Well, here we are. February 2025, and the tech world is already off to an eventful start. If you’ve been following AI news lately, you know it’s been a whirlwind of breakthroughs, disruptions, and, of course, debates over investment, ethics, and accessibility.
I recently went live on LinkedIn, YouTube, Twitch, and Facebook to share some of my thoughts on where AI is heading, and I wanted to summarize those ideas here for the Frank’s World audience.
This video is from FranksWorld of AI.
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AI’s Dot-Com Moment?
One of the biggest themes I touched on was how AI feels a lot like the dot-com boom. Back in the late ‘90s and early 2000s, companies were pouring millions into web startups—many of which had no clear plan for monetization. Remember pets.com and its famous sock puppet commercials? Tons of money went into those companies, but very few had a viable long-term strategy.
That’s the phase we’re in now with AI. There’s a ton of money going into AI hardware and infrastructure, but there’s also a growing realization that throwing millions (or billions) into GPUs and cloud compute without a solid business model may not be the best move. Companies that figure out how to actually monetize AI—whether through enterprise applications, synthetic data, or real-world automation—are going to be the ones that survive this hype cycle.
DeepSeq and the Changing AI Landscape
DeepSeq’s recent advancements have also been a major topic of discussion. The big shocker? They reportedly trained a powerful model for only $6 million—a fraction of what companies like OpenAI, Google, and Anthropic have been spending. While there’s an asterisk next to that number (real-world costs are always more complex), it’s a wake-up call.
For years, AI development has been dominated by a “bigger is better” mindset—more data, more GPUs, more compute. But that’s starting to shift. The rise of small language models (SLMs) is showing that optimization and efficiency can be just as important as raw power. AI teams are now looking beyond hardware constraints and realizing they might actually be data-poor rather than GPU-poor.
Experimenting with AI-Generated Content
Speaking of AI disruption, I’ve been experimenting with AI-generated video content on YouTube. I’ll be honest—I was pretty skeptical about short-form video at first. The TikTok/YouTube Shorts format didn’t seem like my style, but I decided to give it a shot.
Using tools like Pika, I started creating AI-generated explainers—like one where SpongeBob explains Retrieval-Augmented Generation (RAG). It turns out, people love this kind of content. Engagement on my YouTube channel has shot up, and I’m having a blast making these videos.
For fun, I even generated some AI clips using celebrity voices like Gordon Ramsay commenting on tech news. It’s all in good fun, but it also highlights the power of AI tools in content creation. The same technology that’s making it easier for developers to build AI models is also revolutionizing creative media.
The Rise of Open Source AI
Another thing I’m passionate about is open-source AI. If AI is going to truly change the world in a positive way, it needs to be accessible. Big players like OpenAI and Google have incredible models, but many of them are locked behind paywalls or enterprise-only access.
The rise of open-source models is shifting the balance. Tools like Meta’s Llama models and Mistral are giving smaller companies, researchers, and developers the power to build and customize AI models without requiring an enterprise budget. This is why I believe open-source AI is the future—it ensures innovation isn’t just limited to a handful of companies with billion-dollar war chests.
Exciting News for the Data Driven Podcast!
On a personal note, I want to take a moment to express some gratitude. My podcast, Data Driven, has been ranked in the top 100 AI podcasts, and in fact, we hit #38! Competing against industry giants like Lex Fridman and other big-budget AI shows, it’s an honor to be recognized.
Not only that, but my other podcast, Impact Quantum, is making waves in the top 20 quantum computing podcasts. This is just the beginning, and I have some exciting episodes coming up with startup founders doing groundbreaking work in AI and quantum.
What’s Next?
So where do we go from here? I think 2025 is going to be a defining year for AI in several key ways:
1️⃣ The AI investment bubble will burst (for some). Just like the dot-com boom, companies without a clear monetization strategy will struggle. AI is here to stay, but the market will shake out the unsustainable players.
2️⃣ Synthetic data will take center stage. More companies are realizing that data—not just compute power—is the real bottleneck in AI development. Expect a surge in synthetic data solutions to improve model training.
3️⃣ Open-source AI will gain momentum. We’ll see more businesses adopting open-source models instead of relying solely on proprietary solutions. This will lead to faster innovation and more accessible AI tools.
4️⃣ Short-form AI content will continue growing. As AI-generated media gets better, platforms like YouTube, TikTok, and Instagram will become even bigger drivers of AI adoption.
5️⃣ Quantum computing will start making real-world impact. While still in its early stages, quantum computing is getting closer to solving real business problems. Keep an eye on this space.
Final Thoughts
AI isn’t just another passing tech trend—it’s a revolution. But like any revolution, there will be winners and losers. Companies and individuals that embrace experimentation, open-source innovation, and smart AI strategies will be the ones who come out on top.
If you’re not already subscribed to my YouTube channel or my podcasts, now’s the time to get on board. Let me know in the comments—where do you see AI heading in 2025? 🚀
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- Source: http://www.franksworld.com/2025/02/04/ai-in-2025-the-disruption-has-only-just-begun/?utm_source=rss&utm_medium=rss&utm_campaign=ai-in-2025-the-disruption-has-only-just-begun
