The AI Revolution: Energy, Ethics, and Advanced Packaging

Hi there , I'm Francoise von Trapp , and this is the 3D Insights Podcast . Hi everyone , this week we are recording live from ECTC 2025 at the Texas Gaylord Resort in Dallas , texas , for the 75th edition . We're hearing a lot about the most advanced of the advanced packaging technologies .

What's driving this right now is power-hungry AI , still top of mind for every engineer in this industry , and the keynote speaker today , sam Nafziger , is here to talk about emerging trends aimed at addressing the demand for high-performance computing . Welcome to the podcast , sam , thank you .

So I started out in microprocessor design with a focus on power efficiency improvements and how to extract the most performance per watt out of these devices and I've started to lead that cross-company from a power efficiency perspective and simultaneously have driven a lot of our chiplet architecture approaches and design , which is the way to extract more performance out of

the silicon , the advanced silicon technology processes , and of course that involves deep package technology engagements and advanced development . So I've ended up leading the architecture cross productproduct for the company and sponsoring long-lead technology development .

. You were talking this morning about how fast AI has grown since I think you were talking about since COVID .

I mean , we actually are getting models that can replicate many aspects of human intelligence and , of course , if you consider the most valuable commodity in the world , you know how did we get all the comforts of modern existence and cars and computers and medicine ? Right , it's human intelligence inventing stuff .

So if we can now invent machines that can produce intelligence , it's of uncalculable value to the world , and so that's why there's so much excitement and hype about it . Now , the kinds of intelligence that we're manufacturing are imperfect . Right , we're constantly improving , and that's what , you know , makes us feel exciting is the evolution , the pace of development .

It far exceeds anything I've ever seen in the industry .

and synthesize books , provide a distillation and a summary of complex technical treatises into easily consumable paragraphs that would have taken days for an expert in the field to synthesize down . The models are amazing .

I mean , I heard that chat GPT started out really smart and that it's getting dumber .

So Every AI response needs to be checked right , and putting it in mission-critical applications to make decisions is not a good idea at this point . Right , because the models are not reliable . They're very impressive , but we can't depend on the results .

The ability of ai to synthesize vast amounts of data and come up with useful conclusions from that vastly more data than humans can possibly absorb . You know climate data and histories of crop yields for specific variants in certain regions and the soil types and fertilizers , and you know

just talking about the agricultural aspect and come up with a plan for crop rotation and the appropriate farming techniques that will maximize yields and minimize losses . So just an example .

I mean , you know there's human experts that can do that , but an AI can essentially , for these specific fields , become superhuman in its ability to provide those kinds of recommendations .

But the model developers try to focus on the high quality data that can make the model more intelligent versus just a bunch of random numbers . And yeah , the internet's pretty much been tapped out now for these huge model training exercises . That is somewhat independent of the specialty fields , like I just mentioned say in agricultural medicine ?

And actually it's a good thing because now we're leaning into new approaches that leverage reinforcement learning , approaches that have feedback loops and models , checking models , and we often put humans in that loop as well human reinforcement learning to improve the quality responses , to grade the responses , which is the better response out of the set .

And now , when we can automate that with multiple AIs checking each other and generating synthetic data , we've been making significant strides in the ability of these models to actually reason , not just regurgitate answers . So

the initial LLMs , the GPTs and Geminis they're good at using that vast trove of Internet data . They're trained on to produce the most credible response to a given query . But it's essentially just pattern recognition . It's not really thinking . It's just like give it a response and boom , here's the answer .

It's kind of like system one thinking in the brain where you can recognize faces really quickly . But if you start thinking through , okay , if I , if I see John and I last saw him here what's the right way to respond ? Make a connection with John . You know that's , know that's a reasoning thing .

A system two and we're only just starting to get models that can do that . More deliberative thinking .

So I guess maybe I'm in the middle of an existential crisis around this . So I mean , it's too late . You know the genie's out of the bottle , but should we be rolling this out before we've solved the energy problem ?

If you think about what we are achieving with AI , we are developing machines that can solve the world's hardest problems and actually invent new approaches or identify , I'd say , optimal approaches to transportation , routing , to minimizing power consumption across a myriad of industries , to providing better crop yields , reducing pollution , improving gas mileage , countless things

. So actually I view AI as a . It's kind of like . You know it's a compound interest return . Investments in AI are going to improve human productivity , quality of life , health and actually reduce energy consumption in net . Even if itself is consuming a lot of power , the intelligence we're generating is going to be harnessed for vastly more productivity .

AI . Like I said , the economic demand , the potential of AI to make the world better in countless ways , make our manufacturing processes more more efficient , as well as medical and the health benefits , drug discovery , all these things .

So the more intelligence we can generate with AI , I believe is better for humanity , even though , like any tool , it can be used for good or ill . I believe , by and large , we'll use it for good and we try to marginalize the corrupt uses . And the ability to generate more .

Ai is limited by power , and the package community has a huge role in providing power-efficient connectivity for the silicon chips that are at the core of those compute activities , right ?

So , whether it's the memory , whether it's the compute devices , the GPUs , the accelerators or the networking , getting those components closer together with the most energy efficient connectivity possible , with the best heat conductivity , the lowest resistance for power delivery , all of those sorts of problems are going to enable us to develop AI faster and more effectively ,

which I believe is a net good .

There's lots more to come , so tune in next time to the 3D InCites ast . The 3D InCites Podcast is a production of 3D Insights LLC .