E11: AI's energy crisis, 20% freelancer wage drop & London's AI domination

Chris Rod Max discuss AI's massive energy appetite (10x for ChatGPT vs Google searches), creative jobs disrupted (20% wage drop for writers), and the rise of European AI hubs (27% in London). Plus: decision frameworks for AI vs human tasks, uneven benefits across companies & regions, and the scramble for energy-efficient AI infrastructure.

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Takeaways

• AI and data centers consume a significant amount of energy, raising concerns about sustainability and environmental impact.
• The job market is being impacted by AI, with some jobs being replaced by AI and others being enhanced by it.
• London is the leading AI hub in Europe, with a high concentration of AI companies.
• The distribution of AI companies across cities is uneven, with some cities benefiting more from the AI boom than others.

Episode Transcript

Introduction

Rod: Welcome to another episode of the Chris Rodman Show. As always, we're joined by my co-hosts Chris and Max. Today, we'll be discussing three key topics: the impact of AI on energy systems, its ongoing effects on the world of work, and whether software companies are benefiting from AI. Let's dive in.

Powering the AI Revolution: The Energy Crisis Behind Our Digital Future

Rod: Two articles caught my attention this week regarding AI and energy consumption. One from the Washington Post drew parallels between the current AI boom and the crypto craze of a few years ago, particularly in terms of energy consumption. What are your thoughts on how AI is impacting our energy grid and environment?

Chris: This is indeed a crucial topic. The broader question is how technology impacts energy consumption as a whole. Some numbers are quite shocking:

• In the US, data centers are projected to account for 8% of total power consumption by 2030, up from 3% currently.
• By 2034, global data center energy consumption is expected to match India's current usage.
• Data processing volumes have increased dramatically: from 2 zettabytes in 2010 to 120 zettabytes currently, with another 150% increase expected over the next two years.

These figures highlight the massive scale of data consumption and energy usage we're dealing with. It's undoubtedly going to be one of the biggest challenges moving forward and a limiting factor in AI adoption.

Max: I think it's important to view this in historical context. Whenever a new technology emerges, it tends to consume a lot of energy initially. We saw this with cars and the petroleum industry, for example.

I believe we should see AI as a new way of bringing people together and changing our lives, similar to how older technologies did in the 1900s. However, this does raise questions about energy security. Countries and companies will need to think about this more deliberately.

For instance, Microsoft has become one of the largest construction companies in the US due to its data center building efforts. While the US doesn't currently have an energy problem, we might need to consider how this could change as they try to serve the world with their services.

Rod: Let's consider the enterprise perspective. The article mentioned that a ChatGPT search consumes 10 times more energy than a regular Google search. How do you think this will impact corporate social responsibility and ESG guidelines? Will companies start paying closer attention to their AI service providers in terms of energy efficiency?

Chris: I think the bigger issue is where these data centers are built and how they can be operated in a more climate-friendly way. For example, some companies have decided against building data centers in certain locations due to cooling challenges in hot climates.

Rod: I see this as a potential differentiator for startups. Companies that use green energy sources from day one and can prove it might have an edge in the enterprise market. We're also seeing hardware vendors like Intel developing more energy-efficient chipsets for AI workloads.

Max, as an investor, would you be interested in AI companies that focus on energy efficiency?

Max: I think there are two aspects to consider: chipset efficiency and energy sources. From an economic perspective, more efficient chipsets mean greater capacity and potential cost savings for large corporations. If the cost efficiency is significant, it would certainly be intriguing for organizations.

Regarding renewable energy consumption, it's currently more of a European focus. In the EU, there are already rules for disclosing energy sources, which affects data centers. The key business is still selling data center applications, but having green energy certificates might become a factor in winning deals.

Power Struggles and Job Juggles: Navigating AI's Double-edged Sword

Rod: Moving on to our next topic, we've been discussing how AI is impacting the job market. Recent data from Upwork shows some interesting trends in the freelancer market:

• Writing job payments have decreased by 20% since the release of ChatGPT in late 2022
• Translation jobs have seen a 10% decrease
• Marketing jobs have experienced an 8% decrease

Interestingly, the CTO of OpenAI suggested that some creative jobs might not have been necessary in the first place. Max, what are your thoughts on this, especially considering the recent statement from the San Francisco Fed president attempting to quell concerns about AI's impact on the US labor market?

Max: I think it's important to consider how labor and capital are increasingly decoupled. Traditionally, there was a clear link between labor and capital, but with software and now generative AI, we're seeing this shift even more dramatically.

In the long term, if AI can replace many services, we might see a scenario where fewer people need to work. This could potentially lead to discussions about universal basic income. However, in the short term, we're likely to see a transition period affecting different segments of the labor market gradually.

Chris: I've been thinking about this in terms of which jobs are likely to be impacted and which aren't. I've developed a two-by-two matrix to help conceptualize this:

1. Low proprietary data/IP, light assets: These jobs are most likely to be replaced by AI (e.g., design, translation, copywriting)
2. High proprietary data, light assets: These industries will need to transform (e.g., insurance, e-commerce)
3. Low proprietary data, heavy assets: AI will enhance these jobs (e.g., transportation, mining, food and beverages)
4. High proprietary data, heavy assets: These are least likely to be disrupted soon (e.g., law, defense, education, healthcare)

Rod: Interestingly, some freelancers don't see this trend as entirely negative. They report that companies are coming back to them for more nuanced, factual content after receiving generic, AI-generated text. Do you think we might reach a saturation point where companies start valuing human creativity more again?

Chris: I believe we'll all become quality managers to some extent, outsourcing workflow to AI but still needing to review and supervise the output. I also think there's an opportunity for new graduates who can grow up learning to work with AI from the start.

Rod: It's worth noting that a recent report from Microsoft and LinkedIn found that 75% of knowledge workers use AI at work and want to use it even more. They don't want to wait for companies to catch up. This suggests a strong eagerness to adopt the technology, despite potential negative impacts on some jobs.

Mamma Mia, AI's on an Uneven Playing Field: Triumphs and Tribulations

Rod: Let's discuss our final topic: the rise of AI hubs in Europe. A recent report shows that the development of AI companies is not evenly distributed across major European cities. London leads by far, generating 27% of all AI startups in Europe. Paris and Berlin follow with 10% and 12% respectively.

Max, as an investor, are you seeing pitches from all over Europe, or is it mainly concentrated in these major hubs?

Max: Based on my conversations, most AI companies I've encountered have representation in the larger cities you mentioned - London, Paris, or Berlin. There seems to be a compounding effect from the presence of capital, talent, and big tech companies acting as feeders for startups.

For example, my alma mater, UCL, has deep ties with DeepMind and Google, which provides opportunities for creating companies. While there are exceptions, most of the AI startups I see come from these larger hubs.

Rod: Interestingly, some cities traditionally considered tech hubs, like Munich, don't figure prominently in this report. Chris, you're based in Munich - what's your perception of the AI startup scene there?

[Note: Chris's response was cut off due to technical issues]

Rod: Another related point is the performance of public companies in the AI space. While we often think of AI as benefiting those who invest in it, this isn't always reflected in valuations. For example, Salesforce, historically considered an innovator in machine learning and predictive modeling, has seen its stock price barely move since 2023. This is in stark contrast to companies like Nvidia and Meta, whose stocks have skyrocketed.

Chris: I think this relates back to my two-by-two matrix. Salesforce falls into the category of low IP and digital, making it easy to disrupt. The AI features they've introduced may not add enough value for people to pay more, especially when they can get similar functionality from ChatGPT, which they're already paying for anyway.

Conclusion

Rod: To wrap up, we've covered three main topics today:

1. The impact of AI on energy consumption and infrastructure
2. The changing landscape of jobs in the AI era
3. The uneven distribution of AI innovation and its effects on company valuations

We're seeing tangible benefits from AI adoption, but it's creating a divide between those who use it and those who don't. This applies not only to individuals and companies but also to cities and technology hubs.

Thank you for joining us for another episode of the Chris Rod Max Show. Remember to like, subscribe, and leave your comments. We appreciate your feedback and suggestions for future topics and guests. If you enjoyed this episode, please share it with your friends. We look forward to seeing you next week for another engaging discussion!