📅 2024 AI Recap: Key Trends & Insights Building Momentum for 2025

Hello Sunshiners & Happy New Year’s Day 2025!

If you’re anything like me, you probably spent the last 24 hours pondering the incredible products and innovations I shared in Part 1 of this series (or maybe not 😉).

Anyway, those top 15 AI products were a glimpse into the power of what’s possible. But if we’re to truly understand this dynamic space, we need to go further, and unpack the narrative behind all the changes we witnessed in 2024.

In Part 2, I’m shifting my focus from the what to the how and when, journeying through the significant moments of 2024, month by month. By tracking the ebb and flow of news, innovations, and strategic shifts, we’ll piece together a deeper understanding of how the AI landscape took shape. This perspective is not just about recounting the past; it's about equipping ourselves with the context needed to navigate the future of AI and effectively set our 2025 goals for Palm Springs Coachella Valley.

This in-depth look into 2024, combined with the product-focused view from Part 1, should provide a comprehensive landscape analysis to guide our strategic thinking, as we start work on building our AI Studios in 2025. Are you ready to journey with the Sunshine Squad into the heart of 2024's AI narrative? Let's get started.

A Year of AI Transformation in 2024

Yep, 2024 was a landmark year for artificial intelligence, marked by groundbreaking advancements, strategic shifts, and intense debates. From the launch of transformative tools like OpenAI’s Sora to the rise of open-source models like Meta’s Llama 3, the year showcased the rapid evolution of AI. It also highlighted the challenges of scaling, policy-making, and economic ROI. Below is a month-by-month breakdown of the most significant AI stories, with deeper insights and key takeaways for each.

First, for those of you with little time, here’s a quick overview of the key AI stories of 2024, month by month:

Month-by-Month Highlights

1. January: GPT Store Launch

2. February: OpenAI’s Sora Preview

3. March: Microsoft’s Inflection Acquisition

4. April: Meta’s Llama 3 Release

5. May: Ilya Sutskever Leaves OpenAI

6. June: Apple Intelligence & Nvidia’s Rise

7. July: AI ROI Debates

8. August: California’s SB 1047 Debate

9. September: OpenAI’s O1 Preview

10. October: NotebookLM & Anthropic’s Computer-Use Model

11. November: U.S. Election & AI Policy

12. December: Peak Data & the End of Pre-Training Era

Deeper Dive into Each Month

January: GPT Store Launch

What Happened: OpenAI launched the GPT Store, initially seen as the next Apple Store for AI.
Key Insight: While custom GPTs proved useful for personal workflows, the store model failed to gain traction as a business platform.
Takeaway: Utility over hype—AI tools often find their value in practical, repeated use rather than grand commercial ambitions.

February: OpenAI’s Sora Preview

What Happened: OpenAI unveiled Sora, a revolutionary video generation tool, causing immediate disruption in industries like Hollywood.
Key Insight: The tool’s capabilities were so advanced that it led to the suspension of an $800 million studio project (Tyler Perry).
Takeaway: Disruption is inevitable—AI’s rapid advancements can reshape entire industries overnight.

March: Microsoft’s Inflection Acquisition

What Happened: Microsoft acquired Inflection’s IP and team, hedging against its reliance on OpenAI.
Key Insight: This marked a trend of “non-acquisition acquisitions” to avoid antitrust scrutiny.
Takeaway: Diversification is key—tech giants are spreading their bets to mitigate risks in the AI race.

April: Meta’s Llama 3 Release

What Happened: Meta launched Llama 3, narrowing the gap between open-source and closed-source AI models.
Key Insight: By summer, Llama 3.1 405B matched GPT-4’s capabilities, democratizing access to high-performance AI.
Takeaway: Open-source is catching up—the AI landscape is becoming more accessible and competitive.

May: Ilya Sutskever Leaves OpenAI

What Happened: OpenAI co-founder Ilya Sutskever departed to start Safe Superintelligence, focusing solely on achieving superintelligence.
Key Insight: This marked a shift toward prioritizing long-term AI safety over immediate commercial applications.
Takeaway: Safety over profit—some leaders are betting on AI’s future potential rather than its current utility.

June: Apple Intelligence & Nvidia’s Rise

What Happened: Apple announced its AI strategy, rebranded as “Apple Intelligence,” while Nvidia became the world’s most valuable company.
Key Insight: Apple aimed to simplify AI for mainstream users, but its vision fell short of expectations.
Takeaway: Simplicity is hard—even tech giants struggle to make AI accessible to the masses.

July: AI ROI Debates

What Happened: Reports from Sequoia and Goldman Sachs questioned the economic benefits of AI infrastructure spending.
Key Insight: While concerns were raised, the debate highlighted the need for sustainable AI investment strategies.
Takeaway: ROI matters—AI’s long-term success depends on delivering tangible economic value.

August: California’s SB 1047 Debate

What Happened: California’s SB 1047 legislation sparked a national debate on AI policy, focusing on theoretical future risks.
Key Insight: The bill was vetoed due to its focus on speculative issues rather than immediate challenges.
Takeaway: Policy must be practical—AI regulation needs to address real-world concerns first, and then hypothetical future scenarios.

September: OpenAI’s O1 Preview

What Happened: OpenAI previewed O1, a new class of reasoning models designed to “think” before responding.
Key Insight: This hinted at potential plateaus in traditional AI scaling methods.
Takeaway: Innovation is ongoing—AI development requires new approaches to overcome limitations.

October: NotebookLM & Anthropic’s Computer-Use Model

What Happened: NotebookLM’s audio overviews gained recognition, while Anthropic introduced a computer-use model for agentic tasks.
Key Insight: These developments showcased the potential of AI to create personalized and interactive experiences.
Takeaway: Personalization is powerful—AI’s future lies in tailored, user-centric applications.

November: U.S. Election & AI Policy

What Happened: The U.S. presidential election raised questions about AI policy, with the appointment of an AI czar.
Key Insight: AI’s role in national security and governance became a central issue.
Takeaway: Policy is pivotal—AI’s impact on society will be shaped by political decisions, like it or not.

December: Peak Data & the End of Pre-Training Era

What Happened: The AI community grappled with the idea of peak data and the end of the pre-training era.
Key Insight: This sparked renewed experimentation in AI development, including exploration of world models.
Takeaway: Exploration is essential—AI’s next breakthroughs will come from innovative approaches, not just scaling.

Conclusion: A Year of Lessons and Possibilities

The dynamism of 2024 has set the stage for an even more transformative 2025, in my opinion. While I've highlighted key news moments, the reality is that the AI ecosystem saw countless other advancements — new tools, startups, and use-cases — that contributed to its rapid evolution. This ongoing development promises a future full of potential and yet undiscovered possibilities, and it makes me realize how essential it is that our valley keep pace. I'm thrilled to continue exploring it, and even more driven to make the AI Studio a reality so we’re not just observing from the sidelines!

Your turn. I'd love to hear what you’re thinking. Did any of this news resonate with you? Do you remember where you were when you first experienced NotebookLM? Do you use Apple Intelligence? What are your thoughts on the overall state of AI now that we’re in 2025? Whatever you’re experimenting with, I want to hear about it.

Shoot me an email at [email protected] or join the AI Founders Club (and soon-to-be AI Studios). Bonus points if you've got thoughts on my 760-SUNSHINE concept – I'm seriously considering making that happen.

Researched and written with the help of SatGPT and BuzzAI - both Generative AI Agents