Technology is best when it brings people together.”
Edition #25: Open AI Hack, Where's the AI revenue, and will the trillion-dollar AI gamble pay off?
Date: 11-July-2024
Hey {{first name | AI curious reader}},
Thank you for subscribing to the One More Thing in AI Newsletter.
This is specially curated for Startup founders and Business Leaders like you who want to get smarter about AI in less than 5 minutes. It is a snapshot of what I read and learned about AI in the last two weeks.
Founders: In case you want to stress test your AI startup idea:
I hope you enjoy reading this edition. Keep learning and applying AI.
Best,
Renjit Philip
In this edition:
Will AI's $1 Trillion Gamble Pay Off or Fizzle Out? Goldman Sachs Experts Weigh In
Goldman Sachs has released a report (link here >>) on the massive spending on artificial intelligence (AI). They explore whether this huge investment will pay off. Tech companies are set to spend over $1 trillion on AI in the next few years. The report includes views from experts who are both hopeful and doubtful about AI's economic potential.
Key Insights and Arguments
1. Doubts About AI’s Economic Impact
Daron Acemoglu (MIT) thinks the benefits from AI will be smaller than expected. He predicts only a 0.5% increase in productivity and a 1% increase in GDP over the next ten years. He believes AI won't bring significant changes quickly and doubts it will be cost-effective in automating many tasks soon.
Jim Covello (Goldman Sachs) says AI needs to solve very complex problems to justify its high costs. He is skeptical that AI will ever be cheap enough to replace many tasks affordably.
2. Hopes for AI’s Potential
Joseph Briggs (Goldman Sachs) is more optimistic. He predicts AI could automate 25% of all work tasks, leading to a 9% increase in US productivity and a 6.1% GDP growth over the next decade. He believes the cost savings and the trend of technology becoming cheaper support a positive long-term impact of AI.
Kash Rangan and Eric Sheridan (Goldman Sachs) are excited about AI’s potential, even though there hasn't been a clear "killer application" yet. They believe current spending on AI infrastructure is reasonable and expect long-term benefits as AI becomes more integrated into different industries.
3. Challenges and Constraints
Chip Shortages: Analysts warn that chip shortages, especially in High-Bandwidth Memory technology, will limit AI growth in the near future.
Power Supply Issues: Analysts highlight that the rise in power demand from AI and data centers could strain the US power grid, potentially limiting AI’s growth.
4. Market Implications
AI Bubble Concerns: Despite doubts about AI’s fundamental value, some experts caution that the AI bubble could last for a while, benefiting infrastructure providers. Utilities might be the next big winners.
Long-Term Equity Returns: Only the most favorable AI scenarios, where AI boosts growth and profitability without increasing inflation, will result in above-average long-term returns for the S&P 500.
Takeaway
The report presents a balanced view, acknowledging both AI investments' potential and challenges. While there is hope for AI’s transformative power and long-term benefits, there are concerns about immediate economic impacts, technological constraints, and market sustainability. This analysis shows the complexity and uncertainty of AI’s future economic contributions. It's worth a read for business leaders!
To sponsor this Newsletter, subscribed to by startup founders and business leaders, click here:
How did you like this edition?
The AI $600B Gold Rush: Where to Next?
AI is making waves, and everyone's wondering how it will shape the future. Let’s dive into the latest insights from Sequoia Capital.
But the real question is, where's the money going?
For every dollar spent on a GPU, another dollar is needed for energy to run it in a data center. If Nvidia makes $50B from GPUs by the end of the year, data center costs will be about $100B. Companies using these GPUs, like Starbucks or Tesla, must make a 50% profit margin (assume that margin, which is a reasonable average). So, for every $50BN worth of chips sold, the companies need to create $200BN worth of revenues to make the purchase viable. The actual gap is $600BN (see image from Sequoia below).
How Sequoia arrived at the numbers (from their blog)
Takeaways from the post:
Supply shortage has subsided: Late 2023 was the peak of the GPU supply shortage. Startups were desperate for GPUs, but now it's much easier to get them with reasonable lead times.
GPU stockpiles are growing: In Q4, Nvidia reported that about half of its data center revenue came from large cloud providers, with Microsoft alone likely contributing 22%. Hyperscale CapEx is at historic levels, and Big Tech is heavily investing in GPUs. Stockpiling hardware continues, and a reset will occur when stockpiles are large enough to reduce demand.
OpenAI still leads AI revenue: OpenAI’s revenue reached $3.4B, up from $1.6B in late 2023. Most startups are still far behind, with revenues under $100M. AI companies need to deliver significant value to consumers to justify ongoing expenses.
Lack of Pricing Power: Unlike physical infrastructure like railroads, which can charge more because there can only be so many tracks, GPU data centers can’t do that. GPU computing is becoming a cheap, hourly service. More companies keep building AI clouds, stopping any single company from controlling prices, so prices drop, like in the airline industry.
Investment Losses: Investing in new technologies often leads to big losses. History shows that during technology booms, many people lose money, like with railroads. It’s easier to see which investments will lose money than which will win.
Depreciation: Technology like GPUs gets better quickly. Nvidia will keep making better chips, like the B100, which will make current chips lose value fast. People overestimate how long current chips will stay valuable, unlike railroads, which don’t get better as quickly.
Last word:
Winners vs. Losers: There are always winners, even when there’s too much “building” fuelled by VC money. AI is expected to be the next big thing. Lower GPU costs are suitable for new ideas and startups. While investors might lose money, AI company founders will likely succeed due to lower costs and the lessons learned during this time.
Forget about the current costs of AI training and GPUs and start building solutions that add value to end customers. It is still early days of AI.
Have you ever wondered how AI can simplify your business? Stack AI has the answers.
Screenshot from Stack.ai site
Discovering AI's Potential
AI Integration Made Simple: Stack AI shows how AI can streamline processes and enhance decision-making with real-time data. It’s like having a smart assistant 24/7.
Real-World Applications: Businesses use AI to improve customer service, optimize logistics, and boost marketing. StackAI helps you do it in a privacy-first and secure manner.
Enhancing Productivity
Tools for Every Task: Stack AI provides tools that fit seamlessly into your workflow, handling everything from scheduling to data analysis. Imagine if your internal system could learn from your employees’ work habits and suggest more efficient and effective ways.
Data-Driven Decisions: AI can turn your company data into actionable insights.
Future-Proofing Your Business
Staying Ahead of the Curve: Embracing AI now means preparing for the future. Start small—implement AI in one department and watch the benefits unfold.
Measuring Success: Success with AI is about long-term gains. Stack AI suggests setting clear metrics to track AI’s impact over time, leading to increased sales, better customer satisfaction, and more innovation.
Conclusion
Integrating AI isn’t just a trend—it’s a must. Start small, explore the tools, and watch as AI boosts productivity and decision-making. Ready to unlock efficiency? Try Stack AI today (affiliate link).
The Hacker Who Stole OpenAI's Secrets: Lessons in Cybersecurity.
Early last year, a hacker breached OpenAI, the maker of ChatGPT, accessing internal messaging systems and lifting details about the company’s AI technologies. Though the hacker didn't reach the core systems housing the AI code, the breach raised significant concerns about cybersecurity and the risks of AI technology.
A Real-World Example
Imagine you’re at a company picnic, and someone manages to overhear your team’s strategy discussions. They didn't get your secret recipe but now know your playbook. This is similar to what happened at OpenAI. The hacker didn't get the code, but they accessed internal discussions among researchers and employees.
Internal Concerns and Responses
OpenAI executives revealed the breach to their employees in April 2023 but did not inform the public, reasoning that no customer or partner information was stolen. However, this decision stirred anxiety among employees about potential foreign threats, especially from countries like China.
Leopold Aschenbrenner, a former OpenAI researcher, voiced his concerns that the company wasn't doing enough to prevent such threats. He believed stronger security measures were necessary to protect against future breaches. Although he was later dismissed for “leaking information”, his concerns highlight an ongoing debate within tech companies about balancing security with transparency. Punishing the whistleblower?
The Bigger Picture
This incident isn’t isolated. Last month, Chinese hackers used Microsoft systems to attack U.S. federal networks. Such events underscore the importance of robust cybersecurity measures.
Industry Take
Companies like Meta take a different approach, sharing their AI designs as open-source software. They argue that transparency can help identify and fix problems early. However, this openness comes with its own risks, such as the potential misuse of AI technologies to spread disinformation or automate away low-paying jobs.
Balancing Innovation and Security
OpenAI has taken steps to enhance its security, including forming a Safety and Security Committee with experts like Paul Nakasone, a former NSA and Cyber Command leader. Federal and state lawmakers are also pushing for regulations to ensure AI technologies do not pose a significant threat.
Conclusion
The OpenAI hack serves as a wake-up call for all tech companies. Balancing innovation with security is crucial, especially when your mission is to create AGI.
References
That wraps up another power-packed edition of the Newsletter. Want the insider advantage? Subscribe to our email edition to access content before anyone else, and keep it forever in your inbox.
Also, you get exclusive content that only subscribers get access to. Discover the exclusive benefits of being a subscriber!
Support & Sponsor
Forwarded this email by someone, and you want to subscribe?
You can help us spread the word by sharing this Newsletter with a friend or colleague.
This Newsletter is distributed on LinkedIn and by email. Circulation is counted as the sum of individual subscribers on both platforms.
Hit the subscribe button to receive the Newsletter as soon as it is published. If you want to interact with me on social media, follow me on LinkedIn and Twitter.
Disclosure: Some of the links in this Newsletter are affiliate links, which means we may earn a small referral fee at no extra cost to you if you purchase from them. I personally test out the AI tools during my review process. Your support helps keep this Newsletter running—thank you!