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IKEA deployed an AI chatbot named Billy to handle level-one customer service inquiries. It reportedly resolved around 57% of those engagements without human escalation. Most companies would have celebrated the labor savings and stopped there. Cost takeout right? But the more interesting move was to study the 43% of... show more

Brian Solis

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415,739 просмотров • 4 месяцев назад •via X (Twitter)

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If you're in a meeting right now talking about how AI will let you cut 40% of your team, you're playing the small game. IKEA just showed why. They deployed a chatbot named Billy to handle level 1 customer service. It resolved 57% of inquiries on its own. Most companies would've booked the labor savings and stopped there. IKEA studied the 43% Billy couldn't resolve. Those unresolved tickets pointed to one massive demand signal: customers wanted interior design help. They didn't want to figure it out themselves. So IKEA spun up a design consultancy, reskilled the customer service team with AI, and created a new revenue stream that did ~$1B in its first year. Neil Patel and I sit in these meetings every week. Everyone's optimizing for the cut. Almost nobody is asking what their AI is actually telling them. Your business is a problem-solving machine. What other problems could it solve if you redeployed the people you already have? Cutting headcount is the last resort, not the first move.

If you're in a meeting right now talking about how AI will let you cut 40% of your team, you're playing the small game. IKEA just showed why. They deployed a chatbot named Billy to handle level 1 customer service. It resolved 57% of inquiries on its own. Most companies would've booked the labor savings and stopped there. IKEA studied the 43% Billy couldn't resolve. Those unresolved tickets pointed to one massive demand signal: customers wanted interior design help. They didn't want to figure it out themselves. So IKEA spun up a design consultancy, reskilled the customer service team with AI, and created a new revenue stream that did ~$1B in its first year. Neil Patel and I sit in these meetings every week. Everyone's optimizing for the cut. Almost nobody is asking what their AI is actually telling them. Your business is a problem-solving machine. What other problems could it solve if you redeployed the people you already have? Cutting headcount is the last resort, not the first move.

ericosiu

18,990 просмотров • 3 месяцев назад

NEW LONG FORM VIDEO: Why nobody uses Craigslist anymore Craigslist once generated more than $1 billion a year in revenue with just 28 employees. To put that in perspective, that’s more revenue per employee than even Google at its peak. Its founder turned down billions of dollars in venture capital, refused to run advertising, and chose to serve as head of customer service rather than CEO. For years, that philosophy seemed brilliant. But just six years later, Craigslist had lost more than 70% of its revenue. This is the story of how one man’s unconventional principles and vision built an internet empire and how those same principles may have ultimately contributed to its decline. This is the rise and fall of Craigslist.

NEW LONG FORM VIDEO: Why nobody uses Craigslist anymore Craigslist once generated more than $1 billion a year in revenue with just 28 employees. To put that in perspective, that’s more revenue per employee than even Google at its peak. Its founder turned down billions of dollars in venture capital, refused to run advertising, and chose to serve as head of customer service rather than CEO. For years, that philosophy seemed brilliant. But just six years later, Craigslist had lost more than 70% of its revenue. This is the story of how one man’s unconventional principles and vision built an internet empire and how those same principles may have ultimately contributed to its decline. This is the rise and fall of Craigslist.

Michael Girdley

98,039 просмотров • 1 месяц назад

$Elon Musk explains the trillion dollar opportunity in AI “As soon as you unlock digital human, you basically have access to trillions of dollars in revenue” “The most valuable companies currently by market cap, their output is digital. So if you have a human emulator you can basically create one of the most valuable companies in the world overnight” “Take something as simple as customer service. If you have to integrate with the APIs of existing corporations, many of which don’t even have an API, so you’ve got to make one, that’s extremely slow” “However if AI can simply take whatever is given to the outsourced customer service company that they already use, you can make tremendous headway in customer service, which I think is like 1% of the world’s economy and there’s no barriers to entry. You can just immediately say, we’ll outsource it for a fraction of the cost and there’s no integration needed”$

Elon Musk explains the trillion dollar opportunity in AI “As soon as you unlock digital human, you basically have access to trillions of dollars in revenue” “The most valuable companies currently by market cap, their output is digital. So if you have a human emulator you can basically create one of the most valuable companies in the world overnight” “Take something as simple as customer service. If you have to integrate with the APIs of existing corporations, many of which don’t even have an API, so you’ve got to make one, that’s extremely slow” “However if AI can simply take whatever is given to the outsourced customer service company that they already use, you can make tremendous headway in customer service, which I think is like 1% of the world’s economy and there’s no barriers to entry. You can just immediately say, we’ll outsource it for a fraction of the cost and there’s no integration needed”

Jack

93,136 просмотров • 4 месяцев назад

$Elon Musk says AI will gut customer service — a $1 trillion industry — in months. Musk laid out the path AI takes to eat industry after industry. "If you have to integrate with the APIs of existing corporations — many of which don't even have an API, so you've got to make one, and you've got to wade through legacy software — that's extremely slow." Then he picked the industry that skips the slow part. "Take something as simple as customer service." Customer service is a human at a desktop, clicking through software the company already runs. "If AI can simply take whatever is given to the outsourced customer service company that they already use and do customer service using the apps that they already use, then you can make tremendous headway." No API. No integration. No engineering quarter to coordinate. Musk, on the size of the prize: "It's close to a trillion dollars all in, for customer service. And there's no barriers to entry." Customer service is 1% of the world economy. A trillion-dollar market sitting on top of software AI can already use. Then Musk drew the conclusion every CFO will eventually face: "You can immediately say, 'We'll outsource it for a fraction of the cost,' and there's no integration needed." The first wave will not be a smarter AI. It will be the same AI doing the same desktop work cheaper. Musk, looking at the digital human emulator about to ship: "You basically have access to trillions of dollars of revenue." Which $100M cost center in your business has zero integration moat? P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers. 5,000+ downloads. 113 five-star reviews. Grab a free copy here: If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Elon Musk ( Elon Musk ), CEO of Tesla and SpaceX, on Dwarkesh Patel's ( Dwarkesh Patel ) podcast$

Elon Musk says AI will gut customer service — a $1 trillion industry — in months. Musk laid out the path AI takes to eat industry after industry. "If you have to integrate with the APIs of existing corporations — many of which don't even have an API, so you've got to make one, and you've got to wade through legacy software — that's extremely slow." Then he picked the industry that skips the slow part. "Take something as simple as customer service." Customer service is a human at a desktop, clicking through software the company already runs. "If AI can simply take whatever is given to the outsourced customer service company that they already use and do customer service using the apps that they already use, then you can make tremendous headway." No API. No integration. No engineering quarter to coordinate. Musk, on the size of the prize: "It's close to a trillion dollars all in, for customer service. And there's no barriers to entry." Customer service is 1% of the world economy. A trillion-dollar market sitting on top of software AI can already use. Then Musk drew the conclusion every CFO will eventually face: "You can immediately say, 'We'll outsource it for a fraction of the cost,' and there's no integration needed." The first wave will not be a smarter AI. It will be the same AI doing the same desktop work cheaper. Musk, looking at the digital human emulator about to ship: "You basically have access to trillions of dollars of revenue." Which $100M cost center in your business has zero integration moat? P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers. 5,000+ downloads. 113 five-star reviews. Grab a free copy here: If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Elon Musk ( Elon Musk ), CEO of Tesla and SpaceX, on Dwarkesh Patel's ( Dwarkesh Patel ) podcast

GeniusThinking

33,183 просмотров • 2 месяцев назад

blizzard customer service just hit a new low.. 1) coffeeboy on reddit saw a sale running for a boost on the battlenet launcher, he bought it, but the sale was already over 2) coffeeboy tickets blizzard to complain 3) blizzard sends him one of the most unhinged responses in recent memory players have complained recently about "ai responses" and missing the days when you could talk to an actual human.. is this the new way to make their AI responses seem more human? what's going on here

blizzard customer service just hit a new low.. 1) coffeeboy on reddit saw a sale running for a boost on the battlenet launcher, he bought it, but the sale was already over 2) coffeeboy tickets blizzard to complain 3) blizzard sends him one of the most unhinged responses in recent memory players have complained recently about "ai responses" and missing the days when you could talk to an actual human.. is this the new way to make their AI responses seem more human? what's going on here

NoHitJerome

14,552 просмотров • 2 месяцев назад

SaaS is dead. Long live SaaS. In the age of AI and agents, most software companies are still clinging too tightly to their past. Their demise will come quick if they do not embrace and build the technologies that are destined to disrupt them. However those who have already done so must recognize the reality of the world we still live in today. That world still has the majority of its work done by humans who still need workflow tools and technology to get their work done. While we pivoted hard to AI and agents with Fin, which will become the majority of our business quite soon, we recognize the importance of the software our customers use and so have also decided to double down on our investment in Intercom, the world's best help desk and customer service system for how customer service is done at this age. We've reinvented it and rebuilt it to work perfectly alongside your new customer agent systems and we're announcing 60+ new customer features that comprise Intercom 2 today. intercom DOT com SLASH intercom2

SaaS is dead. Long live SaaS. In the age of AI and agents, most software companies are still clinging too tightly to their past. Their demise will come quick if they do not embrace and build the technologies that are destined to disrupt them. However those who have already done so must recognize the reality of the world we still live in today. That world still has the majority of its work done by humans who still need workflow tools and technology to get their work done. While we pivoted hard to AI and agents with Fin, which will become the majority of our business quite soon, we recognize the importance of the software our customers use and so have also decided to double down on our investment in Intercom, the world's best help desk and customer service system for how customer service is done at this age. We've reinvented it and rebuilt it to work perfectly alongside your new customer agent systems and we're announcing 60+ new customer features that comprise Intercom 2 today. intercom DOT com SLASH intercom2

Eoghan McCabe

39,275 просмотров • 3 месяцев назад

In a world where we hear all sorts of scams, one savvy customer recently unlocked a new level of scamming genius that would make even the most hardened con artist chuckle. Alan Goh, the owner of Tonda Pizza Romana in Taman Desa, found himself the unwitting star of an unusual complaint that could only be described in one way: “when AI meets pizza.” The trouble began when an unhappy customer lodged a complaint through GrabFood, claiming their pizza had arrived completely raw. As Goh reviewed the photos submitted by the customer, he couldn’t help but raise an eyebrow. “The dough and the ingredients looked completely raw,” said the 35-year-old restaurant owner. “It’s impossible and troublesome for us to place raw dough and ingredients in a box to send to a customer,” he added.

In a world where we hear all sorts of scams, one savvy customer recently unlocked a new level of scamming genius that would make even the most hardened con artist chuckle. Alan Goh, the owner of Tonda Pizza Romana in Taman Desa, found himself the unwitting star of an unusual complaint that could only be described in one way: “when AI meets pizza.” The trouble began when an unhappy customer lodged a complaint through GrabFood, claiming their pizza had arrived completely raw. As Goh reviewed the photos submitted by the customer, he couldn’t help but raise an eyebrow. “The dough and the ingredients looked completely raw,” said the 35-year-old restaurant owner. “It’s impossible and troublesome for us to place raw dough and ingredients in a box to send to a customer,” he added.

Malay Mail

30,235 просмотров • 2 месяцев назад

There are more than 500 companies building things with AI for enterprises. I'm the only one here on X who made a list of all of them at Here I sit down with the founder of one of them, AptEdge.io. It builds systems for customer support for companies, particularly if your service reps have to help with technical information, like helping fix a machine. I'm trying to help people learn about companies that could help their businesses and also give everyone some real insights into how people are using AI to do new things. Thanks Aakrit Prasad for such an insightful interview and for visiting my home to do so.

There are more than 500 companies building things with AI for enterprises. I'm the only one here on X who made a list of all of them at Here I sit down with the founder of one of them, AptEdge.io. It builds systems for customer support for companies, particularly if your service reps have to help with technical information, like helping fix a machine. I'm trying to help people learn about companies that could help their businesses and also give everyone some real insights into how people are using AI to do new things. Thanks Aakrit Prasad for such an insightful interview and for visiting my home to do so.

Robert Scoble

52,868 просмотров • 2 лет назад

Mark Zuckerberg just argued that AI will force companies to hire more people. Not fewer. Three and a half billion people use Meta every day. Not one of them has a phone number to call. Mark Zuckerberg: “It’s clearly just going to automate jobs and like all these jobs are going to go away… that has not really been how the history of technology has worked.” The entire media cycle runs the same story. AI replaces workers. Industries hollow out. The human becomes unnecessary. History has never once cooperated. Voice support for 3.5 billion daily users costs between ten and twenty billion dollars a year. The math made it untouchable. So Meta never built it. AI changed the math. Zuckerberg: “Let’s say the AI can handle 90 percent of that… you’ve gotten the cost of providing that service down to one 10th.” A service that could not exist becomes standard. Overnight. The moment it goes live, the edge cases arrive. The escalations. The problems no model can close alone. Every one needs a human on the other end. Zuckerberg: “I actually think we’re probably going to go hire more customer support people.” The AI did not kill the jobs. It unlocked a service so vast the company now needs people it never would have hired. When execution costs crater, companies do not pocket the savings. They go after problems they could never afford to touch. New markets. New products. New services that were economically impossible twelve months ago. Every one creates roles that did not exist before the machine arrived. The people terrified of automation are tracking the wrong number. They count the jobs that disappear. They have no framework for the ones that haven’t been invented yet.

Mark Zuckerberg just argued that AI will force companies to hire more people. Not fewer. Three and a half billion people use Meta every day. Not one of them has a phone number to call. Mark Zuckerberg: “It’s clearly just going to automate jobs and like all these jobs are going to go away… that has not really been how the history of technology has worked.” The entire media cycle runs the same story. AI replaces workers. Industries hollow out. The human becomes unnecessary. History has never once cooperated. Voice support for 3.5 billion daily users costs between ten and twenty billion dollars a year. The math made it untouchable. So Meta never built it. AI changed the math. Zuckerberg: “Let’s say the AI can handle 90 percent of that… you’ve gotten the cost of providing that service down to one 10th.” A service that could not exist becomes standard. Overnight. The moment it goes live, the edge cases arrive. The escalations. The problems no model can close alone. Every one needs a human on the other end. Zuckerberg: “I actually think we’re probably going to go hire more customer support people.” The AI did not kill the jobs. It unlocked a service so vast the company now needs people it never would have hired. When execution costs crater, companies do not pocket the savings. They go after problems they could never afford to touch. New markets. New products. New services that were economically impossible twelve months ago. Every one creates roles that did not exist before the machine arrived. The people terrified of automation are tracking the wrong number. They count the jobs that disappear. They have no framework for the ones that haven’t been invented yet.

Dustin

369,810 просмотров • 4 месяцев назад

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!

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 просмотров • 1 месяц назад

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.

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 просмотров • 2 месяцев назад

$Elon Musk explains the trillion dollar opportunity in AI The idea is simple: “As soon as you unlock digital human, you basically have access to trillions of dollars in revenue” The most valuable companies on Earth (Apple, Google, Microsoft) all produce digital output. So if you have a human emulator you can basically create one of the most valuable companies in the world overnight The simple entry point is a customer service It's 1% of the entire global economy. Most companies already outsource it. There's no complex API integration needed - AI just steps in, does the job better, and charges a fraction of the cost The companies that figure this out first won't just win a market, they'll rewrite the rules of what a company even is$

Elon Musk explains the trillion dollar opportunity in AI The idea is simple: “As soon as you unlock digital human, you basically have access to trillions of dollars in revenue” The most valuable companies on Earth (Apple, Google, Microsoft) all produce digital output. So if you have a human emulator you can basically create one of the most valuable companies in the world overnight The simple entry point is a customer service It's 1% of the entire global economy. Most companies already outsource it. There's no complex API integration needed - AI just steps in, does the job better, and charges a fraction of the cost The companies that figure this out first won't just win a market, they'll rewrite the rules of what a company even is

X Freeze

171,720 просмотров • 4 месяцев назад

AMD CEO Lisa Su just doubled her own CPU forecast in 6 months and the reason is one word, agents (Save this). In November, she told investors the CPU market would grow 18-20% per year through 2030. she just revised that number : "That rate is doubling. The CPU market may be over $120 billion by 2030, growing at over 35%." And here's what changed, six months ago AMD modeled 1 CPU for every 4 to 8 GPUs in an AI data center and now the new ratio is closer to 1 to 1. Because agents aren't passive and they don't wait for a question. They work constantly on behalf of every employee, every customer, every process running inside an enterprise. Each of us could have 2, 3, even 5 or 10 agents running at the same time and that's not just more GPU compute but rather a completely different CPU demand curve that nobody had modeled at this scale. And the earnings backed it up. AMD posted $10.3 billion in revenue for Q1, up 38% year over year. Data center revenue hit $5.8 billion, up 57%, free cash flow tripled to a record $2.6 billion and Q2 guidance came in at $11.2 billion, 46% growth year-over-year. The Milk Road Pro portfolio was built around exactly this thesis, that agentic AI would create a massive, underappreciated CPU demand cycle that the market was sleeping on. That position is now up nearly 100%, if you want the full thesis, check out Milk Road Pro, link in bio.

AMD CEO Lisa Su just doubled her own CPU forecast in 6 months and the reason is one word, agents (Save this). In November, she told investors the CPU market would grow 18-20% per year through 2030. she just revised that number : "That rate is doubling. The CPU market may be over $120 billion by 2030, growing at over 35%." And here's what changed, six months ago AMD modeled 1 CPU for every 4 to 8 GPUs in an AI data center and now the new ratio is closer to 1 to 1. Because agents aren't passive and they don't wait for a question. They work constantly on behalf of every employee, every customer, every process running inside an enterprise. Each of us could have 2, 3, even 5 or 10 agents running at the same time and that's not just more GPU compute but rather a completely different CPU demand curve that nobody had modeled at this scale. And the earnings backed it up. AMD posted $10.3 billion in revenue for Q1, up 38% year over year. Data center revenue hit $5.8 billion, up 57%, free cash flow tripled to a record $2.6 billion and Q2 guidance came in at $11.2 billion, 46% growth year-over-year. The Milk Road Pro portfolio was built around exactly this thesis, that agentic AI would create a massive, underappreciated CPU demand cycle that the market was sleeping on. That position is now up nearly 100%, if you want the full thesis, check out Milk Road Pro, link in bio.

Milk Road AI

13,241 просмотров • 2 месяцев назад

Replit CEO Amjad Masad on why the most ambitious employees are no longer blocked by engineering: "How can I upgrade my workforce for them to become generalist business people that can wield AI for the benefit of our customers and our bottom line?" "The most ambitious people are creating billions of dollars of value of their company." "There are people that are closest to the customer that feel that they have ideas that could make more revenue for the business, but they're often blocked by engineering." "Now a lot of them are bringing in Replit to work. They're building that idea, and they're making the money for their company. And then they're getting promoted, and then given more power." "I'm going to build a team of vibe coders that are going to go around the company and find all the inefficiencies and go solve them." "So we have a new role of this generalist automator." Amjad Masad with

Replit CEO Amjad Masad on why the most ambitious employees are no longer blocked by engineering: "How can I upgrade my workforce for them to become generalist business people that can wield AI for the benefit of our customers and our bottom line?" "The most ambitious people are creating billions of dollars of value of their company." "There are people that are closest to the customer that feel that they have ideas that could make more revenue for the business, but they're often blocked by engineering." "Now a lot of them are bringing in Replit to work. They're building that idea, and they're making the money for their company. And then they're getting promoted, and then given more power." "I'm going to build a team of vibe coders that are going to go around the company and find all the inefficiencies and go solve them." "So we have a new role of this generalist automator." Amjad Masad with

a16z

94,601 просмотров • 5 месяцев назад

I can’t forget the visualization below of the scale of a billion dollars. It is why I laugh when the number is carelessly thrown around by Nigerians. According to Mark Cuban, getting wealth at this scale is more about luck than skill or knowledge. You can’t get it linearly, only from exponential growth. The question we should ask if we seriously want to get wealth at this level is how many opportunities are open to an African to create exponential growth in any offering? In America and elsewhere, the market rewards people for creating that level of growth largely through valuation of future prospects and the probability of gaining more as exponential growth continues. We got a brief taste of those valuations in tech simply because foreign money decided to make some bets on entities that are still legally in their own country but operating in Africa. The way those companies are currently valued gives an idea of what they think about our markets but it is not always the reality on ground. The most objective measure is income and we are now slowly proving that previous valuations make sense. Getting to the point of creating individual billionaires is still far away unless we unlock further exponential growth opportunities. Speculation won’t create more billionaires. The government route has largely helped to create our current dollar billionaires by giving them advantage in areas where exponential demand was present. Those areas still have opportunities for growth. Someone sent me a deck and said he was trying to raise $10 Billion dollars, after laughing, I started to seriously look at the opportunities in the sector. The conclusion I reached with my cousin was that the only way to invest money and make money at that scale is to be in bed with the government. This is the bitter reality of Nigeria.

I can’t forget the visualization below of the scale of a billion dollars. It is why I laugh when the number is carelessly thrown around by Nigerians. According to Mark Cuban, getting wealth at this scale is more about luck than skill or knowledge. You can’t get it linearly, only from exponential growth. The question we should ask if we seriously want to get wealth at this level is how many opportunities are open to an African to create exponential growth in any offering? In America and elsewhere, the market rewards people for creating that level of growth largely through valuation of future prospects and the probability of gaining more as exponential growth continues. We got a brief taste of those valuations in tech simply because foreign money decided to make some bets on entities that are still legally in their own country but operating in Africa. The way those companies are currently valued gives an idea of what they think about our markets but it is not always the reality on ground. The most objective measure is income and we are now slowly proving that previous valuations make sense. Getting to the point of creating individual billionaires is still far away unless we unlock further exponential growth opportunities. Speculation won’t create more billionaires. The government route has largely helped to create our current dollar billionaires by giving them advantage in areas where exponential demand was present. Those areas still have opportunities for growth. Someone sent me a deck and said he was trying to raise $10 Billion dollars, after laughing, I started to seriously look at the opportunities in the sector. The conclusion I reached with my cousin was that the only way to invest money and make money at that scale is to be in bed with the government. This is the bitter reality of Nigeria.

Osaretin Victor Asemota

77,890 просмотров • 11 месяцев назад

Customer service is one of the most underfunded parts of most businesses, even as leaders call it high stakes. In our latest episode of Bit by Bit: Leadership Conversations, I sat down with CTO Adrian McDermott (Adrian McDermott) at Zendesk's Relate conference in Denver to talk about why that gap exists and what closing it actually requires. Adrian makes the case that automating service does not reduce contact volume, it grows it, because there is a backlog of customers who gave up before they ever reached you. He calls that the service debt. We get into Jevons paradox, the economics of AI-powered service, what the job of a human agent looks like when tier zero and tier one are automated, and why the “fire 70% of your team” math does not hold up. The conversation ends on something I do not hear enough: stop optimizing for efficiency and start designing for value. 00:12 Introductions 00:54 Saying vs Doing In Customer Service 04:12 Service Debt 07:40 Tiers of Customer Interactions 16:03 Targeting High Value Interactions 19:11 The Service "Architect" 25:15 Focusing On Value Not Simply Efficiency 30:22 Adapting To Change 37:46 Conclusion

Customer service is one of the most underfunded parts of most businesses, even as leaders call it high stakes. In our latest episode of Bit by Bit: Leadership Conversations, I sat down with CTO Adrian McDermott (Adrian McDermott) at Zendesk's Relate conference in Denver to talk about why that gap exists and what closing it actually requires. Adrian makes the case that automating service does not reduce contact volume, it grows it, because there is a backlog of customers who gave up before they ever reached you. He calls that the service debt. We get into Jevons paradox, the economics of AI-powered service, what the job of a human agent looks like when tier zero and tier one are automated, and why the “fire 70% of your team” math does not hold up. The conversation ends on something I do not hear enough: stop optimizing for efficiency and start designing for value. 00:12 Introductions 00:54 Saying vs Doing In Customer Service 04:12 Service Debt 07:40 Tiers of Customer Interactions 16:03 Targeting High Value Interactions 19:11 The Service "Architect" 25:15 Focusing On Value Not Simply Efficiency 30:22 Adapting To Change 37:46 Conclusion

Carolina Milanesi

597,648 просмотров • 2 месяцев назад

Greg Brockman, President of OpenAI, said there is not enough compute in the world to satisfy AI demand, and OpenAI itself cannot launch products it has already built because it cannot find the infrastructure to run them (Save this). OpenAI is spending $50 billion on compute in 2026 alone and it still is not enough. That is the setup but here is the trade. Nebius is one of the most asymmetric infrastructure plays in public markets right now, and most people have never heard of it. Q1 2026 revenue came in at $399 million, up 684% year over year, with AI cloud revenue specifically growing 841% in a single quarter. The company entered 2026 with an exit ARR of $1.25 billion and is targeting $7 to $9 billion by year end, a number that would make it one of the fastest revenue ramps in the history of public infrastructure companies. The contracted backlog sits at $50 billion anchored by a $17.4 billion agreement with Microsoft through 2031 and a $27 billion five-year deal with Meta. They are decade-scale infrastructure commitments from the two largest enterprise AI spenders on earth, signed before the demand curve has even reached its steepest point. Nvidia took a direct equity stake in Nebius, one of only two neoclouds it has invested in alongside CoreWeave. That relationship is not just financial but rather means Nebius gets preferential access to GPU allocation at a moment when every lab and every hyperscaler is competing for the same constrained supply. Contracted power capacity now exceeds 3.5 gigawatts, with expansion plans targeting 5 to 6 GW by mid-2029. And power is the other binding constraint in AI infrastructure, you cannot build a data center without it and Nebius has already secured the capacity that competitors are still fighting to acquire. At full ramp, analysts project revenue in the $15 to $25 billion range by 2029, against a current market cap the contracted backlog alone already dwarfs. Come join Milk Road Pro and get our full Nebius deep-dive, the exact price levels we are watching, how we are sizing the position against the backlog and power capacity timeline, and our full AI thesis. link below!

Greg Brockman, President of OpenAI, said there is not enough compute in the world to satisfy AI demand, and OpenAI itself cannot launch products it has already built because it cannot find the infrastructure to run them (Save this). OpenAI is spending $50 billion on compute in 2026 alone and it still is not enough. That is the setup but here is the trade. Nebius is one of the most asymmetric infrastructure plays in public markets right now, and most people have never heard of it. Q1 2026 revenue came in at $399 million, up 684% year over year, with AI cloud revenue specifically growing 841% in a single quarter. The company entered 2026 with an exit ARR of $1.25 billion and is targeting $7 to $9 billion by year end, a number that would make it one of the fastest revenue ramps in the history of public infrastructure companies. The contracted backlog sits at $50 billion anchored by a $17.4 billion agreement with Microsoft through 2031 and a $27 billion five-year deal with Meta. They are decade-scale infrastructure commitments from the two largest enterprise AI spenders on earth, signed before the demand curve has even reached its steepest point. Nvidia took a direct equity stake in Nebius, one of only two neoclouds it has invested in alongside CoreWeave. That relationship is not just financial but rather means Nebius gets preferential access to GPU allocation at a moment when every lab and every hyperscaler is competing for the same constrained supply. Contracted power capacity now exceeds 3.5 gigawatts, with expansion plans targeting 5 to 6 GW by mid-2029. And power is the other binding constraint in AI infrastructure, you cannot build a data center without it and Nebius has already secured the capacity that competitors are still fighting to acquire. At full ramp, analysts project revenue in the $15 to $25 billion range by 2029, against a current market cap the contracted backlog alone already dwarfs. Come join Milk Road Pro and get our full Nebius deep-dive, the exact price levels we are watching, how we are sizing the position against the backlog and power capacity timeline, and our full AI thesis. link below!

Milk Road AI

14,578 просмотров • 1 месяц назад

The selloff in Micron is one of the best buying opportunities you'll see this year (Save this). Sanjay Mehrotra just explained exactly why the old mental model for Micron, cyclical, commodity, mean reverting no longer applies. Every AI system, regardless of what device it runs on, requires more memory at higher performance to unlock its full potential. From data centers to smartphones to autonomous vehicles, memory is no longer a supporting actor but rather the critical bottleneck determining how fast AI can move. What makes this cycle structurally different starts with what happened in 2023. Certain customers drove industry pricing to one third of 2022 levels, forcing Micron into severe losses while still requiring $10 billion in investment just to stay competitive. Most companies in that situation cut spending and survive but Micron invested through the pain with the vision that the other side would be worth it. Those 2023 investments are now producing 84.9% gross margins, $41.46 billion in quarterly revenue, and Q4 guidance of $50 billion up from $11.3 billion in the same quarter just one year ago. That is what it looks like when a company bets on itself at exactly the right moment. Even Micron's own largest customers, Nvidia, Google, Amazon could not forecast the scale of AI memory demand that materialized. When the biggest technology companies in the world cannot project their own memory requirements, you are watching a structural transformation that nobody had models to predict, still in its early innings. Supply cannot respond quickly enough to close that gap. Mehrotra confirmed on air that tightness extends beyond 2027, new domestic fabs take years to bring online, and new HBM capacity which requires advanced 3D stacking that compounds in complexity at every generation won't meaningfully arrive until late 2028. There is no fast fix to a shortage of the most valuable memory on earth. The strategic customer agreements are the most underappreciated part of the entire story. Multi-year contracts with volume commitments and price floors now cover roughly 20% of DRAM volume and 30% of NAND volume, locking in a $100 billion contractual revenue base. The old Micron was at the mercy of customers who could crater prices overnight while the new Micron has contractual floors that make the 2023 scenario structurally impossible to repeat. Long Micron and make sure to follow me Melvin for more deep dives into AI and memory.

The selloff in Micron is one of the best buying opportunities you'll see this year (Save this). Sanjay Mehrotra just explained exactly why the old mental model for Micron, cyclical, commodity, mean reverting no longer applies. Every AI system, regardless of what device it runs on, requires more memory at higher performance to unlock its full potential. From data centers to smartphones to autonomous vehicles, memory is no longer a supporting actor but rather the critical bottleneck determining how fast AI can move. What makes this cycle structurally different starts with what happened in 2023. Certain customers drove industry pricing to one third of 2022 levels, forcing Micron into severe losses while still requiring $10 billion in investment just to stay competitive. Most companies in that situation cut spending and survive but Micron invested through the pain with the vision that the other side would be worth it. Those 2023 investments are now producing 84.9% gross margins, $41.46 billion in quarterly revenue, and Q4 guidance of $50 billion up from $11.3 billion in the same quarter just one year ago. That is what it looks like when a company bets on itself at exactly the right moment. Even Micron's own largest customers, Nvidia, Google, Amazon could not forecast the scale of AI memory demand that materialized. When the biggest technology companies in the world cannot project their own memory requirements, you are watching a structural transformation that nobody had models to predict, still in its early innings. Supply cannot respond quickly enough to close that gap. Mehrotra confirmed on air that tightness extends beyond 2027, new domestic fabs take years to bring online, and new HBM capacity which requires advanced 3D stacking that compounds in complexity at every generation won't meaningfully arrive until late 2028. There is no fast fix to a shortage of the most valuable memory on earth. The strategic customer agreements are the most underappreciated part of the entire story. Multi-year contracts with volume commitments and price floors now cover roughly 20% of DRAM volume and 30% of NAND volume, locking in a $100 billion contractual revenue base. The old Micron was at the mercy of customers who could crater prices overnight while the new Micron has contractual floors that make the 2023 scenario structurally impossible to repeat. Long Micron and make sure to follow me Melvin for more deep dives into AI and memory.

Melvin

130,037 просмотров • 1 месяц назад

AI Digest No.67: When Students Rebel and Nvidia Surges Here are five stories shaping this week in AI: 💰 Nvidia Smashes a $57B Revenue Record Nvidia posted fifty-seven billion dollars in quarterly revenue, calming fears of an AI bubble and reinforcing its grip on the global compute market. ⚖️ Trump Moves to Block State-Level AI Laws A draft executive order would prevent states from creating their own AI regulations, centralizing power at the federal level and shifting the landscape of AI governance. 🎓 Students Revolt Against AI-Generated University Courses A master’s program using AI for lectures and assessments without disclosure triggered protests and refund demands — raising new questions about trust in higher education. 🛑 A Universal AI Jailbreak Is Exposed Researchers discovered a prompt pattern that bypasses safety systems across nearly all major AI models. Experts say it’s a wake-up call for the entire industry. 📉 AI Could Replace 3 Million UK Jobs A major report warns that low-skill sectors like retail, food service, and transport face severe automation risk unless governments act now. Watch our new AI video edition and read the full digest on Medium

AI Digest No.67: When Students Rebel and Nvidia Surges Here are five stories shaping this week in AI: 💰 Nvidia Smashes a $57B Revenue Record Nvidia posted fifty-seven billion dollars in quarterly revenue, calming fears of an AI bubble and reinforcing its grip on the global compute market. ⚖️ Trump Moves to Block State-Level AI Laws A draft executive order would prevent states from creating their own AI regulations, centralizing power at the federal level and shifting the landscape of AI governance. 🎓 Students Revolt Against AI-Generated University Courses A master’s program using AI for lectures and assessments without disclosure triggered protests and refund demands — raising new questions about trust in higher education. 🛑 A Universal AI Jailbreak Is Exposed Researchers discovered a prompt pattern that bypasses safety systems across nearly all major AI models. Experts say it’s a wake-up call for the entire industry. 📉 AI Could Replace 3 Million UK Jobs A major report warns that low-skill sectors like retail, food service, and transport face severe automation risk unless governments act now. Watch our new AI video edition and read the full digest on Medium

GT Protocol

38,046 просмотров • 8 месяцев назад

SITUATION EXPLAINED: Dwarkesh argues compute could get 10X more expensive. • Anthropic's revenue has been 10X-ing year over year while lab compute only 3X's • Three ways that gap can close: margins rise, compute gets more expensive, or labs shift compute to inference • All three are already happening, Anthropic went from 40% margins in 2025 to possibly 80%+ this year • But labs don't want the third one, heavy inference spend signals AI progress has stalled and you're now a cloud provider • Spot compute prices are up 40%+ since February, and labs pay well above spot for security and scale • Google is reportedly paying SpaceXAI $900 million a month for 110K GPUs, roughly 2X spot • Key claim: if a human-level software engineer ran on an H100, that H100 should rent for $250K a year, 15X today's price • The 3X annual compute growth is 1.4X Moore's Law, 1.2X new fabs, and 1.8X from AI taking wafer allocation from other devices • The fab piece is bottlenecked by EUV tool supply through 2030, and the wafer piece hits a wall by end of 2027 • If compute stays scarce, new labs need far more capital just to reach the same starting line • Compute gets cheap again only once robots can turn sand and copper into computers, which is gated on robotics, not RSI sof 𓋹: "This is what I'm most concerned about, a lack of innovation, not within companies, but a lack of new companies that can actually do something substantially new, because of the scarcity of compute." Theo Jaffee: "The biggest companies in the world by revenue are Amazon and Walmart, at 743 billion and 725 billion. If Anthropic makes 100 billion by the end of this year, that puts them at Target. To go from Target to bigger than Walmart in a year would be very impressive."

SITUATION EXPLAINED: Dwarkesh argues compute could get 10X more expensive. • Anthropic's revenue has been 10X-ing year over year while lab compute only 3X's • Three ways that gap can close: margins rise, compute gets more expensive, or labs shift compute to inference • All three are already happening, Anthropic went from 40% margins in 2025 to possibly 80%+ this year • But labs don't want the third one, heavy inference spend signals AI progress has stalled and you're now a cloud provider • Spot compute prices are up 40%+ since February, and labs pay well above spot for security and scale • Google is reportedly paying SpaceXAI $900 million a month for 110K GPUs, roughly 2X spot • Key claim: if a human-level software engineer ran on an H100, that H100 should rent for $250K a year, 15X today's price • The 3X annual compute growth is 1.4X Moore's Law, 1.2X new fabs, and 1.8X from AI taking wafer allocation from other devices • The fab piece is bottlenecked by EUV tool supply through 2030, and the wafer piece hits a wall by end of 2027 • If compute stays scarce, new labs need far more capital just to reach the same starting line • Compute gets cheap again only once robots can turn sand and copper into computers, which is gated on robotics, not RSI sof 𓋹: "This is what I'm most concerned about, a lack of innovation, not within companies, but a lack of new companies that can actually do something substantially new, because of the scarcity of compute." Theo Jaffee: "The biggest companies in the world by revenue are Amazon and Walmart, at 743 billion and 725 billion. If Anthropic makes 100 billion by the end of this year, that puts them at Target. To go from Target to bigger than Walmart in a year would be very impressive."

MTS

10,617 просмотров • 4 дней назад