For more than two decades we’ve heard about the death of Moore’s Law. It was a principle of the late Intel co-founder Gordon Moore, positing that the number of transistors in a chip would double about every two years. In 2006, Moore himself said it would end in the 2020s. MIT Professor Charles Leiserson said it was over in 2016. Nvidia’s CEO declared it dead in 2022. Intel’s CEO claimed the opposite a few days later.

There’s no doubt that the concept of Moore’s Law — or rather observation, lest we treat this like some law of physics — has lead to incredible innovation among desktop processors. But the death of Moore’s Law isn’t a moment in time. It’s a slow, ugly process, and we’re finally seeing what that looks like in practice.

Creative solutions

We have two brand new generations from AMD and Intel, neither of which really came out of the gate swinging. As you can read in my Core Ultra 9 285K review, Intel’s latest attempt pulls off a lot of impressive feats with its radically new design, but it still can’t hold up to the competition. And the Ryzen 9 9950X, although a clear upgrade over its Zen 4 counterparts, doesn’t deliver the generational improvements we’ve become accustomed to.

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Consider this — looking at Cinebench R23, the multi-core jump from the Ryzen 9 5950X to the Ryzen 9 7950X was 36%. Between the Ryzen 9 7950X and Ryzen 9 9950X? 15%. That’s less than half the improvement within one generation. In Handbrake, the Ryzen 9 7950X sped up transcoding by 34% compared to the Ryzen 9 5950X. With the Ryzen 9 9950X, the improvement shrunk to just 13%.

This isn’t just one odd generation, either. Looking at the single-core performance of the Core i9-101900K and Core i9-12900K, Intel delivered a 54% improvement. Even comparing the Core i9-12900K, which is three generations old at this point, to the latest Core Ultra 9 285K, we see just a 20% improvement. Worse, the new Core Ultra series from Intel shows oddly high results in Cinebench, and if you break out to other applications, you can actually see some regressions compared to a generation or two back.

Even within just a few years, the rate of performance improvements has slowed considerably. Moore’s Law doesn’t directly talk about performance improvements — it’s simply concerned with the number of transistors on a chip. But that has clear performance implications. Throwing more transistors at the problem isn’t practical like it once was — read up on the death of Dennard scaling if you want to learn more why that’s the case.

AMD and Intel may not talk about it publicly, but both companies clearly see the writing on the walls. That’s likely why Intel pivoted to a hybrid architecture in the first place, and why it’s introduced a radical redesign with its Arrow Lake CPUs. And for AMD’s part, it’s no secret that 3D V-Cache has become a defining technology for the company’s CPUs, and it’s a clear way to skirt the bottleneck of Moore’s Law. A large chunk of transistors on any CPU die are dedicated to cache — somewhere in the range of 40% to 70% — and AMD is literally stacking more cache on top that it can’t fit onto the die.

A function of space

I’m reminded of a conversation I had with AMD’s Chris Hall, where we told me: “We were all enjoying Moore’s Law for a long time, but that’s sort of tailed off. And now, every square millimeter of silicon is very expensive, and we can’t afford to keep doubling. We can, we can build those chips, we know how to build them, but they become more expensive.”

I’m not here to defend Nvidia’s insane pricing strategy, but the company has reportedly seen higher pricing from TSMC with its RTX 40-series GPUs than it saw with Samsung with its RTX 30-series GPUs. And, the RTX 4090 does deliver more than twice the transistor count as the RTX 3090 at a very similar die size. If there’s a commitment to Moore’s Law across chips, I’m not sure we as consumers will like the outcome when it comes time to upgrade a PC.

That’s not to mention the other problems a card like the RTX 4090 has faced — high power requirements, an insane cooler size, and a melting power connector. Not all of these problems are a function of doubling the number of transistors, not even close, but it plays a role. Bigger chips for more transistors, more heat, and usually at a higher cost, especially as the cost of silicon continues to increase.

The shortcut

Moore’s Law is dead, PC hardware is getting more expensive, and everything sucks — that’s not how I want to leave this. There will be more ways to deliver performance improvements year over year that doesn’t rely solely on more transistors on a chip at the same size. The way we’re getting there now is just different. I’m talking about AI.

Wait, don’t click off the article. Tech companies are excited about AI because it represents a lot of money — cynical as that perspective is, it’s just the way trillion-dollar corporations like Microsoft and Nvidia work. But AI also represents a way to bring a new form of computing. I’m not talking about a slew of AI assistants and hallucinatory chatbots, but rather applying machine learning to a problem to approximate results that we would previously get with pure silicon innovation.

Look at DLSS. The idea of using upscaling to maintain a certain level of performance is controversial, and it’s a nuanced conversation when it comes to individual games. But DLSS is enabling better performance without a strict hardware improvement. Add on top of that frame generation, which we now see from DLSS, FSR, and third-party tools like Lossless Scaling, and you have a lot of pixels that are never rendered by your graphics card.

A less controversial angle is Nvidia’s Ray Reconstruction. It’s no secret that ray tracing is demanding, and part of getting around that hardware demand is a process of denoising — limiting the number of rays, then cleaning up the resulting image with denoising. Ray Reconstruction delivers a result that would require far more rays and much more powerful hardware, and it does so without limiting performance at all — and once again, through machine learning.

It really doesn’t matter if Moore’s Law is dead or alive and well — if companies like AMD, Intel, and Nvidia want to stay afloat, they’ll continually need to think of solutions to address rising performance demands. Innovation is far from dead in PC hardware, but it might start to look a little different.

Uhh, OpenAI? Are you okay? It seems like the AI juggernaut OpenAI just can’t hold onto its top minds. The increasing number of departing executives seems to be the latest story arc in an ongoing saga of OpenAI drama. Miles Brundage, OpenAI’s senior adviser for the readiness of AGI (Artificial General Intelligence), just announced that he’ll be leaving the company.

Top executives leaving a company is nothing new, but it seems that one walks out of OpenAI once every 1 – 2 months at this point. We’ve already seen Ilya Sutskever (chief scientist), Jan Leike (former Superalignment team leader), John Schulman (Co-founder), and Peter Deng (VP for consumer product) leave the company over the year. Also, the company’s chief technology officer and interim CEO during OpenAI’s massive coup last year, Mira Murati, left just recently.

Miles Brundage is now leaving OpenAI

Another departing leader, another heartfelt post; Miles Brundage posted on X Wednesday that he’s departing OpenAI. He’s been there for six years, which was long before many people knew that OpenAI even existed.

Along with his post on the platform, he left a TLDR for a blog post talking about what’s going on. According to the post, he’s not leaving on bad terms. He said that OpenAI will “support [his] work in various ways.” So, we’re sure that he’ll have some backing for his future endeavors. Unlike some other past employees who went on to other major companies, Brundage has decided to focus on independent AI policy and advocacy.

People who are interested can read his blog post. In it, he dives into topics such as whether companies are ready for AGI, whether people should work at OpenAI, where he’s going from this point on, and more.

With this role vacant, it seems that OpenAI has some hiring to do. The company hasn’t made any posts about Brundage’s departure or who they’re going to replace him with.

What’s going on at OpenAI?

There’s no telling if these individuals are leaving because they’re seeking and better opportunities or there’s something else going on. OpenAI seemed to be rather drama-free for the first year after ChatGPT blew up. However, since the huge coup that saw Sam Altman ejected from OpenAI for a weekend, we’ve been hearing some rather unsettling rumors regarding the company.

Some people who left talked about the toxic work environment they had to endure and the fact that the company deprioritized AI safety. We also can’t forget about the serious dirt that surfaced about Sam Altman and his actions.

It’s just so coincidental that all of these rumors are leaking from the company and head executives are dropping like flies. Only time will tell if there’s some drama brewing at the company.

Another day, another publication contributes to the rise of AI. First, the likes of Time and Dotdash Meredith partnered with OpenAI to license their property, now Reuters is giving Meta’s AI chatbot access to its news content for responses to current events and news questions, Axios first reported.

Basically, the multi-year deal allows users in the US to now receive real-time news details from Meta’s AI chatbot tool, with these answers citing and linking out to Reuters’ relevant stories.

This deal marks Meta’s first AI news deal, but Reuter’s has worked with the company as a fact-checking partner since 2020. “We’re always iterating and working to improve our products, and through Meta’s partnership with Reuters, Meta AI can respond to news-related questions with summaries and links to Reuters content,” a Meta spokesperson said.

The pair have not disclosed whether Meta will get access to Reuters’ library to train its learning language model, Llama. The exact figures of the deal also aren’t clear, but sources report that Reuters is receiving compensation for this access. Money isn’t the only form of payment companies have made in such deals with the devil — *coughs* AI (Lionsgate receives a custom AI model for production and editing in its agreement with Runway).

Non-English game developers may soon be looking toward AI as a means to shorten localization times.

In an interview with 4Gamer (translated by Siliconera) at Tokyo Game Show, Nihon Falcom president Toshihiro Kondo posited the idea of using artificial intelligence to more quickly localize games developed in the Japanese language. Kondo took the stage after a demonstration of ELLA, software created for the purpose of localizing a game’s text in multiple languages, using it in a hypothetical instance for Nihon Falcom’s Legend of Heroes: Kai no Kiseki, another Falcom game known for immense scripts.

Kondo stated that he believed this process of AI translation would speed up translating games into multiple languages, though he thought that a human still needed to make a final pass on the script before it gets applied to the games. He also acknowledged that there’s cultural pushback to using AI in game development and that it could cost jobs.

He also added that even Falcom staff in non-localization divisions, such as designers and artists, push back against using AI. There is a fear from artists that their illustrations will be used for AI learning that they do not consent to. Kondo is hoping these objections can be solved in the future, as he believes that localization AI will be a benefit.

Nihon Falcom, as Kondo notes, does indeed take a lot of time between Japanese-language releases and localizations. The upcoming Ys X: Nordics released in Japan over one year ago but will only release in America in late October. On the other hand, the release of Ys VIII in 2018 required localization company NISA to formally apologize and redo the entire game’s script due to a messy and fast translation, so acting in the name of expediency has proven negative for Falcom games in the past.

Still, localization through AI has been feeling inevitable for the translation industry for some time, so perhaps Kondo is thinking ahead of the curve.

I recently started using an iPhone 15 after five years away from Apple smartphones, and as my prior pieces on the iPhone’s lackluster 60Hz screen and satisfyingly nostalgic form factor may have let on, coming back to iOS has been a bit of a rollercoaster.

There is one thing about the iPhone that I’m unreservedly happy about, though, a feature that I’m happy to call the best in its class.