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4 min read

Follow the Trends: Volume 6

I often talk with friends, coworkers, and family about AI, and I’m constantly asked, ‘What should I be paying attention to in order to stay ahead?’. My answer is always the same: start with education and do your best to pay attention to the macro trends. The reality is that the AI news cycle moves like a flash flood, making it increasingly challenging to keep up with. I feel that same pressure, but I have implemented several strategies to keep me sane and sift through the noise. Focusing on macro trends has helped me organize thoughts more efficiently, leading to better information retainment. I’ve also been able to avoid the fringe aspects of the news cycle that play on people's emotions by spreading fear and hysteria about AI. Today, I’ll share three macro trends and provide insights into what I find interesting and why it matters to your AI education journey.

Infrastructure Built Out

There are several trends that fall into this bucket, and I plan on exploring each in more detail in future volumes. It’s incredible to see the capital expenditures going into infrastructure buildouts related to data centers, power grid facilities, and chip manufacturing. Companies, mainly the ‘Magnificent 7’, are leading the charge in AI capex investments, and it’s not easy to follow by solely reading headlines. There are constant conversations around an AI bubble, with many pundits drawing comparisons to the ‘Dot-Com’ bubble that lasted from 1995-2001. I see the arguments on both sides and fully appreciate the cautious approach being taken by economic experts. But my perspective is more bullish; I see the capex wave as a strategy and competition between the largest companies in the world. Whoever can build out the infrastructure to lower costs (e.g., $ per token, compute costs, etc.) will put pressure on smaller companies to mirror pricing strategies that could put them out of business. Are we in an AI bubble? Probably. But there is opportunity in every situation, which is why the AI infrastructure race is a trend all of us should pay attention to. 

Here are a few headlines that highlight the massive financial stakes being put down to bring AI to life:

  • McKinsey estimates $6.7T in global data-center investment needed by 2030 to meet compute demand, $5.2T of that for AI-class facilities.
  • Alphabet spent $23.95B in capex (49% of operating cash flow), with Meta and Microsoft even higher by share; Amazon near ~90%. Signals long-horizon bets on AI capacity. 
  • Meta’s guidance is eye-popping. 2025 capex outlook lifted to $70–72B (almost double 2024).
  • Power is the new bottleneck. IEA projects data-center electricity use doubling to ~945 TWh by 2030 (slightly more than Japan’s total use today); AI is the biggest driver. In the U.S., data centers account for nearly half of electricity-demand growth through 2030.
  • U.S. grid pressure. EPRI (via U.S. DOE) estimates data centers could consume up to 9% of U.S. electricity by 2030.

Top takeaway: Follow the money to better understand the winners of the future, impacts to your personal life, and realistic expectations for future AI advancement.

Regulation

This is a hot topic, not just in the U.S., but across the globe. AI introduces new challenges that governments and regulators cannot keep up with. And, like anything else, there are politics driving AI regulation that might not be best for consumers (or humanity as a whole). This area of AI seems to have the most noise and the biggest consequences for how the world evolves as AI becomes more prevalent and powerful. In the U.S. specifically, there are several states pushing for AI regulation, while there has been little consensus on what (if anything) should be done at the federal level. California has introduced several pieces of AI-specific legislation to help put guardrails in place to protect people from the potential negative consequences of AI. Time will tell if these measures are successful, but AI regulation will be an important piece of the puzzle as AI becomes more embedded into everyday life. 

Top takeaway: Compliance and law making will impact the speed in which AI labs can execute and influence transparency (for good or bad) across the industry. 

Corporate Adoption

Hype or buzzwords? Pilot vs. production? ROI vs. model change? For those of you who work in corporate America, the noise can be deafening. Every week, a new report is released about AI taking jobs, a fresh round of layoffs, or CEOs highlighting automation gains. But the extremes of these headlines seem isolated to big tech, at least for now. I believe this is due to how early we are in the AI adoption lifecycle. Many companies are struggling to find ROI from true generative AI capabilities and are stuck in ‘pilot’ mode. It’s impossible to predict when we will start to see breakthroughs across the Fortune 500, but I believe it will be here within the next 18 months. The technology is improving at a rapid pace, and compute costs continue to decline. We’ll start to hear more and more headlines related to ‘agentic workflow automation’ and ‘AI employees’ that drive ROI and bottom-line impact. It’s imperative that we all pay attention to this trend to learn and figure out how to incorporate generative AI into our professional lives. 

Top takeaway: AI adoption does not mean impact to the business. Following ROI and workflow automation helps me sift through the noise and study companies leading AI transformation in the workplace. 

It can feel overwhelming to keep up with AI news, but you are ahead of most people by taking time to read Neural Gains Weekly. The only way to get ahead is to put in the effort, even if that is only 10-15 minutes a week. Stay consistent, follow trends, and experiment with AI tools. See you next week!