from negligible revenue to a $30 billion annualized run rate in approximately 18 months while OpenAI is tracking toward $280 billion in annual revenue by 2030 from $13 billion in 2025. There is also a structural reality in the capex number that the bears never account for. Roughly 35 percent of total AI spending goes toward training, building the next model generation which is not revenue-generating in the current period. That means only about 65 percent of the $1.5 trillion in capex is actually deployed against the inference infrastructure that earns revenue today. When you apply the 60 to 70 percent gross margin to the revenue that sits on top of that 65 percent figure, the economics look substantially better than the headline capex to revenue ratio implies. Every CEO who has been closest to this buildout has consistently underestimated it and Jensen Huang projected $1 trillion in AI capex two years ago and was called delusional. Dario Amodei said in early 2026 that AI revenues would reach the low hundreds of billions by 2028 and trillions before 2030 and given where Anthropic's own revenue trajectory is today, he is likely revising those numbers upward. The pattern here is consistent, every time someone models the revenue ceiling, the actual number breaks through it faster than expected. Come join Milk Road Pro for our full breakdown, the real unit economics of the AI inference buildout, how the capex to revenue ratio evolves over the next three years, and our entire AI thesis! Link below!

Milk Road AI

21,141 görüntüleme • 1 ay önce

THIS IS ABSOLUTELY RIDICULOUS. OpenAI and Anthropic are losing money on every dollar they make. OpenAI generated $20 billion in revenue in 2025 and is projected to lose $14 billion in the same year. Internal forecasts project cumulative losses hitting $44 billion by 2028. The company's own CFO warned executives in April 2026 that OpenAI might struggle to finance upcoming computing deals if revenue growth slows. Anthropic reached $4.3 billion in annualized revenue in April 2026 against $19 billion in total costs. It spends $3 to make $1, and is not expected to stop burning cash until 2027. Now look at what these two companies have committed to spend. OpenAI and Anthropic together have committed $1.05 trillion in cloud spending to Microsoft, Oracle, Google and Amazon, making up 43 to 54% of each provider's entire future revenue backlog. - Microsoft: $627B total backlog. OpenAI and Anthropic account for 49%. - Oracle: $553B total backlog. OpenAI alone accounts for 54%. - Google: $467.6B total backlog. Anthropic accounts for 43%. - Amazon: $464B total backlog. OpenAI and Anthropic account for 51%. The entire cloud industry's future revenue is a bet on two companies losing billions every quarter. Microsoft, Alphabet, Meta and Amazon are collectively expected to spend $725 billion in capex in 2026, almost entirely on AI infrastructure. Combined hyperscaler capex from 2025 to 2027 is projected at $1.15 trillion, more than double what was spent from 2022 to 2024. What is the return on all of this? McKinsey's 2025 State of AI survey found that only a minority of companies reported AI meaningfully increased revenue or reduced costs. Enterprise generative AI spending grew from $1.7 billion in 2023 to $37 billion in 2025 and most CIOs still describe their initiatives as pilots without clear ROI metrics. Microsoft's AI business is running at a $37 billion annual revenue run rate with 123% year over year growth. That sounds impressive until you realize most of the capex funding is justified by expected future AI revenue rather than current AI profit. The internet burned money for years before it became the most profitable industry in history. But right now $1 trillion in committed cloud spend, $725 billion in annual capex, two loss-making customers making up half of every major cloud provider's revenue backlog, and the enterprises writing the checks cannot tell you if any of it is working.
0:22

THIS IS ABSOLUTELY RIDICULOUS. OpenAI and Anthropic are losing money on every dollar they make. OpenAI generated $20 billion in revenue in 2025 and is projected to lose $14 billion in the same year. Internal forecasts project cumulative losses hitting $44 billion by 2028. The company's own CFO warned executives in April 2026 that OpenAI might struggle to finance upcoming computing deals if revenue growth slows. Anthropic reached $4.3 billion in annualized revenue in April 2026 against $19 billion in total costs. It spends $3 to make $1, and is not expected to stop burning cash until 2027. Now look at what these two companies have committed to spend. OpenAI and Anthropic together have committed $1.05 trillion in cloud spending to Microsoft, Oracle, Google and Amazon, making up 43 to 54% of each provider's entire future revenue backlog. - Microsoft: $627B total backlog. OpenAI and Anthropic account for 49%. - Oracle: $553B total backlog. OpenAI alone accounts for 54%. - Google: $467.6B total backlog. Anthropic accounts for 43%. - Amazon: $464B total backlog. OpenAI and Anthropic account for 51%. The entire cloud industry's future revenue is a bet on two companies losing billions every quarter. Microsoft, Alphabet, Meta and Amazon are collectively expected to spend $725 billion in capex in 2026, almost entirely on AI infrastructure. Combined hyperscaler capex from 2025 to 2027 is projected at $1.15 trillion, more than double what was spent from 2022 to 2024. What is the return on all of this? McKinsey's 2025 State of AI survey found that only a minority of companies reported AI meaningfully increased revenue or reduced costs. Enterprise generative AI spending grew from $1.7 billion in 2023 to $37 billion in 2025 and most CIOs still describe their initiatives as pilots without clear ROI metrics. Microsoft's AI business is running at a $37 billion annual revenue run rate with 123% year over year growth. That sounds impressive until you realize most of the capex funding is justified by expected future AI revenue rather than current AI profit. The internet burned money for years before it became the most profitable industry in history. But right now $1 trillion in committed cloud spend, $725 billion in annual capex, two loss-making customers making up half of every major cloud provider's revenue backlog, and the enterprises writing the checks cannot tell you if any of it is working.

Crypto Rover

58,862 görüntüleme • 1 ay önce

I wanted to see how far a single sentence could take a real business idea. I opened Replit and typed: “Add a clean subscription system so users can unlock a premium version of my app.” Replit took over from there. It connected Stripe automatically, built the entire payment flow, created the sandbox, set up the data models, and produced a working checkout without me touching a single dashboard. I ran a test payment and watched the customer record appear instantly inside my app. It felt like skipping every complicated step that usually blocks people from launching anything paid. I refined the copy in plain English, adjusted the screens, and shaped the experience as if I were sculpting a small SaaS in real time. No setup. No configuration. Just describing the business and watching it become something people could actually pay for. If you have been sitting on an idea that deserves a revenue stream, give Replit one honest sentence and see how far it goes. → Replit ⠕
0:38

I wanted to see how far a single sentence could take a real business idea. I opened Replit and typed: “Add a clean subscription system so users can unlock a premium version of my app.” Replit took over from there. It connected Stripe automatically, built the entire payment flow, created the sandbox, set up the data models, and produced a working checkout without me touching a single dashboard. I ran a test payment and watched the customer record appear instantly inside my app. It felt like skipping every complicated step that usually blocks people from launching anything paid. I refined the copy in plain English, adjusted the screens, and shaped the experience as if I were sculpting a small SaaS in real time. No setup. No configuration. Just describing the business and watching it become something people could actually pay for. If you have been sitting on an idea that deserves a revenue stream, give Replit one honest sentence and see how far it goes. → Replit ⠕

Parul Gautam

139,549 görüntüleme • 7 ay önce

Priority Fees tracking is live at Priority fees are HYPE burned to move faster on Hyperliquid. Two separate burns: write priority, where you pay for a taker order to be filled sooner, and read priority, an auction for faster market-data reads. Both fully tracked. What you can track: - Priority fees as a share of all Hyperliquid revenue, and how that share is growing over time - Which markets pay the most priority, and where it rivals or beats the trading fee itself - Daily HYPE burned for priority since launch, in dollars and HYPE, split write vs read - An annualized run-rate you can model yourself, with sliders for priority growth and HYPE price - The wallets paying the most, 24h and all-time, and how concentrated the spend is - The live read auction in real time, and who is winning it right now - Cost per fill, total fills, and the user base growing over time Hyperliquid.
0:31

Priority Fees tracking is live at Priority fees are HYPE burned to move faster on Hyperliquid. Two separate burns: write priority, where you pay for a taker order to be filled sooner, and read priority, an auction for faster market-data reads. Both fully tracked. What you can track: - Priority fees as a share of all Hyperliquid revenue, and how that share is growing over time - Which markets pay the most priority, and where it rivals or beats the trading fee itself - Daily HYPE burned for priority since launch, in dollars and HYPE, split write vs read - An annualized run-rate you can model yourself, with sliders for priority growth and HYPE price - The wallets paying the most, 24h and all-time, and how concentrated the spend is - The live read auction in real time, and who is winning it right now - Cost per fill, total fills, and the user base growing over time Hyperliquid.

HL Eco

18,898 görüntüleme • 18 gün önce

David Friedberg: The AI Jobs Panic Is a Crock of Sh*t Why? The revenue potential outweighs the cost savings by 100x. “There is no job loss with AI. I've said it a thousand times, and I will say it again, and again, and again. What I see on the ground, and what I've seen at dozens of companies, including my company that I run, there are two sides to a business. There is revenue and there’s costs. On the cost side of the equation, AI can be used to reduce humans doing things that cost money, to some extent. The effect there, I would argue, is nominal. The real opportunity with AI is on the revenue side, where suddenly one engineer can do 100x or 1000x what they used to be able to do, meaning you can make more products at your company, whether those are agricultural seed products, or boats and ships, or software for companies, or clothing, or what have you. Because of AI, everyone has the ability to expand their revenue base to create more products, and that is the foundation of good economic prosperity. It is called productivity. We can grow productivity in this country with AI. So where I see AI being used is on the revenue side 100x more than the cost side. And in that equation, people are hiring like crazy. We cannot hire enough people. I just had a review meeting with my product and engineering team two days ago, and they're like, ‘We want to add an extra 15 headcount to our engineering squads because we have all this opportunity to do stuff that we couldn't otherwise do.’ So we are going to hire more people. And to Sacks' point, we are seeing that show up in the jobs numbers. The idea that AI is going to destroy jobs is a Luddite idea that is being disproven every single day, and I see it on the ground. It is only a matter of time before people wake up to this and they realize that this narrative that they've all been sold is a crock of sh*t.”
1:42

David Friedberg: The AI Jobs Panic Is a Crock of Sh*t Why? The revenue potential outweighs the cost savings by 100x. “There is no job loss with AI. I've said it a thousand times, and I will say it again, and again, and again. What I see on the ground, and what I've seen at dozens of companies, including my company that I run, there are two sides to a business. There is revenue and there’s costs. On the cost side of the equation, AI can be used to reduce humans doing things that cost money, to some extent. The effect there, I would argue, is nominal. The real opportunity with AI is on the revenue side, where suddenly one engineer can do 100x or 1000x what they used to be able to do, meaning you can make more products at your company, whether those are agricultural seed products, or boats and ships, or software for companies, or clothing, or what have you. Because of AI, everyone has the ability to expand their revenue base to create more products, and that is the foundation of good economic prosperity. It is called productivity. We can grow productivity in this country with AI. So where I see AI being used is on the revenue side 100x more than the cost side. And in that equation, people are hiring like crazy. We cannot hire enough people. I just had a review meeting with my product and engineering team two days ago, and they're like, ‘We want to add an extra 15 headcount to our engineering squads because we have all this opportunity to do stuff that we couldn't otherwise do.’ So we are going to hire more people. And to Sacks' point, we are seeing that show up in the jobs numbers. The idea that AI is going to destroy jobs is a Luddite idea that is being disproven every single day, and I see it on the ground. It is only a matter of time before people wake up to this and they realize that this narrative that they've all been sold is a crock of sh*t.”

The All-In Podcast

151,853 görüntüleme • 1 ay önce

“Netflix posted $1.47 billion in revenue in Australia in 2025, $1.35 billion of which was sent overseas as “distribution fees” to its parent entities. After its offshore fees, Netflix Australia had $119 million in revenue and $20 million in profit. It paid $16 million in income tax and paid a dividend of $41 million. This allows them to transfer vast amounts of money abroad, very little of which is taxable in Australia. Netflix had an effective local profit margin of 1.4 per cent of its total Australian revenue. “A distribution fee is paid by the company under this agreement, and the operating margin retained by the company is an arm’s length return in accordance with the Netflix Group’s transfer pricing policy,” the accounts say. There is no suggestion of wrongdoing by Netflix.” •••••••••••••• Globalists often talk about tariffs and how bad they are for free trade, but you rarely hear them complain about reverse tariffs whereby multinational companies can send profits offshore at tax rates much lower than onshore tax rates. Netflix has a local operating profit margin of 1.4% while their worldwide operating profit (see accounts in comments) was 28%. Multinationals get away with shifting so much money offshore because of our tax treaties allow them by setting withholding tax rates well below the onshore company tax rate. For example the withholding tax on royalties paid to the U.S. is just 5%. (See link below) Netflix can save 25% in every dollar it transfers offshore. So 25 cents on $1.35 billion transferred offshore is $338 million in lost company tax to Australia. To be fair to Netflix, some of the fees paid offshore should be offset by production costs but not at rate of 98.6% in the dollar. I.e 1.4% margin. is the only party that understands transfer pricing. We will apply a 25% withholding tax on all offshore payments that exceed the operating profit ratio of its worldwide entity.
2:02

“Netflix posted $1.47 billion in revenue in Australia in 2025, $1.35 billion of which was sent overseas as “distribution fees” to its parent entities. After its offshore fees, Netflix Australia had $119 million in revenue and $20 million in profit. It paid $16 million in income tax and paid a dividend of $41 million. This allows them to transfer vast amounts of money abroad, very little of which is taxable in Australia. Netflix had an effective local profit margin of 1.4 per cent of its total Australian revenue. “A distribution fee is paid by the company under this agreement, and the operating margin retained by the company is an arm’s length return in accordance with the Netflix Group’s transfer pricing policy,” the accounts say. There is no suggestion of wrongdoing by Netflix.” •••••••••••••• Globalists often talk about tariffs and how bad they are for free trade, but you rarely hear them complain about reverse tariffs whereby multinational companies can send profits offshore at tax rates much lower than onshore tax rates. Netflix has a local operating profit margin of 1.4% while their worldwide operating profit (see accounts in comments) was 28%. Multinationals get away with shifting so much money offshore because of our tax treaties allow them by setting withholding tax rates well below the onshore company tax rate. For example the withholding tax on royalties paid to the U.S. is just 5%. (See link below) Netflix can save 25% in every dollar it transfers offshore. So 25 cents on $1.35 billion transferred offshore is $338 million in lost company tax to Australia. To be fair to Netflix, some of the fees paid offshore should be offset by production costs but not at rate of 98.6% in the dollar. I.e 1.4% margin. is the only party that understands transfer pricing. We will apply a 25% withholding tax on all offshore payments that exceed the operating profit ratio of its worldwide entity.

Gerard Rennick

64,200 görüntüleme • 1 ay önce

$FLNC Batteries, Energy Storage 3.8B Market cap My take: A spicy shorter-term "battery meta" play with a potential long-term "Amazon" thesis. $FLNC is in a capital-intensive expansion phase with thin margins generating billions in revenue but very little in net profit Key: This is a capital-intensive INTEGRATOR, not a battery manufacturer. They don't make lithium-ion batteries but rather procure them (roughly 50% from China and more recently aiming for 50% from USA). They provide large grid-scale battery integration into power systems with roles in: 🔹Advisory, procurement, & build-outs. 🔹AI driven battery fleet management software 🔹Long-term servicing ------------------------- THE "SCALE" Global Scale: Operates in 40+ markets with one of the largest deployed fleets of energy storage projects in the world. Credibility and Reach: Formed as a joint venture between Siemens (an industrial manufacturing giant) and AES (a global utility and power generator) with massive industry backing. Massive Backlog: As of their last report, their backlog was already enormous at ~$4.9 billion. They signed an additional ~$1.1 billion in new contracts after this last quarter ended (including two massive projects in Australia) Major Wins: They can operate at scale and were also just awarded Europe's largest ever BESS project (a massive 4 GWh system in Germany). ------------------------- THE "PROFIT PROBLEM" Wafer-Thin Margins: Out of $602.5 million of revenue in Q3 FY2025, their net income was just $6.9M (a ~1.1% net profit margin). (That 14% number you see is their GAAP Gross Margin, which is already thin, but I'd argue the net profit is the current story and why a company doing $2.6B in revenue is valued at $3.8B). Weak Guidance: FY2025 Adj. EBITDA guidance is just $0 to $20M despite forecasting over $2.6B in revenue. Trade Policy Risk: Highly exposed to US-China trade policy, which has weighed on profits. Roughly half of their battery cells come from China which hurts their tax credits. For these reasons they are strategically increasing their US sourcing now with a supply agreement with AESC for U.S. manufactured battery cells, primarily from AESC's facility in Tennessee. "Strong-ish" Growth: Revenue was up 24.7% YoY. This is good, but not explosive given the market's potential, and it's clearly not translating to the bottom line yet. For these reasons this is currently a smaller short term battery meta play for me that has shown very strong recent stock technical performance despite the significant broader market weakness. When institutions want a "cheap" de-risked pure battery play, I think they will reach for $FLNC. The long term potential case is that the story here is the classic "Amazon" model: Is $FLNC a company that's just in a capital-intensive expansion phase, or is it a low-margin business forever? For years, $AMZN wasn't highly profitable "on paper" as virtually all resources were spent on massive scaling. When the profit switch flipped, the stock exploded. $FLNC is in a similar "scale-at-all-costs" phase with the potential that servicing and software will be the future AWS higher margin story. Their pivot to US sourcing isn't just about "surviving" trade policy; it's about building a protected, high-growth, and potentially higher-margin business in the U.S. September 2025 saw their first shipment of U.S. domestic-content BESS systems. Depending on how this capital-intensive phase goes, they could evolve into a long-term play for me. If they survive the cash burn, scale successfully, and flip that profit switch, the "Amazon of batteries" thesis could play out. Relevance: $TSLA $EOSE $BE $GEV $STEM $ENS $GWH $ENS $TE $FSLR
3:10

$FLNC Batteries, Energy Storage 3.8B Market cap My take: A spicy shorter-term "battery meta" play with a potential long-term "Amazon" thesis. $FLNC is in a capital-intensive expansion phase with thin margins generating billions in revenue but very little in net profit Key: This is a capital-intensive INTEGRATOR, not a battery manufacturer. They don't make lithium-ion batteries but rather procure them (roughly 50% from China and more recently aiming for 50% from USA). They provide large grid-scale battery integration into power systems with roles in: 🔹Advisory, procurement, & build-outs. 🔹AI driven battery fleet management software 🔹Long-term servicing ------------------------- THE "SCALE" Global Scale: Operates in 40+ markets with one of the largest deployed fleets of energy storage projects in the world. Credibility and Reach: Formed as a joint venture between Siemens (an industrial manufacturing giant) and AES (a global utility and power generator) with massive industry backing. Massive Backlog: As of their last report, their backlog was already enormous at ~$4.9 billion. They signed an additional ~$1.1 billion in new contracts after this last quarter ended (including two massive projects in Australia) Major Wins: They can operate at scale and were also just awarded Europe's largest ever BESS project (a massive 4 GWh system in Germany). ------------------------- THE "PROFIT PROBLEM" Wafer-Thin Margins: Out of $602.5 million of revenue in Q3 FY2025, their net income was just $6.9M (a ~1.1% net profit margin). (That 14% number you see is their GAAP Gross Margin, which is already thin, but I'd argue the net profit is the current story and why a company doing $2.6B in revenue is valued at $3.8B). Weak Guidance: FY2025 Adj. EBITDA guidance is just $0 to $20M despite forecasting over $2.6B in revenue. Trade Policy Risk: Highly exposed to US-China trade policy, which has weighed on profits. Roughly half of their battery cells come from China which hurts their tax credits. For these reasons they are strategically increasing their US sourcing now with a supply agreement with AESC for U.S. manufactured battery cells, primarily from AESC's facility in Tennessee. "Strong-ish" Growth: Revenue was up 24.7% YoY. This is good, but not explosive given the market's potential, and it's clearly not translating to the bottom line yet. For these reasons this is currently a smaller short term battery meta play for me that has shown very strong recent stock technical performance despite the significant broader market weakness. When institutions want a "cheap" de-risked pure battery play, I think they will reach for $FLNC. The long term potential case is that the story here is the classic "Amazon" model: Is $FLNC a company that's just in a capital-intensive expansion phase, or is it a low-margin business forever? For years, $AMZN wasn't highly profitable "on paper" as virtually all resources were spent on massive scaling. When the profit switch flipped, the stock exploded. $FLNC is in a similar "scale-at-all-costs" phase with the potential that servicing and software will be the future AWS higher margin story. Their pivot to US sourcing isn't just about "surviving" trade policy; it's about building a protected, high-growth, and potentially higher-margin business in the U.S. September 2025 saw their first shipment of U.S. domestic-content BESS systems. Depending on how this capital-intensive phase goes, they could evolve into a long-term play for me. If they survive the cash burn, scale successfully, and flip that profit switch, the "Amazon of batteries" thesis could play out. Relevance: $TSLA $EOSE $BE $GEV $STEM $ENS $GWH $ENS $TE $FSLR

YeahDave

27,279 görüntüleme • 8 ay önce

THE ECONOMICS OF A FIREWOOD BUSINESS Do you know what the net profit margin is on a firewood business? 85% And I know that because I've talked to dozens of people running these businesses and the numbers are wild This is exactly how it works: 1) You can get firewood for free or extremely cheap. People are literally paying tree companies like mine to get rid of trees. You show up with a truck, you haul it away and they're thanking you for it. 2) Then you split it, you stack it and you season it for a few months and sell it for $300 to $600 per cord. Startup costs are almost nothing, a chainsaw, an axe or a log splitter and a truck and you can rent a log splitter for $70 a day. You could be in this business for under $800. Some people start with even less by borrowing equipment and this is perfect because the demand is consistent. People need fire wood every single winter. It's not a fad, it's not going away and it's local and in the summertime you sell to the barbecue enthusiast. You're not competing with Amazon or big corporations, you 're competing with maybe three other guys in the area that don't even know what a website is. The work is physical but it's simple, you're going to the gym anyway. Cut, split, stack, deliver. No complicated systems, no tech skills required. You run this entire business from your phone with a Facebook marketplace and a Craigslist ad and winter's coming. The kicker is that you build up a customer base and then they come back every single year. So it's recurring revenue without a subscription model. People find a firewood guy that they trust and they stick with them forever. You could realistically do $50,000 to $150,000 a year seasonally working part-time. I'm stumped why more people aren't doing this.
1:20

THE ECONOMICS OF A FIREWOOD BUSINESS Do you know what the net profit margin is on a firewood business? 85% And I know that because I've talked to dozens of people running these businesses and the numbers are wild This is exactly how it works: 1) You can get firewood for free or extremely cheap. People are literally paying tree companies like mine to get rid of trees. You show up with a truck, you haul it away and they're thanking you for it. 2) Then you split it, you stack it and you season it for a few months and sell it for $300 to $600 per cord. Startup costs are almost nothing, a chainsaw, an axe or a log splitter and a truck and you can rent a log splitter for $70 a day. You could be in this business for under $800. Some people start with even less by borrowing equipment and this is perfect because the demand is consistent. People need fire wood every single winter. It's not a fad, it's not going away and it's local and in the summertime you sell to the barbecue enthusiast. You're not competing with Amazon or big corporations, you 're competing with maybe three other guys in the area that don't even know what a website is. The work is physical but it's simple, you're going to the gym anyway. Cut, split, stack, deliver. No complicated systems, no tech skills required. You run this entire business from your phone with a Facebook marketplace and a Craigslist ad and winter's coming. The kicker is that you build up a customer base and then they come back every single year. So it's recurring revenue without a subscription model. People find a firewood guy that they trust and they stick with them forever. You could realistically do $50,000 to $150,000 a year seasonally working part-time. I'm stumped why more people aren't doing this.

Chris Koerner

357,494 görüntüleme • 5 ay önce

I think Starlink is wildly undervalued. It’s a $1+ trillion company in the making on its own. A lot of people still think Starlink is just “internet from space,” but in reality, it’s one of the most important communications networks ever built. In 2025 alone, Starlink generated $11.4 billion in revenue, accounting for roughly 61% of SpaceX’s total revenue. It served more than 10 million customers globally and generated $4.4 billion in operating profit w/ EBITDA margins of 63%. Starlink is a cash machine. Fyi, independent analysts forecast Starlink will generate approximately $20 billion in revenue, $14 billion in EBITDA, and over $8 billion in free cash flow in 2026… plus consumer broadband will continue to expand rapidly, while aviation, maritime, Starshield, and direct-to-cell services will open entirely new markets. The real advantage is that Starlink owns the entire stack. SpaceX builds the satellites, they launch the satellites, they operate the network, and they manufacture the user terminals. No competitor comes close to that level of vertical integration…. On top of this, starship will make the story even more crazier with next-generation satellites, 100+ satellites per launch, dramatically lowering launch costs, and thousands of new satellites being deployed each year, the cost of serving additional customers continuing to fall, while the network & tech keep getting stronger. If you really want to understand why SpaceX is at a $2T valuation… start with Starlink. Starlink already generates the majority of SpaceX’s revenue, profit, and free cash flow. It helps fund Starship development, supports expansion across the company, and provides the financial engine behind SpaceX’s long-term ambitions. The bull case is based on real revenue, real profits, real customers, and a moat that gets wider every year… NOT hype. The way I see it, Starlink will become the most valuable communication company in human history and a $1 trillion valuation doesn’t sound crazy to me for this business/technology alone.
0:23

I think Starlink is wildly undervalued. It’s a $1+ trillion company in the making on its own. A lot of people still think Starlink is just “internet from space,” but in reality, it’s one of the most important communications networks ever built. In 2025 alone, Starlink generated $11.4 billion in revenue, accounting for roughly 61% of SpaceX’s total revenue. It served more than 10 million customers globally and generated $4.4 billion in operating profit w/ EBITDA margins of 63%. Starlink is a cash machine. Fyi, independent analysts forecast Starlink will generate approximately $20 billion in revenue, $14 billion in EBITDA, and over $8 billion in free cash flow in 2026… plus consumer broadband will continue to expand rapidly, while aviation, maritime, Starshield, and direct-to-cell services will open entirely new markets. The real advantage is that Starlink owns the entire stack. SpaceX builds the satellites, they launch the satellites, they operate the network, and they manufacture the user terminals. No competitor comes close to that level of vertical integration…. On top of this, starship will make the story even more crazier with next-generation satellites, 100+ satellites per launch, dramatically lowering launch costs, and thousands of new satellites being deployed each year, the cost of serving additional customers continuing to fall, while the network & tech keep getting stronger. If you really want to understand why SpaceX is at a $2T valuation… start with Starlink. Starlink already generates the majority of SpaceX’s revenue, profit, and free cash flow. It helps fund Starship development, supports expansion across the company, and provides the financial engine behind SpaceX’s long-term ambitions. The bull case is based on real revenue, real profits, real customers, and a moat that gets wider every year… NOT hype. The way I see it, Starlink will become the most valuable communication company in human history and a $1 trillion valuation doesn’t sound crazy to me for this business/technology alone.

Teslaconomics

27,597 görüntüleme • 1 ay önce

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi
5:34

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

Andrew Bolis

36,504 görüntüleme • 10 ay önce

$IREN CCO KENT DRAPER TALK ON AI GIGAFACTORIES $IREN CCO Kent Draper: We were ready for this moment [AI and HPC] and been exploring it for a number of years but the market wasn’t there to make it commercially viable. When the business was founded, our two co-founders have a background in large scale institutional infrastructure banking, and they could see this infliction in the industry where the world was becoming more data dependent. It was very obvious to them that we were going to need power-dense compute, particularly with access to renewables, which is a big part of our story. Design [of data centers] is the easy part. Delivering infrastructure at this scale is not simple. I think our minds this was never actually a pivot for us. Lenovo SVP Vlad Rozanovich: Time to first token is a metric that so many people like $IREN look at because it’s how you maximize value and profit as soon as you can. Daniel Newman: You can’t have a bubble and have AI eating every industry, either too good or not good.
17:24

$IREN CCO KENT DRAPER TALK ON AI GIGAFACTORIES $IREN CCO Kent Draper: We were ready for this moment [AI and HPC] and been exploring it for a number of years but the market wasn’t there to make it commercially viable. When the business was founded, our two co-founders have a background in large scale institutional infrastructure banking, and they could see this infliction in the industry where the world was becoming more data dependent. It was very obvious to them that we were going to need power-dense compute, particularly with access to renewables, which is a big part of our story. Design [of data centers] is the easy part. Delivering infrastructure at this scale is not simple. I think our minds this was never actually a pivot for us. Lenovo SVP Vlad Rozanovich: Time to first token is a metric that so many people like $IREN look at because it’s how you maximize value and profit as soon as you can. Daniel Newman: You can’t have a bubble and have AI eating every industry, either too good or not good.

The Tech Investor

25,559 görüntüleme • 3 ay önce

Anthropic is kicking OpenAI’s ass: Insights from the largest revenue explosion in tech history Brad Gerstner on how Anthropic dominated the last 90 days, and could they hit a $100B run rate by EOY!?: “Anthropic was literally counted out of the game last year, and they've kicked OpenAI's ass over the last 90 days. Bam, you have the largest revenue explosion in the history of technology. So you have to ask, what's going on? The first thing, for me, is that model and product capability just hit this threshold near AGI, whatever the hell you want to call it. And everybody, like Altimeter, said, ‘Damn, this is so good. I have to have it. This is no longer about my IT budget. This is about labor augmentation and labor replacement. Turns out that the TAM for intelligence is radically different than anything we've seen before. And I think the best example of this, right, this is millions of self-interested parties, consumers, enterprises, a thousand now over $1M, right? It's not that there was some great go to market at Anthropic that all of a sudden they snuck up and blew everybody away. No, it was companies demanding the product. They're getting throttled on the product. Why? Because it's so good. It makes them better at their business. We knew intelligence was going to scale on the exponential. The question was whether revenue will scale on the exponential, and that's what we're seeing. And remember, they're doing this with only 1.5-2 gigawatts of compute, and the models are only getting better. So I think when you look out toward the end of the year, I would not be shocked if you see Anthropic exiting this year at $80-$100B in revenue.”
1:25

Anthropic is kicking OpenAI’s ass: Insights from the largest revenue explosion in tech history Brad Gerstner on how Anthropic dominated the last 90 days, and could they hit a $100B run rate by EOY!?: “Anthropic was literally counted out of the game last year, and they've kicked OpenAI's ass over the last 90 days. Bam, you have the largest revenue explosion in the history of technology. So you have to ask, what's going on? The first thing, for me, is that model and product capability just hit this threshold near AGI, whatever the hell you want to call it. And everybody, like Altimeter, said, ‘Damn, this is so good. I have to have it. This is no longer about my IT budget. This is about labor augmentation and labor replacement. Turns out that the TAM for intelligence is radically different than anything we've seen before. And I think the best example of this, right, this is millions of self-interested parties, consumers, enterprises, a thousand now over $1M, right? It's not that there was some great go to market at Anthropic that all of a sudden they snuck up and blew everybody away. No, it was companies demanding the product. They're getting throttled on the product. Why? Because it's so good. It makes them better at their business. We knew intelligence was going to scale on the exponential. The question was whether revenue will scale on the exponential, and that's what we're seeing. And remember, they're doing this with only 1.5-2 gigawatts of compute, and the models are only getting better. So I think when you look out toward the end of the year, I would not be shocked if you see Anthropic exiting this year at $80-$100B in revenue.”

The All-In Podcast

69,777 görüntüleme • 3 ay önce

AI is the first technology in history where more customers makes you POORER. Every tech company in history got cheaper as it scaled. More users meant lower costs per user. That's the entire model. That's why Microsoft prints money. That's why Google prints money. That's why Meta prints money. Software has near-zero marginal cost. Build it once. Sell it a billion times. The 100 millionth user costs basically nothing to serve. This is the single most important rule in tech economics. But AI completely broke it. Every single query costs real compute. Every interaction burns real electricity. Every response depreciates real hardware. There is no "build once, sell forever." There is only "burn money every time someone asks a question." And the numbers prove it: OpenAI hit $20 billion in annualized revenue. Losses? $14 billion. For every dollar they earn, they spend $1.69 delivering it. Their losses TRIPLED as their revenue grew. Not because they're bad at business, but simply because the model itself is broken. Anthropic crossed $30 billion in annualized revenue. Still burning billions. Still not profitable. Still raising tens of billions just to keep the lights on. xAI is burning $1 billion every single month. Perplexity spent 164% of its revenue on compute costs from AWS, They literally spent more on running the AI than they made from selling it. This is not how technology is supposed to work. Google once estimated that adding AI to every search query would require 500,000 A100 servers. The cost of answering a single AI query is 10x MORE than a traditional search result. Traditional software: Serving 1 million users costs roughly the same as serving 100,000. The marginal cost is basically zero. AI: Serving 1 million users can cost 10 times what 100,000 costs. Every new user is a new expense. Every new query is a new dollar burned. This is reverse economics. The more successful you become, the faster you die. And nobody in the industry wants to talk about it because the entire narrative depends on you believing AI companies work like software companies. But they don't. They NEVER will. Software scales to infinity. AI scales to bankruptcy. HSBC ran the numbers on OpenAI specifically. Their conclusion: Even after every funding round, every investment, every deal, OpenAI still faces a $207 BILLION shortfall to reach profitability. The industry response has been to raise prices. ChatGPT went from free to $20 to $200 for the Pro plan. And it's still not enough because the cost of running these models grows FASTER than any price increase consumers will accept. Meanwhile 966 AI startups died in 2024. A 25.6% jump from the year before. AI startups burn cash twice as fast as non-AI tech companies. And the ones building on TOP of OpenAI and Anthropic are in even worse shape. Every wrapper app. Every "AI-powered" SaaS tool. Every startup whose entire product is someone else's model with a different skin on it. They're all margin-negative. Every single one. And these are the companies about to IPO. SpaceX, OpenAI, Anthropic, and Cerebras. $240 billion in combined raises planned for 2026. They're asking you to invest in an industry where the fundamental unit economics don't work. Where the MORE customers you get, the MORE money you lose. Where no company has figured out how to make the math positive. The dot-com bubble had the same pitch: "Revenue is growing. Profitability comes later." For most of them, later never came. The question isn't whether AI will change the world. It will. The question is whether it can do it without going broke first. And right now, every single number literally says no. How can they become profitable?
0:26

AI is the first technology in history where more customers makes you POORER. Every tech company in history got cheaper as it scaled. More users meant lower costs per user. That's the entire model. That's why Microsoft prints money. That's why Google prints money. That's why Meta prints money. Software has near-zero marginal cost. Build it once. Sell it a billion times. The 100 millionth user costs basically nothing to serve. This is the single most important rule in tech economics. But AI completely broke it. Every single query costs real compute. Every interaction burns real electricity. Every response depreciates real hardware. There is no "build once, sell forever." There is only "burn money every time someone asks a question." And the numbers prove it: OpenAI hit $20 billion in annualized revenue. Losses? $14 billion. For every dollar they earn, they spend $1.69 delivering it. Their losses TRIPLED as their revenue grew. Not because they're bad at business, but simply because the model itself is broken. Anthropic crossed $30 billion in annualized revenue. Still burning billions. Still not profitable. Still raising tens of billions just to keep the lights on. xAI is burning $1 billion every single month. Perplexity spent 164% of its revenue on compute costs from AWS, They literally spent more on running the AI than they made from selling it. This is not how technology is supposed to work. Google once estimated that adding AI to every search query would require 500,000 A100 servers. The cost of answering a single AI query is 10x MORE than a traditional search result. Traditional software: Serving 1 million users costs roughly the same as serving 100,000. The marginal cost is basically zero. AI: Serving 1 million users can cost 10 times what 100,000 costs. Every new user is a new expense. Every new query is a new dollar burned. This is reverse economics. The more successful you become, the faster you die. And nobody in the industry wants to talk about it because the entire narrative depends on you believing AI companies work like software companies. But they don't. They NEVER will. Software scales to infinity. AI scales to bankruptcy. HSBC ran the numbers on OpenAI specifically. Their conclusion: Even after every funding round, every investment, every deal, OpenAI still faces a $207 BILLION shortfall to reach profitability. The industry response has been to raise prices. ChatGPT went from free to $20 to $200 for the Pro plan. And it's still not enough because the cost of running these models grows FASTER than any price increase consumers will accept. Meanwhile 966 AI startups died in 2024. A 25.6% jump from the year before. AI startups burn cash twice as fast as non-AI tech companies. And the ones building on TOP of OpenAI and Anthropic are in even worse shape. Every wrapper app. Every "AI-powered" SaaS tool. Every startup whose entire product is someone else's model with a different skin on it. They're all margin-negative. Every single one. And these are the companies about to IPO. SpaceX, OpenAI, Anthropic, and Cerebras. $240 billion in combined raises planned for 2026. They're asking you to invest in an industry where the fundamental unit economics don't work. Where the MORE customers you get, the MORE money you lose. Where no company has figured out how to make the math positive. The dot-com bubble had the same pitch: "Revenue is growing. Profitability comes later." For most of them, later never came. The question isn't whether AI will change the world. It will. The question is whether it can do it without going broke first. And right now, every single number literally says no. How can they become profitable?

Ricardo

167,283 görüntüleme • 2 ay önce

Tools for the 100x Solo Founder For decades, company size has been dictated by coordination costs. If you wanted to grow revenue, you hired people. More customers meant more support reps. More revenue meant more finance and ops. Scale meant headcount. AI is starting to break that relationship. At a16z speedrun 🧊, we’re interested in startups building tools that allow a single founder to operate what would previously have required entire teams. Today’s models have an ever-growing list of new capabilities. They can write production code, reconcile financial statements, run outbound campaigns, manage customer support, and reason over operational data. The constraint is no longer task capability. It’s whether these systems can be orchestrated reliably enough to replace entire business functions. Here are a few concrete examples of what this could look like in practice: • An AI-native finance stack that reconciles transactions in real time, manages payables and receivables, forecasts cash flow, and ensures compliance - without a human bookkeeper. • An autonomous revenue engine that sources leads, runs outbound, manages CRM state, handles inbound qualification, and drives renewals - accountable to pipeline and revenue. • An AI-operated supply chain for SMBs that forecasts demand, manages inventory, and coordinates logistics. The defining feature is control. These systems should have direct access to operational surfaces - bank accounts, CRMs, inboxes, ad platforms, inventory systems - and be trusted to act. They should not just recommend actions but execute them, measure outcomes, and improve over time. If AI meaningfully reduces coordination and execution costs, the minimum viable company shrinks. If you’re interested in building software that enables one person to operate at the scale of many, we’d love to talk to you!
1:43

Tools for the 100x Solo Founder For decades, company size has been dictated by coordination costs. If you wanted to grow revenue, you hired people. More customers meant more support reps. More revenue meant more finance and ops. Scale meant headcount. AI is starting to break that relationship. At a16z speedrun 🧊, we’re interested in startups building tools that allow a single founder to operate what would previously have required entire teams. Today’s models have an ever-growing list of new capabilities. They can write production code, reconcile financial statements, run outbound campaigns, manage customer support, and reason over operational data. The constraint is no longer task capability. It’s whether these systems can be orchestrated reliably enough to replace entire business functions. Here are a few concrete examples of what this could look like in practice: • An AI-native finance stack that reconciles transactions in real time, manages payables and receivables, forecasts cash flow, and ensures compliance - without a human bookkeeper. • An autonomous revenue engine that sources leads, runs outbound, manages CRM state, handles inbound qualification, and drives renewals - accountable to pipeline and revenue. • An AI-operated supply chain for SMBs that forecasts demand, manages inventory, and coordinates logistics. The defining feature is control. These systems should have direct access to operational surfaces - bank accounts, CRMs, inboxes, ad platforms, inventory systems - and be trusted to act. They should not just recommend actions but execute them, measure outcomes, and improve over time. If AI meaningfully reduces coordination and execution costs, the minimum viable company shrinks. If you’re interested in building software that enables one person to operate at the scale of many, we’d love to talk to you!

Emily Bennett

28,843 görüntüleme • 2 ay önce

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!
9:28

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 görüntüleme • 8 ay önce

Rahul Vohra (rahulvohra) is the founder and CEO of Superhuman, and one of the most original, insightful, and articulate founders I’ve met. In our conversation, we discuss: 🔸 Why Rahul restructured his entire executive team to spend 60% to 70% of his time on product, design, and marketing instead of the typical CEO responsibilities. 🔸 The unexpected insight about virality Rahul learned from LinkedIn’s head of growth. 🔸 His counterintuitive approach to finding product-market fit, and how this can algorithmically determine your roadmap. 🔸 The “Single Decisive Reason” framework for making better decisions by avoiding collections of weak justifications. 🔸 How manually onboarding every user (Superhuman had 20 full-time people doing this at peak) allows engineers to focus on product rather than onboarding flows (and creates superfans) 🔸 How Superhuman’s AI features have evolved to create a truly intelligent email experience that works while you sleep. 🔸 Much more 🏆 @Get_Eppo — Run reliable, impactful experiments: 🏆 Fundrise Flagship Fund — Invest in $1.1 billion of real estate: 🏆 OneSchema — Import CSV data 10x faster:
1:12

Rahul Vohra (rahulvohra) is the founder and CEO of Superhuman, and one of the most original, insightful, and articulate founders I’ve met. In our conversation, we discuss: 🔸 Why Rahul restructured his entire executive team to spend 60% to 70% of his time on product, design, and marketing instead of the typical CEO responsibilities. 🔸 The unexpected insight about virality Rahul learned from LinkedIn’s head of growth. 🔸 His counterintuitive approach to finding product-market fit, and how this can algorithmically determine your roadmap. 🔸 The “Single Decisive Reason” framework for making better decisions by avoiding collections of weak justifications. 🔸 How manually onboarding every user (Superhuman had 20 full-time people doing this at peak) allows engineers to focus on product rather than onboarding flows (and creates superfans) 🔸 How Superhuman’s AI features have evolved to create a truly intelligent email experience that works while you sleep. 🔸 Much more 🏆 @Get_Eppo — Run reliable, impactful experiments: 🏆 Fundrise Flagship Fund — Invest in $1.1 billion of real estate: 🏆 OneSchema — Import CSV data 10x faster:

Lenny Rachitsky

169,857 görüntüleme • 1 yıl önce

I just built a Meta Ad Comment Spy in Claude Code that turns every comment on your ads into a voice-of-customer goldmine 🤯 Point Comment Spy at your ad account → it pulls every comment and reply, clusters them into objections, questions, and proof, and writes your next 5 ad angles from real customer language. All inside Claude Code. Perfect for DTC brands and agencies who want ad copy that's already written, buried in the comments on their own ads. If your sharpest hooks come from how customers actually talk, but mining them means scrolling your ad comments by hand, copy-pasting the good ones into a doc, and missing the objections quietly killing your conversion rate... Comment Spy kills the entire loop: → Ranks your ads by spend, so you mine the ones that matter → Pulls every comment AND reply thread through the Meta API → Clusters them: objections, buying questions, desires, proof → Surfaces the exact phrases to drop into your next ad → Flags the conversion leaks hiding in your comments — dead links, broken lead-magnet delivery → Renders the whole thing as a dashboard No manual scrolling. No missed objections. No $99/mo "ad intelligence" subscription. What you get: → Every comment off your top ads, pulled automatically → Clustered into objections, questions, proof, and desires → 5 ad angles written from your real customer language → A dark-mode dashboard, every quote traced to the ad that drew it Built 100% in Claude Code, on the Meta API. I put together a step-by-step playbook showing you how to build Comment Spy yourself: the Meta API setup, the exact prompts, the whole pipeline. Want the playbook for free? > Like this post > Comment "SPY" And I'll send it over (must be following so I can DM)
0:21

I just built a Meta Ad Comment Spy in Claude Code that turns every comment on your ads into a voice-of-customer goldmine 🤯 Point Comment Spy at your ad account → it pulls every comment and reply, clusters them into objections, questions, and proof, and writes your next 5 ad angles from real customer language. All inside Claude Code. Perfect for DTC brands and agencies who want ad copy that's already written, buried in the comments on their own ads. If your sharpest hooks come from how customers actually talk, but mining them means scrolling your ad comments by hand, copy-pasting the good ones into a doc, and missing the objections quietly killing your conversion rate... Comment Spy kills the entire loop: → Ranks your ads by spend, so you mine the ones that matter → Pulls every comment AND reply thread through the Meta API → Clusters them: objections, buying questions, desires, proof → Surfaces the exact phrases to drop into your next ad → Flags the conversion leaks hiding in your comments — dead links, broken lead-magnet delivery → Renders the whole thing as a dashboard No manual scrolling. No missed objections. No $99/mo "ad intelligence" subscription. What you get: → Every comment off your top ads, pulled automatically → Clustered into objections, questions, proof, and desires → 5 ad angles written from your real customer language → A dark-mode dashboard, every quote traced to the ad that drew it Built 100% in Claude Code, on the Meta API. I put together a step-by-step playbook showing you how to build Comment Spy yourself: the Meta API setup, the exact prompts, the whole pipeline. Want the playbook for free? > Like this post > Comment "SPY" And I'll send it over (must be following so I can DM)

Mike Futia

14,227 görüntüleme • 18 gün önce

Big Tech just ran out of money building AI and what they're doing to cover it up should be illegal. Google, Amazon, Microsoft, and Meta are spending a combined $700 BILLION this year on AI infrastructure. This eats up 94% of their total operating cash flow. The richest companies in human history are almost broke. And instead of slowing down, they're covering it up with the biggest financial engineering operation since 2008: Google just sold $80 billion in stock to fund AI infrastructure. That was their first equity raise in 20 YEARS. The last time Google needed to sell stock, YouTube didn't even exist. Sundar Pichai admitted the thing keeping him up at night is "compute capacity." The company that prints $100 billion a year in ad revenue just told Wall Street it isn't enough anymore. Amazon's free cash flow is projected to go NEGATIVE this year for the first time ever. Morgan Stanley estimates a $17 billion deficit and Bank of America says $28 billion. The most profitable logistics machine on Earth is about to burn more cash than it generates, and they quietly filed with the SEC saying they may need to raise even more debt and equity to keep building. All four hyperscalers are now borrowing hundreds of billions in bonds to keep the AI buildout alive. These were the most cash-rich companies in human history, and they're leveraging themselves to the teeth to build infrastructure that nobody has proven will generate enough revenue to pay for itself. And the cracks are already starting to show: Broadcom makes the custom AI chips that power Google, Meta, OpenAI, and Anthropic. This week their AI revenue TRIPLED year over year, sales grew 48%, and profits smashed every Wall Street estimate. The reward for all of that was $320 billion in value erased in a single trading session. Their CEO Hock Tan went on the earnings call and exposed three things about the AI industry: Google is already shopping for cheaper AI chip alternatives, broadcom abandoned its strategy of selling complete AI systems and is now retreating to selling bare chips at lower margins. And despite supposedly "unprecedented demand," Tan refused to raise his full-year forecast, which tells you everything about what he's actually seeing behind the curtain. Wall Street heard all three and hit the sell button so hard it dragged AMD, Intel, and the entire chip sector down with it. When a company triples its AI revenue and gets punished because tripling isn't fast enough, the expectations have left the atmosphere entirely. And here's the really scary part... These companies ARE your retirement account. Apple, Microsoft, Amazon, Google, Meta, and Nvidia make up roughly 30% of the S&P 500. If you have a 401k or an index fund, you are already exposed to this bet whether you chose to be or not. Every single one of these companies is telling you AI will generate trillions in revenue. But right now the math says they're spending trillions FIRST and hoping the revenue shows up later. If the revenue catches up, this becomes the greatest infrastructure buildout in human history. Bigger than railroads and bigger than the internet. If it doesn't, the companies that make up a third of the American stock market just leveraged their balance sheets into the largest write-down cycle since 2000. And unlike the dot-com crash, this time the bubble companies aren't random startups with no revenue. They're the backbone of the entire global economy.
2:08

Big Tech just ran out of money building AI and what they're doing to cover it up should be illegal. Google, Amazon, Microsoft, and Meta are spending a combined $700 BILLION this year on AI infrastructure. This eats up 94% of their total operating cash flow. The richest companies in human history are almost broke. And instead of slowing down, they're covering it up with the biggest financial engineering operation since 2008: Google just sold $80 billion in stock to fund AI infrastructure. That was their first equity raise in 20 YEARS. The last time Google needed to sell stock, YouTube didn't even exist. Sundar Pichai admitted the thing keeping him up at night is "compute capacity." The company that prints $100 billion a year in ad revenue just told Wall Street it isn't enough anymore. Amazon's free cash flow is projected to go NEGATIVE this year for the first time ever. Morgan Stanley estimates a $17 billion deficit and Bank of America says $28 billion. The most profitable logistics machine on Earth is about to burn more cash than it generates, and they quietly filed with the SEC saying they may need to raise even more debt and equity to keep building. All four hyperscalers are now borrowing hundreds of billions in bonds to keep the AI buildout alive. These were the most cash-rich companies in human history, and they're leveraging themselves to the teeth to build infrastructure that nobody has proven will generate enough revenue to pay for itself. And the cracks are already starting to show: Broadcom makes the custom AI chips that power Google, Meta, OpenAI, and Anthropic. This week their AI revenue TRIPLED year over year, sales grew 48%, and profits smashed every Wall Street estimate. The reward for all of that was $320 billion in value erased in a single trading session. Their CEO Hock Tan went on the earnings call and exposed three things about the AI industry: Google is already shopping for cheaper AI chip alternatives, broadcom abandoned its strategy of selling complete AI systems and is now retreating to selling bare chips at lower margins. And despite supposedly "unprecedented demand," Tan refused to raise his full-year forecast, which tells you everything about what he's actually seeing behind the curtain. Wall Street heard all three and hit the sell button so hard it dragged AMD, Intel, and the entire chip sector down with it. When a company triples its AI revenue and gets punished because tripling isn't fast enough, the expectations have left the atmosphere entirely. And here's the really scary part... These companies ARE your retirement account. Apple, Microsoft, Amazon, Google, Meta, and Nvidia make up roughly 30% of the S&P 500. If you have a 401k or an index fund, you are already exposed to this bet whether you chose to be or not. Every single one of these companies is telling you AI will generate trillions in revenue. But right now the math says they're spending trillions FIRST and hoping the revenue shows up later. If the revenue catches up, this becomes the greatest infrastructure buildout in human history. Bigger than railroads and bigger than the internet. If it doesn't, the companies that make up a third of the American stock market just leveraged their balance sheets into the largest write-down cycle since 2000. And unlike the dot-com crash, this time the bubble companies aren't random startups with no revenue. They're the backbone of the entire global economy.

Ricardo

227,397 görüntüleme • 1 ay önce

I just added payments to my app... by typing one simple prompt. Replit's new Stripe integration is a game-changer plug in payments instantly no setup hassle. Turn any idea into a revenue-generating product in minutes: subscriptions, storefronts, paid tools all handled automatically with a sandbox for safe testing. Anyone can build and monetize now. Here's a quick step-by-step to get started ↓ 1. Head to Replit and create a new workspace it's your all-in-one spot for building. 2. Fire up Agent in your workspace this is where the magic happens. Prompt something like: "Add Stripe payments for subscriptions" or "Integrate one-time payments with Stripe." Agent sets it up automatically: handles the connection, data models and flow. No keys or dashboards needed. 3. Test in the built-in sandbox run payments safely see transactions hit your database, no real money involved. 4. Ready to launch? Connect your own Stripe account to go live seamless switch from test to revenue. That's it idea to income in minutes. My referral (get $10 credit on Replit Core):
0:36

I just added payments to my app... by typing one simple prompt. Replit's new Stripe integration is a game-changer plug in payments instantly no setup hassle. Turn any idea into a revenue-generating product in minutes: subscriptions, storefronts, paid tools all handled automatically with a sandbox for safe testing. Anyone can build and monetize now. Here's a quick step-by-step to get started ↓ 1. Head to Replit and create a new workspace it's your all-in-one spot for building. 2. Fire up Agent in your workspace this is where the magic happens. Prompt something like: "Add Stripe payments for subscriptions" or "Integrate one-time payments with Stripe." Agent sets it up automatically: handles the connection, data models and flow. No keys or dashboards needed. 3. Test in the built-in sandbox run payments safely see transactions hit your database, no real money involved. 4. Ready to launch? Connect your own Stripe account to go live seamless switch from test to revenue. That's it idea to income in minutes. My referral (get $10 credit on Replit Core):

Meer | AI Tools & News

17,593 görüntüleme • 7 ay önce