Founder of Baixing.com: The Notion That Large Language Models Will Devour Everything, I Believe Half of It

marsbitPublicado a 2026-07-07Actualizado a 2026-07-07

Resumen

Founder of Baixing.com: I Only Half-Believe the Saying “Large Language Models Will Devour Everything” Author: Wang Jianshuo, Founder of Baixing.com Many proclaim that large models are everything, but the author is skeptical. He argues that such sweeping claims often stem from a limited understanding of the future. Drawing parallels to past technologies like electricity and the internet—which were predicted to “devour everything” but didn’t—he suggests that large language models (LLMs) are better seen as a foundational base. Like electricity, this base is essential for modern development, but its real value emerges only when applied to specific scenarios through various “machines” or “tools” (e.g., Claude Code for programming, Claude Design for design). The author acknowledges that LLMs may indeed replace many existing software systems built on rigid rules, workflows, and forms (e.g., CRMs, SaaS tools), as these are precisely what LLMs excel at processing. However, he emphasizes that beyond software, elements like customer data, execution capabilities (e.g., booking a flight), trust, and physical-world interactions will not be “devoured.” Instead, he foresees that after streamlining existing software, LLMs will open up a larger space for innovative, next-generation applications. These new tools will likely feature fluid interfaces and rely less on fixed rules, unleashing greater creativity. The author cautions against short-sightedness, recalling how in 2004 many believed ...

Author: Wang Jianshuo, Founder of Baixing.com

Many people readily proclaim, "Large models are everything." I don't quite buy it.

Every time I hear phrases like "devour everything," I feel it's mostly because our understanding of the future hasn't reached that level, hence we casually throw out such a broad statement. Otherwise, how could one thing devour everything? Take the internet—it's been shouting about devouring everything for years, has it truly devoured everything now? So which one is it, the internet devours everything, or large models devour everything? Both are devouring, leaving nothing behind?

So I'd rather put it differently: it is a very important foundation. Without this foundation, the whole world can't develop, just like the internet without its underlying backbone network, or like using electricity without power plants. That, I acknowledge.

But after the foundation is in place, that's where the real excitement happens above.

Take electricity. As soon as electricity was generated, what was the first application people saw? The light bulb. Thomas Edison lit the first one, and it just kept shining, shining, shining. If the world stopped there, with only a light bulb, then I could completely say: the power plant is the core of the whole world, the power plant devours everything.

But that's not how things went. Later came motors, to drive machines; and then you find that once a fundamental thing like electricity exists, countless appliances grow on top of it to utilize it. Washing machines are for washing clothes, televisions are for watching TV, vacuum cleaners are for vacuuming—they are all applications of electricity. Without electricity, none of these things would exist. But if you say "electricity devours everything," I don't believe it.

It's the same with large models. They provide foundational intelligence. But this intelligence must be placed into a specific, scenario-oriented "machine" or "device" before it can play its role and truly change the world.

Claude Code is for writing code, Claude Design is for design, VoiceDrop is for writing articles. Using the same large model, placed into different devices, solves completely different problems.

With only electricity, only water, without a washing machine, clothes still can't be washed. Imagine, a power plant produces a massive amount of electricity, electricity is very powerful, and then what? Without a washing machine, can this pile of electricity wash clothes by itself?

Intelligence is great, but most things in the world require the combination of multiple elements to work, just as a washing machine needs to combine elements like electricity, water, and even a tub; large models might be able to replace a lot in the field of software, but application scenarios in the world that require only one element are not many.

Here's a current example. Now we have large models, but having just a large model isn't enough. There needs to be a layer above it called Harness—this layer has only recently emerged—it interacts with code, and finally forms something truly usable. A large model by itself can't write code. Of course, the core of Claude Code, frankly, I could write it with a little over fifty lines; a bit longer with a few more lines, and it can run and write programs. But you have to see: with only a large model and without this outer layer, it's still not easy to use—that is, the intelligence of a large model, if not combined with the code execution capabilities provided by the operating system, relying on the large model for calculations isn't economical, and sometimes even impossible.

The core value of this interface layer is to help us put that intelligence, which is like electricity or water, into a specific application scenario, turning it into a machine that can solve specific problems.

Now, of course, the logic behind "devour everything," I don't completely disbelieve it either.

What it primarily refers to is existing software. So far, we have built up a very, very large layer of software—things pieced together by many rules, forms, buttons, workflows, a lot of it. Massive numbers of filters, fixed templates, a bunch of backend operations, many SaaS detection features. And all the various things we used to know as "M" systems, whether CRM, or HIS (hospital information system), all sorts of so-called "systems," "software," and the like, a whole pile.

This layer, I believe large language models will indeed devour quite a bit of.

Why? Because these original software programs were, by nature, clear, computer-executable instructions, solidified and repeatedly executed—we call this software. And this is precisely what large language models are most adept at chewing through.

But.

Within this layer, besides software, there are many other things. Customer information. Execution capabilities—like when you book a flight ticket, the actual capability to move a plane, to transport a person from here to there. Also trust. Many things in the physical world. These, I don't think will be devoured.

After devouring that layer, it actually opens up a much larger space—the new type of software on top of it.

The interface of the new software will certainly be flow-based, it likely won't solidify as many rules as before. After handing all those rules to AI, think about it, the CRM we could achieve like Salesforce before, that was already the pinnacle of human effort, using all possible strength. But if this part becomes relatively easier to solve, then what everyone will do next is to unleash even more imagination and possibilities on top of it—and that part is precisely what we haven't seen yet.

This is where we often make mistakes. When a new technology arrives, because we can't see the bigger road beyond it, we can only stare at the part in front of us. The leaf blocks the view, obscuring the mountain.

Let alone this kind of trend judgment. I remember back in 2004, a group of friends were complaining, saying that the entire internet could no longer produce companies bigger than Sina, Sohu, and NetEase, that the internet was nearly over, and they would monopolize everything. And look how many years have passed since then? The world turned upside down. We would be crying at our own shortsightedness back then.

So my proposition is this: Are large models important? Yes, they are the foundation, the main driving force in recent times. But once they become stable, continuously provided things, then various kinds of "machines" and "devices" on top are needed to solve specific problems. That thick layer—where it's used, how it's used—is precisely the mainstream of the second wave of this trend.

The phrase "devour everything" is too imprecise. In this world, which thing, which social form, which technology has ever truly devoured everything?

Finding opportunities in the places it does devour—that's the truly important thing.

Criptos en tendencia

Preguntas relacionadas

QWhat is the core viewpoint of the author, the founder of Baixing.com, regarding the statement 'Large language models will devour everything'?

AHe believes it only half. He views large language models as a crucial foundational platform, like electricity or the internet, but argues that they alone cannot 'devour everything.' True value and transformation come from applying this foundational intelligence to specific scenarios through various 'machines' or applications built on top of it.

QHow does the author use the analogy of electricity to explain the role of large language models?

AThe author compares large language models to electricity. Electricity itself is a foundational utility (like a power plant), but it doesn't 'devour everything.' Its real-world impact comes from the diverse array of appliances (like washing machines, TVs) built on top of it to solve specific problems. Similarly, LLMs provide base intelligence, but they need to be integrated into specific applications (like Claude Code for programming) to be truly useful and transformative.

QAccording to the article, what specific layer of existing technology does the author believe large language models are likely to 'devour' or replace?

AThe author believes large language models are likely to replace a significant portion of existing, rule-based, fixed-template software systems, such as many CRM, HIS, and various SaaS applications. These systems are composed of clear, computer-executable instructions, which is precisely what LLMs are good at handling and generating.

QWhat does the author identify as elements that will NOT be 'devoured' by large language models, even as software systems change?

AThe author identifies several elements that will not be devoured: customer information, execution capabilities (like physically moving people or goods), trust, and many aspects of the physical world. These components are essential and exist alongside or beyond the software logic that LLMs might replace.

QWhat is the 'second wave' of the AI浪潮 (wave/tide) that the author predicts will follow the establishment of large language models as a stable foundation?

AThe author predicts the 'second wave' will be focused on the thick layer of applications built *on top* of the large language model foundation. This involves creating the various 'machines' and 'tools' that leverage the base intelligence to solve concrete, specific problems in different domains. This application layer is where the next major phase of innovation and opportunity lies.

Lecturas Relacionadas

Ondo, the Leader in RWA Tokenization, is Entering the Perp DEX Arena

Ondo Finance, the dominant player in the real-world asset (RWA) tokenization space, is entering the perpetual futures (Perp) DEX arena with the launch of "Ondo Perps." This move signifies a strategic pivot from primarily being an asset issuer to becoming a comprehensive trading infrastructure provider. The platform uniquely focuses on tokenized traditional assets like stocks, indices (e.g., US 500), and commodities (gold, silver, oil), offering up to 20x leverage for 24/7 trading. A key differentiator is its planned multi-asset collateral system, which will allow users to employ tokenized stocks and bonds as margin, enabling sophisticated portfolio hedging and strategies. This approach leverages Ondo's existing strengths—deep institutional relationships, regulatory approvals, and a vast library of tokenized assets—to create a bridge between traditional finance liquidity and decentralized, permissionless trading. By integrating its asset issuance (Ondo Global Markets) with a proprietary trading venue, Ondo aims to complete the RWA financialization loop, transforming tokenized holdings from passive investments into productive capital for leverage and complex financial engineering. The launch positions Ondo Perps not just as another derivatives exchange, but as a foundational piece of next-generation infrastructure merging TradFi assets with DeFi-native execution.

Foresight NewsHace 1 hora(s)

Ondo, the Leader in RWA Tokenization, is Entering the Perp DEX Arena

Foresight NewsHace 1 hora(s)

Founder of Baixing.com: I Only Half Believe in the Notion that Large Language Models Devour Everything

The founder of Baixing Wang states that while large language models (LLMs) are an extremely important foundational technology—akin to electricity or the internet—he only "half believes" the notion that they will "consume everything." He argues that LLMs provide a base layer of intelligence, but real-world value and transformation come from integrating this intelligence into specific applications and devices designed for particular scenarios—like how electricity powers various appliances from washing machines to TVs. He agrees LLMs will likely consume or replace a significant portion of existing rule-based, workflow-driven software (e.g., many SaaS systems, CRMs), as these are precisely what LLMs excel at handling. However, numerous other elements—such as customer data, execution capabilities (e.g., booking a flight), trust, and physical-world interactions—will not be consumed. Wang emphasizes that after LLMs absorb certain software layers, they will open up a much larger space for innovation: new types of "streaming" software with less rigid interfaces, where fixed rules are managed by AI. This next wave of applications built on top of the stable LLM foundation is where the true mainstream opportunity lies. He cautions against the short-sightedness of declaring any technology as all-consuming, drawing parallels to past premature predictions about internet giants monopolizing the web. The key is to find opportunities within the areas LLMs do transform.

链捕手Hace 1 hora(s)

Founder of Baixing.com: I Only Half Believe in the Notion that Large Language Models Devour Everything

链捕手Hace 1 hora(s)

Trading

Spot

Artículos destacados

Cómo comprar PEOPLE

¡Bienvenido a HTX.com! Hemos hecho que comprar ConstitutionDAO (PEOPLE) sea simple y conveniente. Sigue nuestra guía paso a paso para iniciar tu viaje de criptos.Paso 1: crea tu cuenta HTXUtiliza tu correo electrónico o número de teléfono para registrarte y obtener una cuenta gratuita en HTX. Experimenta un proceso de registro sin complicaciones y desbloquea todas las funciones.Obtener mi cuentaPaso 2: ve a Comprar cripto y elige tu método de pagoTarjeta de crédito/débito: usa tu Visa o Mastercard para comprar ConstitutionDAO (PEOPLE) al instante.Saldo: utiliza fondos del saldo de tu cuenta HTX para tradear sin problemas.Terceros: hemos agregado métodos de pago populares como Google Pay y Apple Pay para mejorar la comodidad.P2P: tradear directamente con otros usuarios en HTX.Over-the-Counter (OTC): ofrecemos servicios personalizados y tipos de cambio competitivos para los traders.Paso 3: guarda tu ConstitutionDAO (PEOPLE)Después de comprar tu ConstitutionDAO (PEOPLE), guárdalo en tu cuenta HTX. Alternativamente, puedes enviarlo a otro lugar mediante transferencia blockchain o utilizarlo para tradear otras criptomonedas.Paso 4: tradear ConstitutionDAO (PEOPLE)Tradear fácilmente con ConstitutionDAO (PEOPLE) en HTX's mercado spot. Simplemente accede a tu cuenta, selecciona tu par de trading, ejecuta tus trades y monitorea en tiempo real. Ofrecemos una experiencia fácil de usar tanto para principiantes como para traders experimentados.

471 Vistas totalesPublicado en 2024.12.12Actualizado en 2026.06.02

Cómo comprar PEOPLE

Discusiones

Bienvenido a la comunidad de HTX. Aquí puedes mantenerte informado sobre los últimos desarrollos de la plataforma y acceder a análisis profesionales del mercado. A continuación se presentan las opiniones de los usuarios sobre el precio de PEOPLE (PEOPLE).

活动图片