I love LLMs, I hate hype

Why are LLMs so hyped? That question cuts to the heart of a peculiar cultural split: the same technology inspires genuine, defensible enthusiasm from working engineers and apocalyptic prophecy from venture-funded evangelists — often in the same breath. George Hotz, the hacker better known as geohot, founder of comma.ai and tinygrad, crystallized the tension in a July 2026 blog post titled "I love LLMs, I hate hype" — and the argument he makes is worth examining carefully, because it separates the real from the ridiculous more cleanly than almost anything else published this year. This article unpacks that argument, situates it against the broader technical record, and tries to answer the questions that keep surfacing across forums, engineering blogs, and board meetings: are LLMs at their limit, are they useless, is using them unethical, and why does the hype feel so suffocating even when the tools themselves are genuinely good?

The Reddit Reality Check: Where the Discourse Actually Lives

Before reaching the technical arguments, it is worth acknowledging where most people actually encounter AI discourse: comment threads. The love LLMs hate hype sentiment is not confined to long-form blog posts. On Reddit, the same tension Hotz describes plays out in real time across communities like r/LocalLLaMA, r/MachineLearning, and r/programming. Posts titled "LLMs are useless" sit alongside posts claiming AGI is months away. Neither camp is monolithic: the "useless" posts usually describe a specific failure mode — hallucinated citations, broken code generation for niche frameworks, or chatbot sycophancy — while the "AGI is near" posts usually cite a cherry-picked benchmark. Both reactions are responses to hype, not to the technology itself. The people caught in the middle — those who use LLMs daily, find them genuinely useful, and remain skeptical of singularity narratives — are the love LLMs, hate hype contingent, and they are, as this article will argue, the most epistemically honest participants in the conversation.

The Two Flavors of AI Hype (And Why Both Are Corrosive)

Not all hype sounds the same. Hotz identifies two distinct strains that dominate AI discourse, and naming them precisely helps dissolve much of the ambient confusion.

Fear-Mongering as a Business Model

The first strain is what Hotz calls "negative valence hype" — the constant drumbeat of anxiety about windows closing, irreversible competitive disadvantage, and a coming permanent underclass of people left behind by AI. As he writes in his post:

"This constant bullshit about some window closing, or the perpetual underclass, or falling hopelessly behind…it's mostly designed to make you feel bad about yourself and move to shitty San Francisco."

This kind of messaging is not accidental. It is engineered to manufacture urgency — the same psychological mechanism behind Black Friday countdowns and subscription upsells. Applied to AI, it drives enterprise procurement decisions before tooling is ready, pushes developers onto half-baked platforms, and creates a cultural climate where skepticism reads as naivety rather than rigor. The Reddit threads lamenting AI hype — the people who identify as loving LLMs while hating the hype — are reacting, often correctly, to exactly this manufactured panic.

The Apocalypse-or-Nothing Fallacy

The second strain is arguably more insidious: the rhetorical leap from "this is a genuinely useful tool" to "this will own the entire light cone of future civilization." Hotz names the pattern directly — critics describe LLMs as fancy autocomplete, a smarter search engine, or a better compiler, and are immediately accused of not seeing the big picture, as though the only alternative to dismissal is eschatological enthusiasm. He is blunt about where he stands: "I'll bet you everything I have that this doesn't happen."

This binary framing — either LLMs herald the end of human cognitive labor or skeptics are Luddites — forecloses the nuanced middle ground where honest technical analysis lives. It is the same dynamic visible in Reddit communities where "LLMs are useless" posts and "AGI is eighteen months away" posts alternate without either camp engaging seriously with the other's evidence. Neither pole is intellectually serious. Both are reactions to hype rather than evaluations of the technology.

What Frontier Labs Are Actually Selling You

Here is where Hotz's analysis sharpens into something genuinely important, and where the financial incentives behind AI hype become legible. His core claim is not that AI won't create enormous value — he explicitly believes it will. The claim is more precise and more damaging to current market narratives:

"It's not that AI won't create that much value, it's that they won't capture it."

The distinction matters enormously. Much of the progress driving LLM capability improvements, Hotz argues, is not the unique intellectual contribution of any particular frontier lab. It is the continuation of Moore's Law and general-purpose computing progress — the same compounding hardware improvements that have made every generation of software more powerful than the last. Labs that raised billions of dollars on narratives of proprietary breakthroughs are, in his framing, beneficiaries of a rising tide they did not create and cannot dam. As he puts it directly: "AI is something that's happening mostly due to Moore's law and general progress in computing, not something that they are doing."

The market is already providing supporting evidence. Sebastian Raschka's 2025 LLM State of the Union documented that DeepSeek trained its V3 model for an estimated $5 million in compute credits for the final training run — not the $50–500 million range previously assumed for frontier-class models — and that its R1 reasoning fine-tune cost approximately $294,000. (Raschka explicitly notes that the $5M figure covers only the final compute run, excluding researcher salaries and hyperparameter search.) Open-weight models including Meta's Llama 4 and DeepSeek R1 are, as multiple analysts have observed, now "reasonably close" to proprietary models across a widening range of workloads.

Hotz makes the political economy explicit: anti-open-source arguments from frontier labs, typically packaged as safety concerns or geopolitical risk warnings, are fundamentally about fear of commodification. "Of course they have a strong incentive against you finding this out, because then you might not want to give them billions of dollars." This is no longer a fringe position. A growing number of investors and analysts have publicly flagged that frontier labs may fail to capture long-term value as open-source alternatives close the performance gap on common workloads — the same commodification dynamic that has already played out in databases, operating systems, and cloud infrastructure.

LLMs Are Not AGI — And That Is Fine

It bears stating clearly, because the conflation is so pervasive: LLMs are not AGI. They are not on a demonstrated path to AGI. Failing to hold this distinction is responsible for a disproportionate share of both the breathless enthusiasm and the equally breathless backlash — including the Reddit communities where "i hate LLMs" posts are frequently written by people who actually hate the claims made about LLMs rather than the tools themselves.

Andrej Karpathy, in his 2025 year-in-review, described LLMs as displaying "jagged intelligence" — simultaneously demonstrating expert-level capability in some domains while remaining trivially manipulable or confused in others. In his words, they are "at the same time a genius polymath and a confused and cognitively challenged grade schooler, seconds away from getting tricked by a jailbreak to exfiltrate your data." He characterizes LLMs not as evolving animals on the path to human-like intelligence, but as "summoned ghosts" — genuinely novel entities whose architecture, training data, and optimization pressure are so different from biological intelligence that human cognitive categories simply do not apply cleanly. Benchmarks, he notes, have become systematically unreliable: because Reinforcement Learning from Verifiable Rewards (RLVR) allows models to be trained against verifiable test conditions, high benchmark scores increasingly reflect proximity to training distributions rather than genuine generalization — a process he calls "benchmaxxing." Raschka's review documented the same divergence precisely — Llama 4 scored impressively on standard benchmarks but underperformed in real-world deployment, a gap that captures the benchmark-gaming dynamic in concrete terms.

The scaling picture reinforces this reading. Pre-training scaling — making models larger on more data — still produces improvements, but as Raschka documents, it is no longer the most cost-effective lever and no longer yields the dramatic qualitative shifts it once did. GPT-4.5 was widely cited in the industry as evidence that "pure scaling alone is not generally the most sensible way forward," with its increased training budget described as "bad bang for the buck" relative to the capability gains it delivered. Progress in 2025 and into 2026 has shifted toward inference-time scaling (spending more compute during generation rather than training) and RLVR post-training — both meaningful advances, but neither constitutes a path to artificial general intelligence. The question of whether LLMs are at their limit therefore has a carefully qualified answer: the naive pre-training scaling limit is being approached; the broader capability frontier, expanded by inference-time compute and improved post-training, has not been exhausted. That is a meaningful distinction, but it is a long way from AGI.

The Honest Case for Loving LLMs

None of the above should be read as a case against LLMs. Hotz is explicit and specific that he genuinely loves them — and his reasons are grounded in engineering reality rather than marketing copy, which makes them more credible and more durable.

He runs local models. At the time of writing his post, he had set up a Linux machine running opencode — an open-source AI coding environment — connected to a local GLM-5.2 model instance, enthusiastically declaring it "the Year of the Linux Desktop." That phrase is a decades-old running joke in open-source communities, used to describe the perpetually-deferred promise that Linux would finally go mainstream on personal computers. Hotz deploys it ironically and affectionately: local AI tooling has finally become good enough to use daily, in the same way Linux on the desktop became genuinely viable long after the jokes stopped being funny. He uses AI coding tools and acknowledges real, measurable productivity gains — not as a concession to the hype cycle, but as an honest report from the workbench.

Karpathy's review documents the same phenomenon at a broader level. Vibe coding — building functional software from plain-English descriptions — has crossed a genuine threshold of practical usefulness, and agent tools like Claude Code can now manage extended, multi-step engineering tasks on a developer's local machine with meaningful reliability. Karpathy describes Claude Code as "the first convincing demonstration of what an LLM Agent looks like — something that in a loopy way strings together tool use and reasoning for extended problem solving," noting that its value comes specifically from running on the developer's own machine with access to their private environment, data, and context. These are real capability shifts. They are just not the same thing as the end of human cognitive labor.

Hotz's frame for LLM utility is deliberately modest and more durable for it. He compares LLMs to find-and-replace, to Stack Overflow, to regex — tools that are genuinely useful, that change how you work, but that do not upend the nature of expertise or eliminate the need for judgment. To anchor the category distinction, he cites what he presents as a Linus Torvalds quote: agents make programming 10× more productive, but compilers made programming 1,000× more productive. The point is not the exact numbers; it is the categorical gap. Even generous productivity estimates for LLMs place them in the tier of important, workflow-changing tools rather than civilizational ruptures. That is a meaningful contribution. It just carries a very different financial valuation than "the entire light cone of future value."

Karpathy's framing adds a social dimension worth noting: LLMs empower non-professionals more dramatically than they empower professionals, inverting the usual technology diffusion pattern. A non-programmer who can now build a functional application via natural language gains proportionally far more than a senior engineer who was already highly productive. That is a genuine, defensible social good. It is just not the same thing as replacing all knowledge work.

Is Using LLMs Unethical? An Honest Accounting

The question is using LLMs unethical surfaces persistently in technical communities and deserves direct treatment rather than dismissal in either direction. Engineer and writer Nicole Tietz-Sokolskaya tackled it at length and reached no clean verdict — which is itself informative about the genuine complexity involved. Her conditional conclusion: "I think it is unethical to use them without addressing the ethical questions above. If you're not working on mitigating the harms from LLMs (which do exist), then you might be doing something unethical."

Concern	The Case Against	Mitigating Context
Energy & Water Use	Large cloud inference queries consume significant power; new AI data center demand has been met partly by gas-fired generation	Local models on efficient hardware have minimal per-query climate impact; Tietz-Sokolskaya notes that training costs are one-time and amortized, and that advocating for remote work would likely do more for climate outcomes than abstaining from LLMs entirely
Training Data & Copyright	Most frontier models were trained on data scraped without explicit creator consent; opt-out mechanisms were largely absent at the time of training	U.S. courts have been actively litigating fair-use claims in multiple cases involving AI training data; outcomes remain unsettled as of mid-2026. The more acute practical concern may be using models to wholesale replace original creators rather than as a compositional tool
Labor Displacement	Writers, illustrators, and software contractors face genuine economic pressure from LLM-powered substitutes	Technological displacement is not inherently unethical in itself, but the absence of structural mitigation — retraining support, income bridges — may be; Tietz-Sokolskaya argues the ethical weight falls more heavily on platform companies and policymakers than on individual users
Hallucination & Bias	Confident fabrication causes real harm in high-stakes contexts; bias encoded in training data has poorly understood downstream effects on underrepresented groups — Tietz-Sokolskaya describes this as "a large harm to all underrepresented groups"	Deployment context is decisive: using LLMs for verified code generation or creative brainstorming carries fundamentally different risk than using them for medical diagnosis or legal advice without verification
Power Concentration	A small number of companies control the largest models and the training decisions that shape their outputs, creating asymmetric cultural and economic influence — a concern Tietz-Sokolskaya describes as particularly dangerous in a political climate where governments are actively erasing marginalized groups	Open-weight models — including Llama 4, DeepSeek R1, and Mistral variants — provide meaningful alternatives; the competitive and ideological landscape is substantially less monolithic than it appeared in 2023

Tietz-Sokolskaya's conclusion — that using LLMs without actively working to understand and address their associated harms may itself constitute a form of ethical passivity — is serious and worth sitting with. At the same time, calls to stop using LLMs entirely rarely survive contact with the full accounting above, particularly once local, open-weight models enter the picture. The ethical stakes are real, unevenly distributed across use cases and model types, and not resolved by individual abstention. They are resolved, to the degree they can be, by informed deployment choices, support for open alternatives, and pressure on the policy and corporate actors who shape structural conditions.

The Productivity Gap That No One Wants to Answer

Perhaps the most underrated challenge Hotz raises is an empirical one. If LLMs have genuinely made programming significantly more productive — and there is real evidence that they have, at least for bounded task types — a reasonable observer would expect to see that productivity reflected in aggregate software output. His question is worth quoting directly:

"Where's all this new magical software that the productivity improvements should imply?"

This is not a rhetorical dismissal. It is a serious measurement problem. Vibe-coded applications exist and are proliferating, but as Hotz notes with characteristic bluntness: "all the vibe coded stuff is still slop." Karpathy's review captures the same dynamic from a different angle: LLMs have enabled a class of software that non-programmers can now build, substantially lowering the floor of what requires professional intervention. But the upper bound of software quality — systems requiring deep architectural reasoning, accumulated domain expertise, multi-year maintenance horizons, and long-horizon planning — has not demonstrably shifted. The floor has dropped; the ceiling has held.

Hotz also flags a real cost that the productivity narrative tends to elide: working with AI-generated output can increase cognitive fatigue. As he writes, "you have to be really careful, they can increase cognitive fatigue." Constant context-switching between evaluating AI output and performing genuine problem-solving imposes a mental overhead that pure task-completion metrics do not capture. If an engineer finishes a feature in half the calendar time but arrives at the end of the day more mentally depleted, the productivity gain is real but partial — and may not compound the way the hype implies.

These observations are not arguments against using LLMs. They are arguments against accepting uncritical productivity claims at face value — precisely the epistemic discipline that should apply to any tool, and that the love LLMs, hate hype position demands.

Frequently Asked Questions

Why are LLMs so hyped if they have real limitations?

Because hype and usefulness are not mutually exclusive. LLMs generate genuine productivity gains for specific tasks, which provides a credible foundation for enthusiasm. That foundation is then amplified by financial incentives — venture capital valuations, enterprise sales cycles, and media attention all reward expansive claims over measured ones. The result is a technology that is legitimately useful and extravagantly overpromised at the same time.

Are LLMs at their limit?

The naive pre-training scaling limit — simply making models larger on more text — is being approached and is no longer the dominant lever for improvement. That is not the same as a general capability ceiling. Inference-time scaling and RLVR post-training are active frontiers producing real gains, particularly in math and structured reasoning. The honest answer is: the cheap scaling is over; meaningful progress continues but requires more targeted and expensive interventions.

Are LLMs useless?

No — but their usefulness is task-specific and often overstated for complex, long-horizon work. For code completion, boilerplate generation, summarization, translation, and natural-language-to-code for well-defined problems, they deliver measurable value. For tasks requiring deep domain expertise, sustained logical consistency across long contexts, or judgment under genuine uncertainty, their reliability drops sharply. "Useless" is as wrong as "magical."

Should I stop using LLMs?

Not on the basis of the ethical concerns alone — the case is more nuanced than that, as the table above illustrates. You should, however, be deliberate about which models you use (open-weight where possible), what tasks you delegate to them (verify outputs in high-stakes contexts), and whether your use substitutes for human creative and intellectual labor in ways that concentrate harm on specific communities.

LLMs are not AGI — so what are they?

They are powerful statistical models trained to predict text, subsequently shaped by reinforcement learning to be more useful and less harmful. They exhibit jagged, uneven competence that does not map onto human intelligence categories. They are genuinely novel tools — what Karpathy calls "summoned ghosts," entities whose architecture and optimization pressure are so different from biological intelligence that they should not be evaluated through a human cognitive lens. What they are not is a prototype of artificial general intelligence, a near-term existential risk, or a replacement for expert judgment in domains where stakes are high.

Key Takeaways

• LLM hype comes in two forms: fear-mongering engineered to manufacture urgency, and messianic singularity narratives that leap from "useful tool" to "civilization-ender." Both are epistemically dishonest and financially motivated.
• Frontier labs face a commodification threat they are actively obscuring. Much of AI progress derives from general computing improvements and Moore's Law rather than proprietary breakthroughs — and open-weight models trained for a fraction of proprietary budgets are closing the performance gap on common workloads rapidly.
• LLMs are not AGI and display no demonstrated path toward becoming so. Jagged intelligence, systematic benchmark gaming, and the exhaustion of naive pre-training scaling all describe a technology that is genuinely powerful and genuinely bounded — not a prototype superintelligence.
• The honest case for loving LLMs is a tools case: useful in the way regex, Stack Overflow, and compilers are useful — transformative at the margin, not transformative in the civilizational sense. The Linus Torvalds quote Hotz cites — agents as a 10× productivity gain versus compilers as a 1,000× gain — captures the category gap cleanly. Local, open-weight models make the adoption case stronger, not weaker, by reducing the ethical and financial stakes of adoption.
• The ethics of using LLMs are real but not binary. Energy consumption, training data provenance, labor displacement, and power concentration are legitimate concerns whose severity varies sharply by deployment context and model type. Individual abstention is not the only ethical response, and passive indifference is not an ethical response at all.
• The productivity gap is an open empirical question. Real productivity gains exist and are documented; the aggregate software output that should accompany them at scale is not yet visible. Cognitive overhead and output quality limits are underreported costs that complicate the productivity narrative.
• The love LLMs, hate hype position is not a contradiction — it is the most intellectually defensible stance currently available. Tools can be genuinely useful and genuinely overhyped at the same time. Holding both truths simultaneously is not fence-sitting; it is accuracy.

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The next twelve to eighteen months will likely force a reckoning with the gap between what the hype has promised and what the output actually shows. Inference-time scaling and RLVR extensions are real technical levers, and both Karpathy and Raschka expect them to drive meaningful capability gains — particularly in mathematics, code, and structured reasoning domains where verifiable rewards are available. But if the productivity dividend promised by AI-assisted engineering does not begin to appear in measurably better or faster software at the aggregate level, the backlash currently contained in "LLMs are useless" Reddit threads will migrate into boardrooms and budget reviews. The technology is good enough to survive that scrutiny — the genuine utility is there and is not going away. The hype, built on financial incentives and category errors rather than engineering reality, almost certainly is not. The love LLMs, hate hype position is not a hedge. It is the read most likely to age well.