LLM commoditization

Extracted Attributes

• Hardware Trend

  PCs equipped with GPUs and NPUs for running local models

• Economic Metric

  Economic value created by the surrounding ecosystem rather than market share of the model

• Primary Drivers

  Open-source initiatives, developer mobility, and standardized training datasets

• Core Architecture

  Transformer-based neural networks

• Historical Parallels

  Database market, flat-screen TVs, cloud storage, and computing

• New Competitive Advantage

  Integration with proprietary data, customer workflows, and specialized use cases

Timeline

• Alex Tamkin identifies commoditization as one of four possible futures for Large Language Models. (Source: Web Search: Alex Tamkin Essay)

  2022-06-01

• Microsoft CMO Jared Spataro argues that even advanced models like OpenAI o1 will be quickly commoditized due to intense market competition. (Source: Web Search: Microsoft WorkLab)

  2024-10-09

• Tech Monitor reports that core LLM functionality is becoming commoditized as models converge on similar architectures and datasets. (Source: Web Search: Tech Monitor)

  2025-08-07

Figure AI

Figure AI, Inc. is an American robotics company developing AI-powered humanoid robots. The company was founded in 2022 by Brett Adcock, also known for founding Archer Aviation and Vettery.

Web Search Results

• How LLMs are changing business and its implications - LinkedIn

  The Commoditization of LLMs and Its Business Implications 1. Increased Accessibility Large Language Models (LLMs) are becoming more affordable and readily available. This commoditization mirrors past tech trends like cloud storage and computing—once exclusive tools are now accessible to startups, mid-size companies, and enterprise teams alike. Open-source initiatives and smaller, cheaper models are driving this shift. 2. Strategic Differentiation With LLMs becoming standard, the competitive advantage is no longer just about owning an advanced model. It now hinges on how effectively a business integrates LLMs with proprietary data, customer workflows, and use cases to deliver real value. Companies that personalize AI for their needs are leading the way. 3. Specialized Applications The [...] needs are leading the way. 3. Specialized Applications The trend is moving from general-purpose models to smaller, task-specific LLMs designed for particular domains like legal, healthcare, or customer service. These models are more efficient, cheaper to run, and easier to govern, enabling tailored solutions with better performance in specific tasks. 4. Security & Compliance As LLMs are embedded into critical business processes, security risks and regulatory compliance become major concerns. Companies must ensure proper governance, secure infrastructure, and compliance with privacy standards, especially when using models trained on sensitive or proprietary data.

• Four Futures for Large Language Models - Alex Tamkin

  Search this site Embedded Files Alex Tamkin # Essays # Four Futures for Large Language Models June 2022 (original twitter thread) Almost two years after GPT-3 we've seen continued scaling of large language models, with multiple firms joining the fray Here are four possible futures for how these competitive dynamics might shake up over the next few years: 1) Commoditization Current LLMs are mostly trained on publicly available sources, like wikipedia, blogposts, GitHub, online books, etc. GPT-3 was trained on 570GB of such text (for perspective, that's barely enough to fill half of a 1TB microSD card). If this continues, LLMs may end up commoditized, with mostly-interchangeable models available from multiple providers [...] Firms might gain a temporary edge by scaling up their model/data/context length/retrieval bank (The challenges here are nothing to sneeze at, and will likely pose barriers) But, if others can quickly follow suit, this wouldn't fundamentally alter the competitive landscape 2) Market specialization via private data To stave off commoditization, firms might focus on building LLMs for specific applications where private data gives a competitive edge For example, a software company with a large, private codebase might build superior code LLMs Similarly, a hospital system with a large EHR database may have an edge when building a medical LLM And a company with a messaging app may be able to build a better LLM chatbot [...] Unique sources of unlabeled data are likely to become increasingly important for differentiation 3) Dominance through Data Flywheels (aka "Neural Network Effects") Another way to prevent commoditization is to build data flywheels, where user behavior creates unique training data not accessible to competitors For example, when users use an LLM like GitHub Copilot, they can accept or reject proposed completions This produces training data that creates a powerful feedback loop: More people use the model -> the model gets better -> more people use the model These data flywheels can make it challenging for later entrants to catch up: New users will gravitate towards the best existing models, further strengthening them at the expense of the newcomers 4) Disillusionment

• Are LLMs becoming a commodity? - Tech Monitor

  This is perhaps not totally surprising. As Akamai CTO Robert Blumofe points out, “I think pretty much every LLM out there is using the same neural net architecture, the transformer. They are pretraining on pretty much the exact same dataset, which is basically everything you can hoover up off the web.” Differences come in terms of tuning, or reinforcement learning through human feedback, he says. “All of which,” Blumofe argues, “supports a case that the core LLM functionality is, indeed, becoming commoditized.” [...] ## Trust the process? If LLMs are becoming more commoditised, Beckley tells Tech Monitor, the “scaffolding” around them becomes much more important. “The value,” he says, lies “in how you’re able to apply a pipeline of data in a meaningful process that does useful work reliably and safely.” For Appian, that means applying a process or workflow, and being crystal clear about what problems LLMs could and should be set to work on. After all, he says, we’re 15 years on from the subprime mortgage crisis, which was in large part based on foolhardy automation of decision making and a lack of human intervention. “That,” says Beckley, “was a fascinating example of how a toxic algorithm can run amok and almost destroy the world.” [...] AI and automation # Are LLMs becoming a commodity? What should a CIO do when the biggest, baddest AI models all seem to look the same? Joe Fay August 7, 2025 Share this article Copy Link Share on X Share on Linkedin Share on Facebook The idea that large language models (LLMs) have been commoditised might seem absurd. Big tech is pouring billions of dollars into developing gargantuan models like ChatGPT or Llama. Google reportedly spent $191m alone training Gemini Ultra.

• The State Of LLMs 2025: Progress, Problems, and Predictions

  Right now, LLM development is prohibitively expensive and challenging at scale, which is why only a few major companies develop state-of-the-art LLMs. However, I think LLM development is becoming increasingly commoditized, as LLM developers frequently rotate between employers and will eventually be hired by bigger financial institutions, biotech companies, and others with budgets to develop competitive in-house LLMs that benefit from their private data. ​ These LLMs don’t even have to be entirely trained from scratch; many state-of-the-art LLMs like DeepSeek V3.2, Kimi K2, and GLM 4.7 are being released and could be adapted and further post-trained. 8. Building LLMs and Reasoning Models From Scratch [...] 6.1 Coding Today, I still write most of the code I care about myself. With “care about,” I mean in contexts where it matters that I understand the code and that the code is correct. For example, if I set up an LLM training script, I would implement and carefully go over the training logic. This is a) to make sure it’s doing what I think it should be doing and b) to preserve my knowledge and expertise in this task. However, I now use LLMs to add the more mundane code around it, such as adding a command-line argparse boilerplate so I can use my own code more conveniently from the command line. Image 21 _Figure 14: Example adding command line arguments to a training script using the prompt “Add argparse for all hyperparameter options to training-script.py”._ [...] Image 24 _Figure 17: Example of sectors and types of data that could be useful for training domain-specific LLMs, but where selling the data externally would be concerning. (I am not a legal expert, and this is not legal advice, but I can imagine that if it’s a pure local LLM that doesn’t leave the companies’ secure servers, training the model on patient health data is no different than developing other types of internal software that works with that patient health data.)_

• LLMs Are Becoming a Commodity—Now What? - Microsoft

  Technology and commoditization Think of another technology that was groundbreaking for its time: the television. Once a rare luxury made by only a few companies, TVs are now produced by many manufacturers, with excellent models widely available. About two decades ago, flat-screen TVs were coveted and expensive. Now it can cost as much to mount a TV on the wall as it does to buy the TV itself, and “flat-screen TV” has become a redundant phrase. We expect LLMs to follow a similar path to commoditization, but at a swifter pace. [...] Skip to Main Content # LLMs Are Becoming a Commodity—Now What? Future-proofing your organization means looking beyond the latest model. By Jared Spataro, Microsoft CMO of AI at Work October 09, 2024 Whenever a compelling new AI model emerges, I like to put it through its paces. Recently, I’ve been experimenting with the preview of OpenAI o1 (formerly known as Strawberry), an astonishing new LLM that’s capable of solving complex and layered problems, especially in math, science, and coding. For businesses, o1 model and a slew of others in the works represent a clear opportunity. But they also reflect a less obvious challenge: as LLMs become more sophisticated, they’ll also become quickly commoditized, with not a lot of differentiation between them. [...] People across the business world are already experimenting with how o1 can handle tasks like responding to RFPs or performing risk assessments. It’s clear that we’ll look back and consider o1 to be one of the most pivotal advancements in generative AI. So if o1 is such a breakthrough, why am I arguing that models will be commoditized? It comes down to competition. With so much energy and opportunity in the AI space, model developers are racing to exceed one another’s advances. We can expect to see more models, from more providers, with more capabilities on par with one another.