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Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration...

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Patrick OShaughnessy

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Founding Partner of Marathon Management Holdings Gokul Rajaram, who sits on the board for $COIN, $PINS, and $TTD, tells us why all software is vulnerable if companies fail to innovate, but it depends on the time horizon: "The market, right now is behaving in a way that it's treating all software companies the same with their respective moats" Gokul Rajaram believes that: "All software is vulnerable if companies fail to innovate, the difference is more in terms of time horizons". He categorises software into two types of companies: 1. Utility is tied to the outcome 2. Utility is tied to the data (systems of record) "Overall, systems of record [Salesforce] have a longer time horizon while things that are purely based on outcomes [Zendesk]...those are going to be the ones that are going to have to face a nearer-term reckoning"
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Founding Partner of Marathon Management Holdings Gokul Rajaram, who sits on the board for $COIN, $PINS, and $TTD, tells us why all software is vulnerable if companies fail to innovate, but it depends on the time horizon: "The market, right now is behaving in a way that it's treating all software companies the same with their respective moats" Gokul Rajaram believes that: "All software is vulnerable if companies fail to innovate, the difference is more in terms of time horizons". He categorises software into two types of companies: 1. Utility is tied to the outcome 2. Utility is tied to the data (systems of record) "Overall, systems of record [Salesforce] have a longer time horizon while things that are purely based on outcomes [Zendesk]...those are going to be the ones that are going to have to face a nearer-term reckoning"

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Sequoia's Pat Grady says systems of record software companies are relatively safe from AI disruption, but that systems of engagement companies built on top of them are in trouble: "The first wave of the on-prem to cloud transition was transitioning systems of record — the Workdays, Salesforces, and Servicenows of the world." "The second layer on top of that was the systems of engagement. The systems of record might own the core database, but then there are a bunch of different workflow applications that reside on top." "I think what we're going to see with the wave of AI software is a third layer on top of those." "It's the layer that does the work. It's the agents getting deployed. They may or may not need those workflows beneath them, but certainly need access to everything that's sitting in that system of record." "As a result, I think those systems of record companies are relatively safe. They may not catch a lot of net new workloads, because those might go to the AI native companies. But I think overall they're pretty safe." "I think some of those workflow-based companies in the middle are in trouble, because they're neither the systems of record nor the agentic capability that's getting deployed. So they'll have to figure out how to become like that agent harness, so to speak, for whatever job needs to be done."

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Y Combinator

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Brian sits on the board of Y Combinator. He said the last batch had 175 companies and only 16 of them weren't enterprise. "Here are the reasons I think it's happening. Number one, when ChatGPT came out, people were afraid it was going to kill their business. Number two, the business model is tricky. There is no consumer business model for AI that I've seen. For example, ChatGPT, there's three ways it can monetize subscriptions. Unfortunately, they're probably going to hit a local maximum percentage of users. Ads, they're hitting a local maximum because Claude and Gemini are not going to do ads. And e-commerce, they shut down the third party apps. And so the first thing is you need to have a business model around consumer AI. People are not trained to pay for information. The second problem is distribution is mature. Like the app store. Now again, top three apps in the app Store are AI, so it does prove you have something revolutionary, you'll find your way to the top. The third thing is, while I think Silicon Valley, we like to describe ourselves as rebels. I think it's very trend based and vibe based. And I think the trend is enterprise. Maybe finally the reason people aren't doing consumer companies is that they're just harder. You have to be good at a lot more things. You generally have to be better at design, marketing, culture, press. It's not purely technology and sales. But my prediction is that we're living in the age of enterprise AI, and I think in the next 12 to 24 months you're gonna see the beginning of a consumer AI renaissance. Almost every app on my home screen has not changed since AI, including Airbnb. I think that's gonna change in two years."
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0:59

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)

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Marc Benioff just exposed the biggest hypocrisy in the AI boom. The companies building the AI that’s supposed to kill software are some of Salesforce’s largest customers. Benioff: “The AI companies love our products and they can’t buy enough of them. They’re some of our largest customers now: Anthropic, OpenAI, Google, Amazon, you name it.” Let that land. The most advanced AI labs on earth. The companies with more engineering talent and compute than anyone. The ones building the technology that analysts say will make traditional software obsolete. Still buying traditional software. At scale. Benioff: “No one has a company that’s running entirely on a large language model because it’s not real.” Not because they haven’t tried. Because an LLM is not a foundation. It’s a feature. Benioff: “Yeah, Minority Report, I watched the movie. Great guys, fantastic. But I’m in the present-moment reality right now. We’re living in this world. This is 2026.” The analysts writing reports about fully autonomous AI companies have never had to run one. Benioff is running one of the largest enterprise software companies on earth. The gap between those two perspectives is where billions of dollars are being misallocated. Benioff: “How are we doing our financials, our HR, our customer information? How are we doing all of these aspects of our business?” A neural network that hallucinates cannot execute a financial transaction that has to be right every single time. Cannot secure customer data with zero tolerance for error. Cannot provide the determinism that every real business runs on. Benioff: “We need the determinism, and the programmability, and the security, and the sharing.” AI doesn’t replace those requirements. It sits on top of them. Benioff: “I think the software industry is going to be bigger and broader and do more this year than ever before.” The future isn’t AI replacing software. It’s AI making software exponentially more powerful. The smartest people building the future already know this. They’re the ones still buying the software.

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1:58

No longer a ‘conspiracy theory’ Online retailers are accessing your data and then adjusting prices for items based on that data Airlines are being caught adjusting prices based on data too “This holiday season, you and your neighbor could be shopping for the exact same item and pay different prices — surveillance pricing is and how your personal data plays a role. You may be shopping for gifts from the comfort of your couch, but stores can still see who you are. Most of these companies are charging you a different price based on some data they have about you. It could be your location, it could be your proximity to the store, it could be your shopping history, knowing that you bought a product previously and are willing pay for it again.” “Consumer watchdog president Jamie Court says surveillance pricing is nearly impossible to avoid — Court says his organization has evidence all the major retailers use personal algorithms to nudge a price up or down, targeting data from your Amazon cart, Uber rides, or even the brand of laptop you're using to shop” “For those traveling for the holidays. Airlines will charge you a different price depending upon how many times you go to the website. “Currently, there is no federal regulation for companies that use surveillance pricing. We reached out to the FTC”

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Freelance work is being hit especially hard by AI, according to Revelio Labs. CEO Ben Zweig says the most exposed work is transactional and task-based. For example, companies that once hired someone on Upwork to build a one-off website can now often do it themselves with AI.

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Michigan Senator Mallory McMorrow reveals companies are using dynamic algorithmic pricing to charge Americans more based on your data She says this is even being used to increase rents - A $240 flight to Florida increased to $423 because you looked twice - Uber pricing doubled because phone battery was below 20% “Usually it's about 18 bucks, but oh, the app knows that your phone battery is low, so it's double the price. Companies are using algorithmic pricing tools to study your personal data and charge you exactly what they think you'll pay. This is called surveillance pricing, and it means that companies can charge you completely different prices for the exact same thing, and it's not just happening to consumers. Some companies are now using similar algorithms to track workers and decide their wages, hours, and schedules, even their locations — My plan bans companies from setting personalized prices based on your data — It protects all workers, including gig workers and contractors from algorithmic wage discrimination, and it cracks down on companies using these systems to coordinate things like higher rents and insurance premiums, because the economy shouldn't be a rigged game.”

Wall Street Apes

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Mark Cuban just told every software company on Earth they’re already dead. The people inside them are still building roadmaps. Cuban: “Software is dead because everything’s going to be customized to your unique utilization.” Every SaaS company was built on one bet. Software stays rigid. Humans stay adaptable. You learn the tool. You bend to it. You pay for someone else’s version of your solution. AI just inverted that. The tool bends to you or it dies. Cuban: “33 million companies aren’t going to have AI budgets, aren’t going to have AI experts.” 33 million businesses feel something shifting beneath them. None can name it. The distance between what AI can do and what small companies can access is the most mispriced gap in markets today. Not a technology problem. A translation problem. Cuban: “Learn all you can about AI but learn more on how to implement them in companies.” Everyone is racing to build intelligence. Almost nobody is racing to deploy it where the pain is deepest. The person who walks into a 40-person company and rewires their entire operation captures more value than the team that trained the model. Understanding pain is now worth more than building intelligence. Cuban: “Every single job available for kids coming out of school because every single company needs that.” The most important career of the next decade has no title. No degree path. No university knows it needs to exist yet. It belongs to whoever learns two languages fluently. The language of a business that can’t articulate what’s breaking. And the language of an AI that doesn’t know where to aim. 33 million companies. Zero translators. Whoever arrives first doesn’t enter a market. They create one.

Dustin

473,667 Aufrufe • vor 1 Monat

Just watched Kai-Fu Lee 's interview on the US-China AI landscape. A few takeaways felt especially useful✌️: 1. The US is currently about 15 months ahead of China in frontier AI models. 2. Top US AI companies are like “geniuses trying to win the Nobel Prize,” while Chinese AI companies are more like “a group of smart kids forming a study group.” They still compete, but open source creates more shared learning. 3. China’s AI companies face a real monetization challenge in enterprise software. Many Chinese companies are less used to annual subscriptions or usage-based pricing, and still tend to think of software as project-based work. 4. For the same product, Western, Middle Eastern, and Central Asian companies may be willing to pay around 8x more than Chinese companies. That helps explain why many Chinese AI companies are expanding overseas. 5. China may produce many breakthrough AI consumer apps, because Chinese companies are especially strong at consumer apps, hardware, robotics, embodied AI, and AI-first devices. 6. The US may continue to lead in frontier models, while China may move faster in turning AI into products people actually use. 7. His advice for young people in the age of AI: you don’t necessarily need to become an engineer first. What matters more is accountability, taste, conviction, courage, and foresight. More👇
17:56

Just watched Kai-Fu Lee 's interview on the US-China AI landscape. A few takeaways felt especially useful✌️: 1. The US is currently about 15 months ahead of China in frontier AI models. 2. Top US AI companies are like “geniuses trying to win the Nobel Prize,” while Chinese AI companies are more like “a group of smart kids forming a study group.” They still compete, but open source creates more shared learning. 3. China’s AI companies face a real monetization challenge in enterprise software. Many Chinese companies are less used to annual subscriptions or usage-based pricing, and still tend to think of software as project-based work. 4. For the same product, Western, Middle Eastern, and Central Asian companies may be willing to pay around 8x more than Chinese companies. That helps explain why many Chinese AI companies are expanding overseas. 5. China may produce many breakthrough AI consumer apps, because Chinese companies are especially strong at consumer apps, hardware, robotics, embodied AI, and AI-first devices. 6. The US may continue to lead in frontier models, while China may move faster in turning AI into products people actually use. 7. His advice for young people in the age of AI: you don’t necessarily need to become an engineer first. What matters more is accountability, taste, conviction, courage, and foresight. More👇

Mia Bao

44,564 Aufrufe • vor 2 Monaten

AOC: Trump has been saying the economy is booming, but it’s really only seven tech companies. They’re driving this growth in just one sector: AI. So the entire economic growth can be tracked down to seven companies and their AI expansion. Forty percent of economic growth is attributed to these companies alone, and 80% of stock gains this year came from AI companies. People are justifying these levels of investment because of the promises CEOs make — that there will be a return on that investment. For a company like OpenAI, their valuation is based on the expectation that they’re going to figure out how to make a profit off this, and they haven’t. Generating this increased human dependency that can be mined — people’s deepest fears, emotional content — can all be mined for this empty promise that we’re getting from these companies in the pursuit of profit. The reason I bring all of this up is that the exposure to this industry and this investment, I fear, has reached broad levels of the American economy. We’re talking about 40% of growth being attributed to that sector alone, and it has not turned a profit. We’re talking about a massive economic bubble. Depending on the exposure of that bubble, we could be facing 2008-style threats to economic stability.
3:29

AOC: Trump has been saying the economy is booming, but it’s really only seven tech companies. They’re driving this growth in just one sector: AI. So the entire economic growth can be tracked down to seven companies and their AI expansion. Forty percent of economic growth is attributed to these companies alone, and 80% of stock gains this year came from AI companies. People are justifying these levels of investment because of the promises CEOs make — that there will be a return on that investment. For a company like OpenAI, their valuation is based on the expectation that they’re going to figure out how to make a profit off this, and they haven’t. Generating this increased human dependency that can be mined — people’s deepest fears, emotional content — can all be mined for this empty promise that we’re getting from these companies in the pursuit of profit. The reason I bring all of this up is that the exposure to this industry and this investment, I fear, has reached broad levels of the American economy. We’re talking about 40% of growth being attributed to that sector alone, and it has not turned a profit. We’re talking about a massive economic bubble. Depending on the exposure of that bubble, we could be facing 2008-style threats to economic stability.

Acyn

1,058,002 Aufrufe • vor 9 Monaten

DeepSeek-R1 shattered the assumption that performant AI models must be built closed source with loss-leading computational costs. This is the reality that Web3 x Crypto firms have been waiting for, leading me to believe that the most performant AI models in the future will be built on-chain. Resource Requirements DeepSeek R1 (671 billion parameters), which took over a billion dollars, 2,000 Nvidia H800 GPUs, and over 55 days, beat benchmarks held by OpenAI’s o1 mode (near 2 trillion parameters)l, which required hundreds of billions of dollars to develop along with over 16,000 advanced GPUs. The idea that AI models must be closed-source and have loss-leading computational costs to succeed is crumbling. The Existing Decentralized AI Narrative AI x Crypto projects believed that crowdsourced, public, decentralized AI would eventually create better models than their centralized counterparts. This had thus far not been true, as the highest-performing models had come from closed-source companies like OpenAI and Anthropic. Crypto x AI companies have adapted to this by specializing in infrastructure rather than model-building. For example, GPU marketplaces like , The Render Network, io.net, and Exabits have developed sustainable revenues. Companies that allow users to share their network bandwidth like touch grass and Gradient have found their niche in supplying services, like distributed web scraping, to web2 clients. Storage networks like Arweave Ecosystem, Filecoin, and Ocean Protocol have also done well by being the platform on which these projects are built. Supply networks have flourished because of their ability to tailor their cheaper and more scalable services to off-chain customers. Renewed Focus Now that GPU and financial resources are no longer limitations to creating quality AI models, web3 AI companies can focus on replicating DeepSeek’s effectiveness while offering new benefits like modality, user ownership, censorship resistance, privacy, and more. Pantera Capital has funded companies in this space like and Sentient that believe they can match or exceed the performance of traditional AI companies while offering additional services or benefits. , for example, is building a platform where anyone can monetize AI models, data sets, and applications in a collaborative space. Users can permissionlessly train models manually, provide training data, and create tailored AI models with no-code tools. They are only able to cater to all these stakeholders (AI developers, users, resource providers) because everything is tied to their native Sahara blockchain. We invested in them precisely for this reason. The Future of AI will be built with Web3 Infrastructure I believe that supply-side projects will continue to grow, while consumer-facing projects can begin competing with web2 competitors by taking advantage of their ability to build networks that invite community involvement. and Sentient, for example, have begun setting up systems for users to train models based on the users’ expertise. These platforms will allow users to pick and choose the data and integrations to whatever they are applying the model towards. Sahara already has over 780,000 users on their waitlist while Sentient has over 1 million interactions. In the near future, I believe that the most performant AI models will be built on-chain. For the full blog post, read my newsletter.
0:32

DeepSeek-R1 shattered the assumption that performant AI models must be built closed source with loss-leading computational costs. This is the reality that Web3 x Crypto firms have been waiting for, leading me to believe that the most performant AI models in the future will be built on-chain. Resource Requirements DeepSeek R1 (671 billion parameters), which took over a billion dollars, 2,000 Nvidia H800 GPUs, and over 55 days, beat benchmarks held by OpenAI’s o1 mode (near 2 trillion parameters)l, which required hundreds of billions of dollars to develop along with over 16,000 advanced GPUs. The idea that AI models must be closed-source and have loss-leading computational costs to succeed is crumbling. The Existing Decentralized AI Narrative AI x Crypto projects believed that crowdsourced, public, decentralized AI would eventually create better models than their centralized counterparts. This had thus far not been true, as the highest-performing models had come from closed-source companies like OpenAI and Anthropic. Crypto x AI companies have adapted to this by specializing in infrastructure rather than model-building. For example, GPU marketplaces like , The Render Network, io.net, and Exabits have developed sustainable revenues. Companies that allow users to share their network bandwidth like touch grass and Gradient have found their niche in supplying services, like distributed web scraping, to web2 clients. Storage networks like Arweave Ecosystem, Filecoin, and Ocean Protocol have also done well by being the platform on which these projects are built. Supply networks have flourished because of their ability to tailor their cheaper and more scalable services to off-chain customers. Renewed Focus Now that GPU and financial resources are no longer limitations to creating quality AI models, web3 AI companies can focus on replicating DeepSeek’s effectiveness while offering new benefits like modality, user ownership, censorship resistance, privacy, and more. Pantera Capital has funded companies in this space like and Sentient that believe they can match or exceed the performance of traditional AI companies while offering additional services or benefits. , for example, is building a platform where anyone can monetize AI models, data sets, and applications in a collaborative space. Users can permissionlessly train models manually, provide training data, and create tailored AI models with no-code tools. They are only able to cater to all these stakeholders (AI developers, users, resource providers) because everything is tied to their native Sahara blockchain. We invested in them precisely for this reason. The Future of AI will be built with Web3 Infrastructure I believe that supply-side projects will continue to grow, while consumer-facing projects can begin competing with web2 competitors by taking advantage of their ability to build networks that invite community involvement. and Sentient, for example, have begun setting up systems for users to train models based on the users’ expertise. These platforms will allow users to pick and choose the data and integrations to whatever they are applying the model towards. Sahara already has over 780,000 users on their waitlist while Sentient has over 1 million interactions. In the near future, I believe that the most performant AI models will be built on-chain. For the full blog post, read my newsletter.

paul.nft

32,465 Aufrufe • vor 1 Jahr

This is the biggest irony in tech history. Microsoft beat revenue estimates. Stock plunged 11%, wiped out $400 BILLION in market cap. Salesforce reported growth. Stock fell 5.6%. ServiceNow beat earnings. Stock crashed 11%. SAP beat projections. Stock dropped 16%. Entire software sector entered bear market territory. Down 22% from peak. These are the companies everyone said would WIN from AI. They spent billions BUYING AI companies. ServiceNow: $7.75 billion for Armis. Salesforce: $8 billion for Informatica. They launched AI products. Built AI workflows. Hired AI teams. And the market said: You're all dead. Because investors just realized something nobody wanted to admit: AI doesn't make software companies stronger. AI makes software companies OBSOLETE. Morgan Stanley: "In an environment of heightened investor skepticism, stable growth falls short of shifting the narrative." Good earnings aren't enough anymore. The market is pricing in a world where AI replaces the software these companies sell. ServiceNow CEO tried defending on the earnings call: "AI needs workflow orchestration. ServiceNow is the gateway to this shift." Market response: 11% crash. Because here's what he didn't say: If AI can write code, automate workflows, and generate apps at a fraction of the cost, why would anyone pay $50,000 per year for enterprise software licenses? The per-seat pricing model that made SaaS companies rich is getting murdered by AI efficiency. One AI agent replaces 10 seats. One prompt replaces months of custom development. One LLM call replaces entire software categories. Klarna already proved it. CEO said they pulled Salesforce out of their stack. Built everything themselves using AI. And that's just the beginning. The software apocalypse hit hardest on companies that INVESTED IN AI: Atlassian: down 12.6% Intuit: down 7.8% HubSpot: down 11.5% Zscaler: down 6.3% Meanwhile, the companies ENABLING AI made money: Nvidia: up Semiconductor stocks: surging Memory firms: rallying The divide is brutal. Hardware companies print cash. Software companies get destroyed. Because in an AI-first world, you need GPUs to build the models. But you don't need software subscriptions when the AI builds the software for you. Jim Cramer called it the "P/E multiple compression crisis." Translation: Investors don't care about earnings anymore. They care about whether your business model survives the next 5 years. And right now software business models look doomed. They're literally stuck: If they DON'T invest in AI, they fall behind. If they DO invest in AI, they cannibalize their own products. It's a death spiral with no exit. ServiceNow spent $12 BILLION on acquisitions in 2025 alone. Trying to buy their way into relevance. And yesterday the market cooked them. The craziest thing to me tho... Most software companies beat earnings. Revenue was solid. Growth was fine. But it didn't matter. Because the market stopped pricing software on what it earns TODAY. It's pricing software on what it's worth in a world where AI does the job for free. And in that world these companies are worth nothing. This is the biggest sector repricing since 2008. $500 billion in market value gone in ONE DAY. And it's not stopping. Because every company watching this is thinking the same thing: "If I can replace ServiceNow with 3 AI agents and save $10 million per year, why wouldn't I?" The answer used to be: "Because you need enterprise-grade reliability." But now? AI agents are getting reliable. Fast. Software companies just realized they're competing with open-source models that cost $0.02 per 1,000 tokens. You can't win a pricing war against free. The companies that spent BILLIONS preparing for AI are getting killed BY AI. What an irony.
1:54

This is the biggest irony in tech history. Microsoft beat revenue estimates. Stock plunged 11%, wiped out $400 BILLION in market cap. Salesforce reported growth. Stock fell 5.6%. ServiceNow beat earnings. Stock crashed 11%. SAP beat projections. Stock dropped 16%. Entire software sector entered bear market territory. Down 22% from peak. These are the companies everyone said would WIN from AI. They spent billions BUYING AI companies. ServiceNow: $7.75 billion for Armis. Salesforce: $8 billion for Informatica. They launched AI products. Built AI workflows. Hired AI teams. And the market said: You're all dead. Because investors just realized something nobody wanted to admit: AI doesn't make software companies stronger. AI makes software companies OBSOLETE. Morgan Stanley: "In an environment of heightened investor skepticism, stable growth falls short of shifting the narrative." Good earnings aren't enough anymore. The market is pricing in a world where AI replaces the software these companies sell. ServiceNow CEO tried defending on the earnings call: "AI needs workflow orchestration. ServiceNow is the gateway to this shift." Market response: 11% crash. Because here's what he didn't say: If AI can write code, automate workflows, and generate apps at a fraction of the cost, why would anyone pay $50,000 per year for enterprise software licenses? The per-seat pricing model that made SaaS companies rich is getting murdered by AI efficiency. One AI agent replaces 10 seats. One prompt replaces months of custom development. One LLM call replaces entire software categories. Klarna already proved it. CEO said they pulled Salesforce out of their stack. Built everything themselves using AI. And that's just the beginning. The software apocalypse hit hardest on companies that INVESTED IN AI: Atlassian: down 12.6% Intuit: down 7.8% HubSpot: down 11.5% Zscaler: down 6.3% Meanwhile, the companies ENABLING AI made money: Nvidia: up Semiconductor stocks: surging Memory firms: rallying The divide is brutal. Hardware companies print cash. Software companies get destroyed. Because in an AI-first world, you need GPUs to build the models. But you don't need software subscriptions when the AI builds the software for you. Jim Cramer called it the "P/E multiple compression crisis." Translation: Investors don't care about earnings anymore. They care about whether your business model survives the next 5 years. And right now software business models look doomed. They're literally stuck: If they DON'T invest in AI, they fall behind. If they DO invest in AI, they cannibalize their own products. It's a death spiral with no exit. ServiceNow spent $12 BILLION on acquisitions in 2025 alone. Trying to buy their way into relevance. And yesterday the market cooked them. The craziest thing to me tho... Most software companies beat earnings. Revenue was solid. Growth was fine. But it didn't matter. Because the market stopped pricing software on what it earns TODAY. It's pricing software on what it's worth in a world where AI does the job for free. And in that world these companies are worth nothing. This is the biggest sector repricing since 2008. $500 billion in market value gone in ONE DAY. And it's not stopping. Because every company watching this is thinking the same thing: "If I can replace ServiceNow with 3 AI agents and save $10 million per year, why wouldn't I?" The answer used to be: "Because you need enterprise-grade reliability." But now? AI agents are getting reliable. Fast. Software companies just realized they're competing with open-source models that cost $0.02 per 1,000 tokens. You can't win a pricing war against free. The companies that spent BILLIONS preparing for AI are getting killed BY AI. What an irony.

Ricardo

1,816,013 Aufrufe • vor 7 Monaten

Mark Cuban said what every CEO in America needs to hear. "There are only two types of companies in this world, those who are great at AI, and everybody else. If you don't know AI, you are going to fail. Period, end of story." Mark Cuban didn't stop there. "Whether you are an employee, you're going to have to understand how AI impacts your job and how you can use it to be better at your job. Same if you're a student. And if you're a CEO, you can't just say, okay, I'm going to get my tech guys to understand it and educate me on it. You have to understand it yourself because it will have significant impact on every single thing that you do. There's no avoiding it." This is coming from someone who built and sold the Dallas Mavericks, who made his first fortune selling a company to Yahoo for $5.7 billion in 1999, and who has spent the last two years going deeper on AI than almost any investor his age. He is describing the Innovator's AI Dilemma, entrepreneurs are right now building AI-native companies designed to displace every major incumbent. If a CEO tears down their company to rebuild it AI native, investors revolt but if they do nothing, AI-native startups eat their market and investors revolt anyway. Either path leads to shareholder lawsuits and there is no comfortable middle. The companies that survive will be the ones where leadership, not just the tech team, genuinely understands what the technology can do. The ones that don't will look back at this moment the same way Blockbuster looks at 2005.
0:33

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

StockMarket.News

163,468 Aufrufe • vor 4 Monaten