The GPU—The core of AI processing

Welcome to Edition 12 of the Generative AI with Varun newsletter! This week, we explore how GPUs work, dive into Big Tech’s $300 billion race to lead in Generative AI, and uncover why inference costs for advanced LLMs are rapidly decreasing. Let's get started.

What powers the AI revolution? GPUs.

Here’s why. ??

Artificial Intelligence is transforming our world, from automating tasks to generating lifelike images and solving complex problems. But behind every AI breakthrough is a crucial engine: the GPU. ?

In this video, I explain GPUs with a simple analogy—baking cookies. ??

Think of a CPU as a single oven, baking one tray at a time. ??

A GPU, on the other hand, is like having a wall of smaller ovens, baking multiple trays simultaneously. ????????

This parallel processing is why GPUs are essential for training AI models and powering technologies like ChatGPT and image generation tools. ??

The ability to handle massive computations at speed is what drives the innovation we’re seeing in AI today.

?? Check out the video to see how it all comes together. Subscribe to the Generative AI with Varun YouTube channel for more bite-sized videos on AI.

Big Tech’s $300 Billion Race to Dominate Generative AI ??

The competition to lead in generative AI is intensifying as Amazon, Google, Meta, Microsoft are collectively projected to invest a staggering $300 billion in capital expenditures next year. ??

According to Morgan Stanley, the majority of this investment will focus on expanding infrastructure—data centers, real estate, advanced GPUs, and networking equipment—essential for powering next-generation AI services. ??

Why It Matters ??

The surge in spending reflects the immense potential of generative AI and large language models (LLMs). With skyrocketing demand for AI-driven cloud capabilities, companies like Amazon are significantly ramping up their investments.

Morgan Stanley has raised Amazon’s 2025 CapEx estimate by 22% to $96.4 billion, with projections reaching $105 billion in 2026. This positions Amazon as a leader among hyper-scalers investing heavily to capitalize on the AI opportunity.

Key Insights??

1. A Unique Moment in Tech History: Amazon CEO Andy Jassy describes this investment wave as a “once-in-a-lifetime” opportunity to reshape the digital landscape through generative AI.
2. Surging Demand for AI Services: Recent earnings reports from Big Tech reveal an accelerating demand for AI solutions, signaling a future where AI-powered technologies transform industries and redefine human interaction with technology.

The Bottom Line ??

As these tech giants pour unprecedented resources into advancing AI capabilities, the industry is on the cusp of transformative applications and massive shifts in how businesses operate, innovate, and scale.

This monumental investment not only highlights the race to dominate generative AI but also underscores the profound impact AI will have on the global economy and society at large.

Inference Costs Plummet for Advanced LLMs ??

The cost of running powerful language models is dropping fast, as OpenAI and Anthropic roll out optimized models that make high-quality AI more accessible than ever.??

OpenAI’s 100x Drop with GPT-4o Mini ??

As shown in the visual, OpenAI’s GPT-4o mini achieves a staggering 100x reduction in inference costs compared to GPT-4. This smaller, cost-effective model maintains strong performance, making advanced AI more affordable for real-world applications.

Anthropic’s 60x Drop with Claude 3.5 Haiku ??

Similarly, Anthropic’s Claude 3.5 Haiku delivers a 60x cost reduction from earlier models, processing large token volumes rapidly for scalable use cases like real-time support and complex data analysis.

A New Era of AI Accessibility ?

These dramatic reductions—100x for OpenAI and 60x for Anthropic—signal a transformative shift, making it viable for more organizations to harness powerful LLMs. Advanced AI is now within reach for a wider range of applications, driving innovation across industries. ????

Diving into AI Use Cases

??? I recently had the privilege of joining the Do Good Work podcast with my friend Raul Hernandez Ochoa!

We discussed Generative AI use cases, the importance of experimenting with new tools, and how companies can balance innovation with data privacy.

I also shared my personal journey and motivations behind starting my YouTube channel, Generative AI with Varun.

Raul and I connected through the LinkedIn Top Voices community earlier this year, and his content and platform continue to inspire me. ??

?? Watch the full conversation on YouTube: https://lnkd.in/gQt-WzUu

?? Listen to the podcast on Apple Podcasts or Spotify

I'd love to hear your thoughts—what AI use cases stood out to you? Share in the comments below!

Data Fuels AI Success ???

AI is powerful, but let’s get to the heart of it: Without the right data strategy, even the best AI models can fail.

Here’s why:

1?? Quality Matters. AI models need clean, consistent, and relevant data to deliver meaningful results. Garbage in, garbage out.

2?? Alignment Drives Impact. Your data strategy should directly connect to business goals. AI only delivers value when it solves the right problems.

3?? Scalability is Key. As your AI ambitions grow, your data pipelines need to scale seamlessly—without creating bottlenecks.

And let’s not forget: A strong data strategy ensures fairness and prevents biases in AI, building trust in the results.

The bottom line? Treat your data as a strategic asset, not an afterthought. It’s the difference between AI that works and AI that drives measurable impact.??

Subscribe to the Generative AI with Varun Newsletter to level up your data and AI strategy.

Have a happy Thanksgiving!