As any of you who’ve already seen my precursor “2025 Look Ahead” piece may remember, we’ve intentionally flipped the ordering of my two end-of-year writeups once again this year. This time, I’ll be looking back over 2024: for historical perspective, here are my prior retrospectives for 2019, 2021, 2022 and 2023 (we skipped 2020).

As I’ve done in past years, I thought I’d start by scoring the topics I wrote about a year ago in forecasting the year to come:

• Increasingly unpredictable geopolitical tensions
• The 2024 United States election
• Windows (and Linux) on Arm
• Declining smartphone demand, and
• Internal and external interface evolutions

Maybe I’m just biased but I think I nailed ‘em all, albeit with varying degrees of impactfulness. To clarify, by the way, it’s not that if the second one would happen was difficult to predict; the outcome, which I discussed a month back, is what was unclear at the time. In the sections that follow, I’m going to elaborate on one of the above themes, as well as discuss other topics that didn’t make my year-ago forecast but ended up being particularly notable (IMHO, of course).

Battery transformations

I’ve admittedly written quite a lot about lithium-based batteries and the devices they fuel over the past year, as I suspect I’ll also be doing in the year(s) to come. Why? My introductory sentence to a recent teardown of a “vape” device answers that question, I think:

The ever-increasing prevalence of lithium-based batteries in various shapes, sizes and capacities is creating a so-called “virtuous circle”, leading to lower unit costs and higher unit volumes which encourage increasing usage (both in brand new applications and existing ones, the latter as a replacement for precursor battery technologies), translating into even lower unit costs and higher unit volumes that…round and round it goes.

Call me simple-minded (as some of you already may have done a time or few over the years!) but I consistently consult the same list of characteristics and tradeoffs among them when evaluating various battery technologies…a list that was admittedly around half its eventual length when I first scribbled it on a piece of scrap paper a few days ago, until I kept thinking of more things to add in the process of keyboard-transcribing it (thereby eventually encouraging me to delete the “concise” adjective I’d originally used to describe it)!

• Volume manufacturing availability, translating to cost (as I allude to in the earlier quote)
• Form factor implementation flexibility (or not)
• The required dimensions and weight for a given amount of charge-storage capacity
• Both peak and sustained power output
• The environmental impacts of raw materials procurement, battery manufacturing, and eventual disposal (or, ideally, recycling)
• Speaking of “environmental”, the usable operating temperature range, along with tolerance to other environment variables such as humidity, shock and vibration
• And recharge speed (both to “100% full” and to application-meaningful percentages of that total), along with the number of recharge cycles the battery can endure until it no longer can hold enough anode electrons to be application-usable in a practical sense.

Although plenty of lithium battery-based laptops, smartphones and the like are sold today, a notable “driver” of incremental usage growth in the first half of this decade (and beyond) has been various mobility systems—battery-powered drones (and, likely in the future, eVTOLs), automobiles and other vehicles, untethered robots, and watercraft (several examples of which I’ll further elaborate on later in this writeup, for a different reason). Here, the design challenges are quite interconnected and otherwise complex, as I discussed back in October 2021:

Li-ion battery technology is pretty mature at this point, as is electric motor technology, so in the absence of a fundamental high-volume technology breakthrough in the future, to get longer flight time, you need to include bigger batteries…which leads to what I find most fundamentally fascinating about drones and their flying kin: the fundamental balancing act of trading off various contending design factors that is unique to the craft of engineering (versus, for example, pure R&D or science). Look at what I’ve just said. Everyone wants to be able to fly their drone as long as possible, before needing to land and swap out battery packs. But in order to do so, that means that the drone manufacturer needs to include larger battery cells, and more of them.

Added bulk admittedly has the side benefit of making the drone more tolerant of wind gusts, for example, but fundamentally, the heavier the drone the beefier the motors need to be in order to lift it off the ground and fly it for meaningful altitudes, distances, and durations. Beefier motors burn more juice, which begs for more batteries, which make the drone even heavier…see the quagmire? And unlike with earth-tethered electricity-powered devices, you can’t just “pull over to the side of the road” if the batteries die on you.

Now toss in the added “twist” that everyone also wants their drone to be as intelligent as possible so it doesn’t end up lost or tangled in branches, and so it can automatically follow whatever’s being videoed. All those image and other sensors, along with the intelligence (and memory, and..) to process the data coming off them, burns juice, too. And don’t forget about the wireless connectivity between the drone and the user—minimally used for remote control and analytics feedback to the user…How do you balance all of those contending factors to come up with an optimum implementation for your target market?

Although the previous excerpt was specifically about drones, many of the points I raised are also relevant at least to a degree in the other mobility applications I mentioned. That said, an electric car’s powerplant size and weight constraints aren’t quite as acute as an airborne system’s might be, for example. This application-defined characteristics variability, both in an absolute sense and relative to others on my earlier list, helps explain why, as Wikipedia points out, “there are at least 12 different chemistries of Li-ion batteries” (with more to come). To wit, developers are testing out a diversity of both anode and cathode materials (and combinations of them), increasingly aided by AI (which I’ll also talk more about later in this piece) in the process, along with striving to migrate away from “wet” electrolytes, which among other things are flammable and prone to leakage, toward safer solid-state approaches.

Another emerging volume growth application, as I highlighted throughout the year, are battery generators, most frequently showcased by me in their compact portable variants. Here, while form factor and weight remain important, since the devices need to be hauled around by their owners, they’re stationary while in use. Extrapolate further and you end up with even larger home battery-backup banks that never get moved once installed. And extrapolate even further, to a significant degree in fact, and you’re now talking about backup power units for hospitals, for example, or even electrical grid storage for entire communities or regions. One compelling use case is to smooth out the inherent availability variability of renewable energy sources such as solar and wind, among other reasons to “feed” the seemingly insatiable appetites of AI workload-processing data centers in a “green”-as-possible manner.  And in all these stationary-backup scenarios, installation space is comparatively abundant and weight is also of lesser concern; the primary selection criteria are factors such as cost, invulnerability, and longevity.

As such, non-lithium-based technologies will likely become increasingly prominent in the years to come. Sodium-ion batteries (courtesy of, in part, sodium’s familial proximity to lithium in the Periodic Table of Elements) are particularly near-term promising; you can already buy them on Amazon! The first US-based sodium-ion “gigafactory” was recently announced, as was the US Department of Energy’s planned $3 billion in funding for new sodium-ion (and other) battery R&D projects. Iron-based batteries such as the mysteriously named (but not so mysterious once you learn how they work) iron-air technology tout raw materials abundance (how often do you come across rust, after all?) translating into low cost. Vanadium-based “flow” batteries also hold notable promise. And there’s one other grid-scale energy storage candidate with an interesting twist: old EV batteries. They may no longer be sufficiently robust to reliably power a moving vehicle, but stationary backup systems still provide a resurrecting life-extension opportunity.

For ongoing information on this topic, in addition to my and colleagues’ periodic coverage, market research firm IDTechEx regularly publishes blog posts on various battery technology developments which I also commend to your inspection. I have no connection with the firm aside from being a contented consumer of their ongoing information output!

Drones as armaments

As a kid, I was intrigued by the history of warfare. Not (at all) the maiming, killing and other destruction aspects, mind you, instead the equipment and its underlying technologies, their use in conflicts, and their evolutions over time. Three related trends that I repeatedly noticed were:

1. Technologies being introduced in one conflict and subsequently optimized (or in other cases disbanded) based on those initial experiences, with the “success stories” then achieving widespread use in subsequent conflicts
2. The oft-profound advantages that adopters of new successful warfare technologies (and equipment and techniques based on them) gained over less-advanced adversaries who were still employing prior-generation approaches, and
3. That new technology and equipment breakthroughs often rapidly obsoleted prior-generation warfare methods

Re point #1, off the top of my head, there’s (with upfront apologies for any United States centricity in the examples that follow):

• Chemical warfare, considered (and briefly experimented with) during the US Civil War, with widespread adoption beginning in World War I (WWI)
• Airplanes and tanks, introduced in WWI and extensively leveraged in WWII (and beyond)
• Radar (airplanes), sonar (submarines) and other electronic surveillance, initially used in WWII with broader implementation in subsequent wars and other conflicts
• And RF and other electronics-based communications methods, including cryptography (and cracking), once again initiated in WWII

And to closely related points #2 and #3, two WWII examples come to mind:

• I still vividly recall reading as a kid about how the Polish army strove, armed with nothing but horse cavalry, to defend against invading German armored brigades, although the veracity of at least some aspects of this propaganda-tainted story are now in dispute.
• And then there was France’s Maginot Line, a costly “line of concrete fortifications, obstacles and weapon installations built by France in the 1930s” ostensibly to deter post-WWI aggression by Germany. It was “impervious to most forms of attack” across the two countries’ shared border, but the Germans instead “invaded through the Low Countries in 1940, passing it to the north”. As Wikipedia further explains, “The line, which was supposed to be fully extended further towards the west to avoid such an occurrence, was finally scaled back in response to demands from Belgium. Indeed, Belgium feared it would be sacrificed in the event of another German invasion. The line has since become a metaphor for expensive efforts that offer a false sense of security.”

I repeatedly think of case studies like these as I read about how the Ukrainian armed forces are, both in the air and sea, now using innovative, often consumer electronics-sourced approaches to defend against invading Russia and its (initially, at least) legacy warfare techniques. Airborne drones (more generally: UAVs, or unmanned aerial vehicles) have been used for surveillance purposes since at least the Vietnam War as alternatives to satellites, balloons, manned aircraft and the like. And beginning with aircraft such as the mid-1990s Predator, UAVs were also able to carry and fire missiles and other munitions. But such platforms were not only large and costly, but also remotely controlled, not autonomous to any notable degree. And they weren’t in and of themselves weapons.

That’s all changed in Ukraine (and elsewhere, for that matter) in the modern era. In part hamstrung by its allies’ constraints on what missiles and other weapons it was given access to and how and where they could be used, Ukraine has broadened drones’ usage beyond surveillance into innate weaponry, loading them up with explosives and often flying them hundreds of miles for subsequent detonation, including all the way to Moscow. Initially, Ukraine retrofit consumer drones sourced from elsewhere, but it now manufactures its own UAVs in high volumes. Compared to their Predator precursors, they’re compact, lightweight, low cost and rugged. They’re increasingly autonomous, in part to counteract Russian jamming of wireless control signals coming from their remote operators. They can even act as flamethrowers. And as the image shown at the beginning of this section suggests, they not only fly but also float, a key factor in Ukraine’s to-date success both in preventing a Russian blockade of the Black Sea and in attacking Russia’s fleet based in Crimea.

AI (again, and again, and…)

AI has rapidly grown beyond its technology-coverage origins and into the daily clickbait headlines and chyrons of even mainstream media outlets. So it’s probably no surprise that this particular TLA (with “T” standing for “two” this time, versus the the usual) is a regular presence in both my end-of-year and next-year-forecast writeups, along with plenty of ongoing additional AI coverage in-between each year’s content endpoints. A month ago, for example, I strove to convince you that multimodal AI would be ascendant in the year(s) to come. Twelve months ago, I noted the increasing importance of multimodal models’ large language model (LLM) precursors over the prior year, and the month(-ish) before that, I’d forecasted that generative AI would be a big deal in 2023 and beyond. Lather, rinse and repeat.

What about the past twelve months; what are the highlights? I could easily “write a book” on just this topic (as I admittedly almost already did earlier re “Battery Transformations”). But with the 3,000-word count threshold looming, and always mindful of Aalyia’s wrath (I kid…maybe…), I’ll strive to practice restraint in what follows. I’m not, for example, going to dwell on OpenAI’s start-of-year management chaos and ongoing key-employee-shedding, nor on copyright-infringement lawsuits brought against it and its competitors by various content-rights owners…or for that matter, on lawsuits brought against it and its competitors (and partners) by other competitors. Instead, here’s some of what else caught my eye over the past year:

• Deep learning models are becoming more bloated with the passage of time, despite floating point-to-integer conversion, quantization, sparsity and other techniques for trimming their size. Among other issues, this makes it increasingly infeasible to run them natively (and solely) on edge devices such as smartphones, security cameras and (yikes!) autonomous vehicles. Imagine (a theoretical case study, mind you) being unable to avoid a collision because your car’s deep learning model is too dinky to cover all possible edge and corner cases and a cloud-housed supplement couldn’t respond in time due to server processing and network latency-and-bandwidth induced delays…
• As the models themselves grow, the amount of processing horsepower (not to mention consumed power) and time needed to train them increases as well…exponentially so.
• Resource demands for deep learning inference are also skyrocketing, especially as the trained models referenced become more multimodal and otherwise complex, not to mention the new data the inference process is tasked with analyzing.
• And semiconductor supplier NVIDIA today remains the primary source of processing silicon for training, along with (to a lesser but still notable market segment share degree) inference. To the company’s credit, decades after kicking off its advocacy of general-purpose graphics processing (GPGPU) applications, its longstanding time, money and headcount investments have borne big-time fruit for the company. That said, competitors (encouraged by customers aspiring for favorable multi-source availability and pricing outcomes) continue their pursuit of the “Green Team”.
• To my earlier “consumed power” comments, along with my even earlier “seemingly insatiable appetites of AI workload-processing data centers” comments, and as my colleague (and former boss) Bill Schweber also recently noted, “AI-driven datacenter energy demand could expand 160 percent over the next two years, leaving 40 percent of existing facilities operationally constrained by power availability,” to quote recent coverage in The Register. In response to this looming and troubling situation, in the last few days alone I’ve come across news regarding Amazon (“Amazon AI Data Centers To Double as Carbon Capture Machines”) and Meta (“Meta wants to use nuclear power for its data centers”). Plenty of other recent examples exist. But will they arrive in time? And will they only accelerate today’s already worrying global warming pace in the process?
• But, in spite of all of this spiraling “heavy lifting”, researchers continue to conclude that AI still doesn’t have a coherent understanding of the world, not to mention that the ROI on ongoing investments in what AI can do may be starting to level off (at least to some observers, albeit not a universally held opinion).
• One final opinion; deep learning models are seemingly already becoming commodities, a trend aided in part by increasingly capable “open” options (although just what “open” means has no shortage of associated controversy). If I’m someone like Amazon, Apple, Google, Meta or Microsoft, whose deep learning investments reap returns in associated AI-based services and whose models are “buried” within these services, this trend isn’t so problematic. Conversely, however, for someone whose core business is in developing and licensing models to others, the long-term prognosis may be less optimistic, no matter how rosy (albeit unprofitably so) things may currently seem to be. Heck, even AMD and NVIDIA are releasing open model suites of their own nowadays…

Auld Lang Syne

I’m writing this in early December 2024. You’ll presumably be reading it sometime in January 2025. I’ll split the difference and wrap up by first wishing you all a Happy New Year!

As usual, I originally planned to cover a number of additional topics in this piece, such as (in no particular order save for how they came out of my noggin):

• Matter and Thread’s misfires and lingering aspirations
• Much discussed (with success reality to follow?) chiplets
• Plummeting-cost solar panels
• Iterative technology-related constraints on China (and that country’s predictable responses), and
• Intel’s ongoing, deepening travails

But (also) as usual I ended up with more things that I wanted to write about than I had a reasonable wordcount budget to do so. Having now passed through 3,000 words, I’m going to restrain myself and wrap up, saving the additional topics (as well as updates on the ones I’ve explored here) for dedicated blog posts to come in the coming year(s). Let me know your thoughts on my top-topic selections, as well as what your list would have looked like, in the comments!

—Brian Dipert is the Editor-in-Chief of the Edge AI and Vision Alliance, and a Senior Analyst at BDTI and Editor-in-Chief of InsideDSP, the company’s online newsletter.

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