Static site generators still beat LLMs for one critical reason: scalability


If you’ve just started programming, you may get excited by the powerful tools available in 2026 that help to skip all the legwork. But what if I told you that despite the marvel of LLMs, they’re not the best-in-class tools for generating a static website?

LLMs are not the best-in-class tools for most professions, and web development is no different. Every token costs money, and they’re a bit of a gamble—the results can vary. In comparison, static site generators (SSGs) are time-honored solutions that solve a very specific problem: scalability.

Fundamental differences

Static site generators and LLMs are diametrically opposed

An SSG is a program that compiles a suite of templates into many HTML files. Templates comprise domain-specific syntax (like curly braces), placeholder variables, and HTML—it’s a mix of markup and code. Usually, you write your text content in another markup format (like Markdown), set up your templates, and the SSG will compile your content into HTML. Consequently, the outputs are very deterministic (predictable), with no differences between outputs except for their content.

An LLM is a combination of vectors, mathematics, and a little magic. The full scope escapes me, but I’m confident when I say they’re non-deterministic. They’re models of human language that statistically predict the next token. Blending them with a great deal of context changes their output, which varies between answers.

This contrast between deterministic and non-deterministic represents an obvious conflict. But which one do you choose for building a static website, and why?

Scalability

LLMs will buckle as a static website grows

A relaxed man lounging on an orange beanbag watches as a friendly yellow robot works on a laptop for him, while multiple red exclamation-mark warning icons float around them. Credit: Lucas Gouveia/How-To Geek | ViDI Studio/Shutterstock

If you’re building a small landing page, a simple HTML document may suffice. Displaying information or generating leads doesn’t require a great deal of code reuse, but publishing content does. A typical blog can have hundreds of posts, and larger organizations, tens of thousands. Code reuse is one of the earliest fundamental lessons you learn as a programmer, which I think some (novice) vibe coders don’t understand. LLMs encourage disposable code by making it so incredibly cheap to produce in large quantities or by merely forgetting. It’s difficult to judge the exact outcome.

A client may accept a simple landing page, and it may work for you, but it’s not necessarily the most responsible choice. For example, they may wish to expand from a simple landing page to include business-specific content or to include additional contact/about pages. A small website can quickly grow into a dozen web pages, which presents a dilemma: what approach do you take? Do you vibe code multiple individual pages, or do you find a more scalable solution?

By scalable, I mean designing a system to handle increasing workloads. Take, for example, a new website you wrote for a client. It has a dozen pages. Now the client wishes to display a call to action in a sidebar on every page. An LLM can surely update all 12 pages, but what about the next change, or the change after that? Will you duplicate all of your work 12 times from here until the website ceases to exist?

The website could also expand to hundreds or thousands of pages. Naturally, you see how the workload begins to stack up. Every minor change requires replication across many pages, and subtle bugs will sneak in when using a non-deterministic tool like an LLM. I’ve seen LLMs remove entire blocks of code for no reason, or for reasons I don’t fully understand; either way, the change isn’t entirely within my comprehension. Will you verify every single modification? I’m assuming you won’t have written automated tests either, so will you manually test each file?

Linux mascot sitting on a chip with blurred code in the background.


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SSGs

What works for one works for all

Setting up a simple website with a static site generator can take 20–30 minutes using Hugo. What you get is a deterministic tool that guarantees to replicate code across all pages—what works for one works for all. When you make sweeping changes, you simply update one template file and recompile, which takes only a second. You can even use an LLM to make the change, so the choice isn’t mutually exclusive, and LLMs can be very much part of your workflow.

An SSG is a tool that solves a specific problem. The advent of LLMs does not change the existence of that problem. However, LLMs are not the right tool for the job. Vibe coding a multi-page website without proper tooling is a gross waste of resources, and if you intend to build a static website, there is no better way than an SSG.


SSGs and LLMs are tools at opposite ends of the spectrum. There is no reason they cannot work together, but there’s a powerful case against using LLMs to build a static website. If you’re doing that, and it’s more than a few pages, you need to stop and reassess your approach. Some may say it’s subjective, but so is putting square wheels on a car. If a person wishes to do that, who am I to say? Scratch that; it’s silly. Like square wheels, use the correct tool for the job.

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Recent Reviews


As I’m writing this, NVIDIA is the largest company in the world, with a market cap exceeding $4 trillion. Team Green is now the leader among the Magnificent Seven of the tech world, having surpassed them all in just a few short years.

The company has managed to reach these incredible heights with smart planning and by making the right moves for decades, the latest being the decision to sell shovels during the AI gold rush. Considering the current hardware landscape, there’s simply no reason for NVIDIA to rush a new gaming GPU generation for at least a few years. Here’s why.

Scarcity has become the new normal

Not even Nvidia is powerful enough to overcome market constraints

Global memory shortages have been a reality since late 2025, and they aren’t just affecting RAM and storage manufacturers. Rather, this impacts every company making any product that contains memory or storage—including graphics cards.

Since NVIDIA sells GPU and memory bundles to its partners, which they then solder onto PCBs and add cooling to create full-blown graphics cards, this means that NVIDIA doesn’t just have to battle other tech giants to secure a chunk of TSMC’s limited production capacity to produce its GPU chips. It also has to procure massive amounts of GPU memory, which has never been harder or more expensive to obtain.

While a company as large as NVIDIA certainly has long-term contracts that guarantee stable memory prices, those contracts aren’t going to last forever. The company has likely had to sign new ones, considering the GPU price surge that began at the beginning of 2026, with gaming graphics cards still being overpriced.

With GPU memory costing more than ever, NVIDIA has little reason to rush a new gaming GPU generation, because its gaming earnings are just a drop in the bucket compared to its total earnings.

NVIDIA is an AI company now

Gaming GPUs are taking a back seat

A graph showing NVIDIA revenue breakdown in the last few years. Credit: appeconomyinsights.com

NVIDIA’s gaming division had been its golden goose for decades, but come 2022, the company’s data center and AI division’s revenue started to balloon dramatically. By the beginning of fiscal year 2023, data center and AI revenue had surpassed that of the gaming division.

In fiscal year 2026 (which began on July 1, 2025, and ends on June 30, 2026), NVIDIA’s gaming revenue has contributed less than 8% of the company’s total earnings so far. On the other hand, the data center division has made almost 90% of NVIDIA’s total revenue in fiscal year 2026. What I’m trying to say is that NVIDIA is no longer a gaming company—it’s all about AI now.

Considering that we’re in the middle of the biggest memory shortage in history, and that its AI GPUs rake in almost ten times the revenue of gaming GPUs, there’s little reason for NVIDIA to funnel exorbitantly priced memory toward gaming GPUs. It’s much more profitable to put every memory chip they can get their hands on into AI GPU racks and continue receiving mountains of cash by selling them to AI behemoths.

The RTX 50 Super GPUs might never get released

A sign of times to come

NVIDIA’s RTX 50 Super series was supposed to increase memory capacity of its most popular gaming GPUs. The 16GB RTX 5080 was to be superseded by a 24GB RTX 5080 Super; the same fate would await the 16GB RTX 5070 Ti, while the 18GB RTX 5070 Super was to replace its 12GB non-Super sibling. But according to recent reports, NVIDIA has put it on ice.

The RTX 50 Super launch had been slated for this year’s CES in January, but after missing the show, it now looks like NVIDIA has delayed the lineup indefinitely. According to a recent report, NVIDIA doesn’t plan to launch a single new gaming GPU in 2026. Worse still, the RTX 60 series, which had been expected to debut sometime in 2027, has also been delayed.

A report by The Information (via Tom’s Hardware) states that NVIDIA had finalized the design and specs of its RTX 50 Super refresh, but the RAM-pocalypse threw a wrench into the works, forcing the company to “deprioritize RTX 50 Super production.” In other words, it’s exactly what I said a few paragraphs ago: selling enterprise GPU racks to AI companies is far more lucrative than selling comparatively cheaper GPUs to gamers, especially now that memory prices have been skyrocketing.

Before putting the RTX 50 series on ice, NVIDIA had already slashed its gaming GPU supply by about a fifth and started prioritizing models with less VRAM, like the 8GB versions of the RTX 5060 and RTX 5060 Ti, so this news isn’t that surprising.

So when can we expect RTX 60 GPUs?

Late 2028-ish?

A GPU with a pile of money around it. Credit: Lucas Gouveia / How-To Geek

The good news is that the RTX 60 series is definitely in the pipeline, and we will see it sooner or later. The bad news is that its release date is up in the air, and it’s best not to even think about pricing. The word on the street around CES 2026 was that NVIDIA would release the RTX 60 series in mid-2027, give or take a few months. But as of this writing, it’s increasingly likely we won’t see RTX 60 GPUs until 2028.

If you’ve been following the discussion around memory shortages, this won’t be surprising. In late 2025, the prognosis was that we wouldn’t see the end of the RAM-pocalypse until 2027, maybe 2028. But a recent statement by SK Hynix chairman (the company is one of the world’s three largest memory manufacturers) warns that the global memory shortage may last well into 2030.

If that turns out to be true, and if the global AI data center boom doesn’t slow down in the next few years, I wouldn’t be surprised if NVIDIA delays the RTX 60 GPUs as long as possible. There’s a good chance we won’t see them until the second half of 2028, and I wouldn’t be surprised if they miss that window as well if memory supply doesn’t recover by then. Data center GPUs are simply too profitable for NVIDIA to reserve a meaningful portion of memory for gaming graphics cards as long as shortages persist.


At least current-gen gaming GPUs are still a great option for any PC gamer

If there is a silver lining here, it is that current-gen gaming GPUs (NVIDIA RTX 50 and AMD Radeon RX 90) are still more than powerful enough for any current AAA title. Considering that Sony is reportedly delaying the PlayStation 6 and that global PC shipments are projected to see a sharp, double-digit decline in 2026, game developers have little incentive to push requirements beyond what current hardware can handle.

DLSS 5, on the other hand, may be the future of gaming, but no one likes it, and it will take a few years (and likely the arrival of the RTX 60 lineup) for it to mature and become usable on anything that’s not a heckin’ RTX 5090.

If you’re open to buying used GPUs, even last-gen gaming graphics cards offer tons of performance and are able to rein in any AAA game you throw at them. While we likely won’t get a new gaming GPU from NVIDIA for at least a few years, at least the ones we’ve got are great today and will continue to chew through any game for the foreseeable future.



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