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"The average accretion was 180 basis points, that's significant" Franklin Templeton's Chris Galipeau on companies starting to quantify what AI does to margins "In Q2 there were two dozen S&P component companies that specifically quantified their EBIT margin accretion from using AI, and I think the average accretion was...

18,449 次观看 • 6 天前 •via X (Twitter)

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Chris Bryant today all but admitted why the government refuses to accept legislation that makes AI companies reveal their training data: They are going to offer creators transparency legislation *later*, in return for upending copyright law & letting AI companies train on people's life's work without permission. His justification for rejecting transparency legislation: "If we're going to get to that proper compromise solution [between creators and AI companies], it's going to require all the bits of the jigsaw to be put together into a comprehensive picture." That is: if creators want to know when AI companies are using their work, they're going to have to give up their rights. There is no other way of interpreting this comment I can think of. He is saying creators can't get transparency from AI companies without giving something up. And there is nothing to give up except their current rights under the law, which compel AI companies to ask their permission before using their work. Bryant & co. have given no other reason to reject transparency legislation. AI companies are breaking the law, and government refuses to hold them to account. IMO this is awful treatment of the creative sector by the government. Commercial generative AI training on copyrighted work without a license is illegal in the UK; creators cannot enforce their rights, because training happens behind closed doors; the government won't empower creators to enforce their rights without requiring them to give up other rights. Bookmark this. I hope I'm wrong, and that this is not the government's plan - but I see no other conclusion you can draw from Chris Bryant's words here.

Ed Newton-Rex

24,097 次观看 • 1 年前

The AI business model is undergoing a transformation. For the last few years, the playbook was simple: put an AI wrapper on a SaaS product and sell it by the seat. That era is ending. The new wave of AI companies are moving beyond simple subscriptions and embracing a more sophisticated approach tied directly to value creation. Here’s what’s changing: 💰 From Seats to Spend: The most forward-thinking companies are shifting to usage-based and outcome-driven pricing. Think less about how many people use the AI and more about what the AI does. This includes new revenue streams like "agentic checkout" on ChatGPT, where AI agents complete purchases and transactions directly within a chat interface. The closer the AI is to the dollar, the more value it captures. 🎙️ From Text to Voice & Video: The interface for AI is becoming more human. Voice is mainstream (Sierra for support, Listen Labs for market research). The next frontier is video, where AI will see, understand, and interact with the world in real-time. The keyboard is no longer the only way to talk to a machine. 🤖 From Advisors to Actors: Early AI copilots gave advice. The next generation takes action. These agents aren't just suggesting what to do; they are executing complex workflows that directly impact the metrics that matter: boosting conversion, reducing average handle time (AHT), improving NPS, and cutting churn. This is about moving from passive assistance to active problem-solving. The common thread? A relentless focus on tangible ROI. We’re incredibly bullish on founders who understand this shift and are building companies that align their success with the success of their customers. The future of AI isn't just about intelligence; it's about impact.

Konstantine Buhler

24,887 次观看 • 10 个月前

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Dustin

82,757 次观看 • 5 个月前