AI: Happy New AI-fueled 2024!

...guideposts for the New Year and beyond

I’d like to wish you all a Happy New Year, and thank you all for being a part of this AI Tech Wave journey together on AI: Reset to Zero. Over 220 daily AI posts later, the process to understand, leverage and use AI has barely begun. Hope you’ve all enjoyed this break to catch our collective breath from a roller coaster of an OpenAI/Microsoft driven AI ride in 2023. Today, I wanted to frame and highlight some of the key drivers for AI this year.

Yesterday I highlighted what we may have to look forward to at least in the first half of 2024 in terms of broad trends and opportunities.

In my post-Christmas day ‘A needed Holiday Interregnum’ post, I highlighted some of the challenges and opportunities for the ‘Magnificent 7’ Big Tech companies:

“All the companies large and small are set to hit the ground running at that time. OpenAI, with Microsoft’s assistance, after it’s dramatic end to the first year of ChatGPT’s shot hurt around the world, is poised to close potentially concurrent secondary and primary round of capital raises that may take their valuation to a $100 billion or more.”

“Microsoft CEO Satya Nadella is now more keenly than ever focused on driving their AI partnership with OpenAI forward commercially, while navigating OpenAI board and governance expanding exercise in early 2024.”

“Their rival Anthropic is also finishing up big round of late-year fund raising with two Magnificent 7 big techs Google and Amazon. while racing with OpenAI to put additional Safety safeguards on their otherwise ‘Full-Speed ahead’ plans for next-gen LLM AIs. Both are doing their best to reassure the public and regulators that they have this AI ‘Speed vs Safety’ balancing thing front and center on their operating dashboard.”

“Google is set to ramp up their now announced three-pronged Gemini LLM launch plans at the start of the year. I continue to call Google’s AI shot in 2024 to augment Search with AI despite some market concerns.”

“Apple is ramping up to launch their Vision Pro platform a month ahead of schedule in February instead of March. And likely doing a lot more things on AI small and large in the inimitable Apple way.”

“Nvidia is racing to make sure they can ramp up millions of AI GPU chips and data center infrastructure for both AI training and inference ahead of rivals like Intel, AMD, Qualcomm and so many others next year. TSMC of course is laser focused on making the chips in its fabs for all of the above. And Elon of course is rabidly focused on optimizing all his options AI Grok and others, using his AI morphing global megaphone X/Twitter.”

“Meta led by that other ‘Magnificent 7’ founder/CEO Mark Zuckerberg, is threading the needle well between open and closed source Foundation LLM AI models led by its Llama 2 and Pytorch AI software infrastructure strategy. As I will expand on in future posts, the whole closed vs open AI debate is likely to increasingly be a distinction without a difference. Open source AI efforts will very much be an important driver for innovation worldwide, but every company commercializing AI, large or small, open or closed, will be focused on commercial opportunities around its AI products and services.”

“Investors private and public are still seeing AI as something that helps their portfolios going into the New Year despite the hundreds of billions being deployed far ahead of AI ‘product-market-fit’ and the resultant revenues and profits in the years to follow.”

Today as we get ready to kick off a shortened work week tomorrow, I wanted to highlight a few items from my three year AI roadmap built around my AI Tech Wave tech stack chart below, that are particularly relevant in light of the discussions highlighted above.

To highlight my key points on the three year AI roadmap built off the chart above, I’d like to present them again with updates as needed for the last six months:

1. “Picks and Shovels still rule”: Infrastructure companies (Boxes 1-3) will still be going strong with continued reinvention of the underlying AI ‘Compute’ layers, from chips to data centers to networking to databases to software infrastructure. GPUs should be in easier supply. Compute costs for LLMs will likely show greater efficiencies than expected today. We may have multiple Foundation LLM models with a trillion or more parameters, larger context windows, more memory (GPT 5 and 6 from OpenAI for example), but there likely will be a cast of additional specialized LLMs that provide multimodal and hard reasoning/logic capabilities than currently envisioned. Nvidia and founder/CEO Jensen Huang still will rule AI infrastructure in 2024.

   

2. “Data, Data, Everywhere”: DATA, Box 4 above will likely be a far richer landscape than envisioned today, with far more new pools of Data unleashed, especially from the Edge on local devices. As posited before, Apple has interesting opportunities here, even before it Vision Pro platform, which by 2026 will be in its third year of global rollout. Data will still be the key driver to better AI relevance, with far better reasoning, reliability and safety. And contrary to many industry fears, the AI industry will find plenty more data going forward for both AI augmentation/add-ons for existing technology, and new ‘AI native’ applications, products and services. With better AI UI/UX to use it all.

   

3. “AI Beyond Search”: By 2026, we should now have strong evidence of how consumers and businesses are using LLM AI in applications, services, and even possibly ‘smart agents’ (Boxes 5 & 6 above). And uses of AI far beyond Search and ChatGPT style chat bots will likely be the norm not the exception. I still think Google will break out new Google Gemnini driven AI augmentation across Google Search and its other properties like YouTube, Gmail, Google Docs and so many more that billions use every day. The media of course will continue to laser focus on the Google vs OpenAI/Microsoft race in 2024.

   

4. “Smarter AI Agents”: Smart agents and AI companions/assistants/Copilots in particular are the current holy grail, and the underlying technologies around customization and memory have still to be built at scale. By 2026, we likely get the first possible peek at OpenAI and others driven ‘multimodal’ Smart agent traction at mainstream scale (hundreds of millions of users). Maybe even finally useful Voice AI ones via Alexa, Siri, and Google Nest. And of course more robots small and large, infused with LLM and SLM AI technologies.

   

5. “Reinforcement Loops Emerge at Scale”: The big questions today are of course around the reliability and accuracy of AI, and a lot of promise hangs in the balance around reinforcement learning feedback loops in the billions and trillions. By July 4, 2026, we will likely know if the current aspirations of ‘Sparks’ of super intelligence and AGI were in the ballpark. We’re going to see a gusher of new data driven reinforcement loops from local devices and users. And companies like Apple are likely to be at the fore-front of what’s coming, leveraging next generation Foundation LLM AI models AND what’s being termed SLM or small language model AIs.

   

6. “Regulatory Stability in AI”: Also, regulators around the world will likely have figured out the right balance between fear and opportunity around AI. The EU AI Act maybe in its second or third year, with others like it in the US and elsewhere. Optimistic that startups the world over have opportunities to go head to head with incumbents without undue regulatory friction. And do it open source if they choose. It’s important for regulators everywhere to not pick favorites between open and close, small or large, narrow or wide/general (aka AGI and ‘super intelligence’), and certainly not do much of anything before the actual AI technologies are actually researched, built, and ready for deployment at scale.

   

7. “Working with China“: As I said yesterday, the US-China efforts to ‘thread the needle’ despite the national security and trade issues around technology and geopolitics continue into 2024. What’s been encouraging is that despite a lot of aggressive actions on both sides driven by politics, both sides have so far kept their eye on the longer term ball of their true north longer-term interests to cooperate rather than compete more contentiously. For now the better ‘Cooperation’ focused Prisoner’s Dilemma Game Theory continues in practice.

So, a lot of AI events to watch for this year. From the multi-billion dollar AI infrastructure races and dramas pitting the ‘Magnificent 7’ to compete and cooperate with each other, to the unique head winds in this AI Tech Wave vs previous ones. Especially in terms of deep fears around AI and the resulting regulatory frenzy, to the geopolitics driven re-globalization efforts that are re-routing trade and investments.

Not to mention of course the fundamental reliance of AI technologies on ALL our digital Data online, now and all the data lakes, rivers, and oceans to come. And of course who should be compensated what for those data contributions coming in the form of our usage driven ‘reinforcement learning loops’. Or not due to the long-relied upon legalities around ‘Fair Use’.

All this while our best and brightest are trying to figure out how AI technologies fundamentally work as they scale exponentially, while trying to make them far more reliable, safer, and useful at scale. We’re of course the ones tasked with making sense of it all, as it upends how we’ve done things with traditional software driven products and services, now imbued with probabilistic software.

There is a lot here that is familiar and reusable from patterns in previous technology waves we’ve navigated as investors and users. But a whole lot that is very different. On top of it all AI technologies are also likely to play with our emotional levers as pets do, making us see ‘intelligence’ and ‘thinking’ that are likely no more than math-driven mirages.

But the potential long-term benefits of AI technologies are still alluring, promising, and likely worthwhile. Despite all the endless complexity, unreliability, and scary aspects of it all. This year will not likely give us a lot of the answers we seek, but we will be a lot wiser about it all when it’s time for New Year’s 2025.

Again, I want to thank you all for being a part of this AI journey together. Want to wish you and yours all the best for the New Year ahead. Stay tuned.

(NOTE: The discussions here are for information purposes only, and not meant as investment advice at any time. Thanks for joining us here)