9point8 Collective’s Post

Sequoia recently published a blog post about AI's $600B problem. Read the original piece: https://lnkd.in/eCxC2Z-i “Winners vs. losers: I think we need to look carefully at winners and losers—there are always winners during periods of excess infrastructure building. AI is likely to be the next transformative technology wave, and as I mentioned in the last piece, declining prices for GPU computing is actually good for long-term innovation and good for startups. If my forecast comes to bear, it will cause harm primarily to investors. Founders and company builders will continue to build in AI—and they will be more likely to succeed, because they will benefit both from lower costs and from learnings accrued during this period of experimentation. “

Exploring the AI Landscape: Brazil’s Unprecedented Opportunity 🚀✨ Amidst the global race for AI dominance, Brazil stands on the cusp of a technological revolution. The surge in GPU investments, as highlighted by Sequoia Capital’s insightful article, not only underscores the pivotal role of AI but also signals a golden era for Brazilian entrepreneurs. This moment is ripe for innovation, urging local startups to harness AI’s potential, crafting solutions that resonate globally while fostering Brazil’s tech ecosystem. Let’s embrace this journey, transforming challenges into opportunities. #AI #Brazil #Entrepreneurship #Innovation” https://lnkd.in/dyuBGN-H

The $240B Question go AI #ai “For startups, the takeaway is clear: As a community, we need to shift our thinking away from infrastructure and towards end-customer value. Happy customers are a fundamental requirement of every great business. For AI to have an impact, we need to figure out how to leverage this new technology to make people’s lives better. How can we translate these amazing innovations into products that customers use every day, love and are willing to pay for? “ We at ZIPPYAR already found one, personalized prescription instructions in video. How goal is to deliver it without increasing the cost os healthcare, which is already extremely high. So how can we build these solutions while minimizing the cost with its infrastructure? This for us is #technologyforgood

AI’s $600B Question https://buff.ly/4ezKuW3 by @sequoia big gap between revenue expectations implied by the AI infrastructure build-out, and actual revenue growth in the AI startup & incumbent ecosystem! 🤖 🫧 #rafAIgual #AI #valuation #bubble #vc #investing #startup #ecosystem

You need LLM Data Products - 100s of them per enterprise to justify the spend. And a factory to generate these. You need Hasper! :) "For startups, the takeaway is clear: As a community, we need to shift our thinking away from infrastructure and towards end-customer value. Happy customers are a fundamental requirement of every great business. For AI to have an impact, we need to figure out how to leverage this new technology to make people’s lives better. How can we translate these amazing innovations into products that customers use every day, love and are willing to pay for? " https://lnkd.in/gUb9XutV

We know life would be very differentin 5-10 years, do not have clarity on what exactly will change, and this is okay as we experiment every day Wish if this could be pursued with carbon neutral objectives Is it worth slowing down? “ There’s a $125B+ hole that needs to be filled for each year of CapEx at today’s levels. So the $200B question is: What are you going to use all this infrastructure to do? “

Sequoia Capital's key message: it's time to build the application layer of AI. Let's put this amazing infrastructure to work - for real people "For startups, the takeaway is clear: As a community, we need to shift our thinking away from infrastructure and towards end-customer value. Happy customers are a fundamental requirement of every great business. For AI to have an impact, we need to figure out how to leverage this new technology to make people’s lives better. How can we translate these amazing innovations into products that customers use every day, love and are willing to pay for? The AI infrastructure build out is happening. Infrastructure is not the problem anymore. Many foundation models are being developed—this is not the problem anymore, either. And the tooling in AI is pretty good today. So the $200B question is: What are you going to use all this infrastructure to do? How is it going to change people’s lives?" https://lnkd.in/gzaEDT9y

A very insightful way of looking at the AI landscape by “following the GPUs”. The huge CapEX investments ($200B+) in Language Models and Data Centers is expected to drive down training and inference costs for startups, which is a big barrier today. Good news for entrepreneurs who are focused on building value and making lives better for their customers.

A $500 billion hole in the AI market. So what should we do about it? Corporations and startups are investing huge amounts of money in infrastructure to develop their AI services. It is a fact. How much should these AI services earn to recoup their costs? After all, this is not all done out of love for art; business must bring in money. David Kahn from the Sequoia Foundation tried to answer this question. He made his first calculations in September last year (https://lnkd.in/dUa5QtMk). You can read the details, as he believed, at the link. In short, he took the publicly known costs of chips, added electricity costs, threw in a pinch of company margins, and the result was... $200 billion. Yes, that’s how much all AI companies and AI services of corporations, like Google, should earn per year and Microsoft to recoup the investment. The problem is that he only counted $75 billion in revenue. Where is the other $125 billion? This is the main mystery. About nine months passed and David decided to repeat his calculations (https://lnkd.in/dHFF7R8u). And this time it turned out that in the fourth quarter of this year, the annual revenue of AI companies and services should already be $600 billion. This is ideal. But here, as you understand, we have a problem again. Yes, OpenAI itself has significantly increased its revenue, others have caught up. But Kahn counted only $100 billion in revenue. That is, the hole has reached $500 billion! One could argue that they are investing in infrastructure for a long time, so they can fight back for a long time. Kahn writes that he partly agrees. But there are nuances. Chips lose value quite quickly because new, more efficient ones appear. Plus, great competition forces services to reduce prices to the limit. The conclusion is simple - not all companies will survive this hype around AI. And investors will simply burn their money by betting on the wrong player. But this is a necessary evil with such large-scale changes in the market. But for the founders, as Kahn writes, everything is not so bad. They will accumulate experience and create better AI products. #productmanagement #management #marketing #customerservice #hr #hiring #UX #startup #web3 #tech #growth #digital #Growthhacking #projectmanagement #blockchain #UI #startup #fintech #agile #edtech #business #leadership #analytics #Productdevelopment #design #iot #ai #researchanddevelopment

5 #AI Predictions For The Year 2030 >> https://buff.ly/4a4bn1g #tech #ArtificialIntelligence #ML #MachineLearning #genAI #generativeAI #data #algorithms #leadership #bigtech #SiliconValley #LLM #startups #future