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We hope everyone had a relaxing and meaningful Memorial Day weekend! This week, we are excited to share an insight piece from our very own Chris Guest who leads our SF North Bay chapter and the Traction Design publication. Chris dives into the commoditization of foundation models, how the LLM giants are building differentiation, and the key questions founders should consider when developing their strategic positioning.

Only twice in my adult life has trying a new tech product taken my breath away.

The first was when I unboxed my first-generation iPhone on the day of release. The second was the first time I used ChatGPT.

Although the pace of advancement in AI is faster, smartphones have managed to increase in price over time, while LLM pricing seems headed for zero.

What’s going on here?

How did the most exponential technology in history get reduced to a commodity in just 18 months?

And if it can happen to the titans of LLM creation, with all their talent and money, what does it mean for you and your AI product?

Let’s have a look at one specific factor: positioning.

There have been many great articles recently about the importance of positioning your product as different, not just better and avoiding competition. Check out this from Category Pirates 🏴‍☠️, this from "IRON" Mike Steadman, this from Lenny Rachitsky, or this from myself, to cite just a few.

These articles argue that most entrepreneurs default to “competing” without even thinking about the implications of that strategy. I’d add that this bias is matched by a habit of the customer to compare the products they use or buy.

Even if you try not to engage in the comparison game, you will be sucked into it.

This a problem because product comparison always happens on a continuum of price vs some other feature. As I explain in my recent article Category Proof in Engineering, the extent to which you are “better” limits the extent to which you can charge more. And when your competitors reduce their prices, or raise their quality to reduce your leadership, your margins get pulled down with it.

Lack of pricing power is especially problematic for LLMs because of the massive costs involved in scaling compute, training, and talent.

And yet, just 18 months since the world marveled at the release of ChatGPT, head-to-head comparison of features, capabilities, and pricing is the status quo for all LLMs.

The many leaderboards are emblematic of this.

The default state of customers and analysts is to compare, but we’re currently seeing a new SOTA model being released several times per week, which makes it hard for market commentators to keep track, and hard for prospective buyers to understand the true differentiation.

There are various established comparison methods like MMLU, ARC, HellaSwag, and TruthfulQA. These are also compiled into model leaderboards like HuggingFace’s Open LLM Leaderboard.

This is helpful for the demand side of this market:

But the problem this creates for the supply side of the LLM market is that any leadership is fleeting, lasting only until a competitor beats you on some measure, which in AI happens on a daily basis.

No sooner is a SOTA marvel of AI research and development released, than it is stripped back to a bland comparison of benchmark metrics, and appraised on a feature/price continuum.

This is rational behavior for the buyers in the market, but this problem is compounded by the LLM makers themselves, who fall into the trap of offering the comparison proactively.

Amid the hyperbole of AI enthusiasts and hysteria of AI doomers, I’ve started to notice quite rational concerns being raised about the lack of pricing power in the API market for LLMs.

This also affects the consumer usage market as well:

Should you be in any doubt about how significant this problem is, consider the research from Play Bigger advisors that finds that in any given tech category, just one dominant player takes 76% of all the profit from the category.

This challenges the assumptions of many people that believe a market may be big enough to sustain multiple players. In reality, the next 2-10 challenger brands they see are fighting over just 24% of the scraps.

I recently asked the authors of this study whether they expected the same scenario may play out for the LLMs, to which Lochhead🏴‍☠️ expressed 100% confidence that it would.

As discussed in my recent article Category Proof In Engineering, the escape hatch from the battle field of head-to-head competition is to avoid the default mode of comparison and actively work to be incomparable.

This starts from creating and positioning a product that is so tangibly different from competitors, that it makes no sense to compare it all. When you exist in a category of one, pricing pressure is released.

IOW, the best way to compete is not to.

So how are the makers of the LLMs currently positioning them to counter these risks?

In the parlance of Category Design, Open AI is the Category King. (Though I prefer to say category champion.)

You see this in the language of competitors and commentators alike when they talk of “GPT-grade quality” and in their propensity to default to Open AI as the safest bet.

From Sahar Mor in Top 8 Leaderboards for choosing the right AI model for your task

This level of leadership should grant Open AI a license to focus on their own advancements in communication and disregard competitors entirely. But even they fall into the trap of comparing themselves to others.

This chart from their own release of GPT-4o comparing themselves to other players may be intended to show their competitor’s inferiority, but it actually validates the competitors as being worthy of comparison.

Meta describes Llama almost exclusively by how it is “better” and never by how it is different.

Meta’s own communication of Llama 3 boasts of “The most capable openly available LLM to date” and the consumer-facing Meta AI apps as “we believe Meta AI is now the most intelligent AI assistant you can use for free”

While these were probably intended as confident assertions of their excellence, they are in fact examples of “better” positioning that acknowledge the existence of competitors. Especially as they also name and compare them in their own marketing:

Released just a few weeks ago, Google/Deepmind’s Gemini 1.5 is another remarkable feat of engineering, launched with the most unremarkable positioning:

Such language makes it hard to even argue that Google is playing the “better” game. It almost admits that it is not better.

Most people like to assume that the best product wins. Or if not the best product, then maybe the best team, or the deepest pockets.

This is not going well for the LLM makers, who despite having the world’s greatest resources, find themselves locked in a fierce competitive battle. If this is the case at the model level, what does it look like at the application layer where your product likely lives?

Even more competition, and even less differentiation.

Sample a few categories on There’s an AI for That and you’ll see there are, for example:

Dig deeper by exploring how competitors in these crowded spaces position themselves on Product Hunt, and you’ll find hundreds of similar products jostling with statements like:

…And thousands more just like them, identical, and devoid of any discernible positioning.

There is no way a prospective buyer could choose among so many identikit products, which means there is no way the market will be able to sustain them.

While it is hard to predict exactly how the battle at the LLM layer will play out, it is easy to predict that there will be a bloodbath for undifferentiated products at the app layer.

What saddens me about the commoditization of the LLMs is that they do actually have some very meaningful differences among them.

When viewed at the consumer experience layer, differentiation is even more exciting:

The product differentiation is there, but even that does not overcome the gravitational pull of the market’s instinct to compare, nor the maker’s reflex to compete.

While most LLM makers seem intent on engaging their enemies in open battle, a few LLMs avoid the comparison trap by being categorically different and incomparable:

Though both these products are technically LLMs, you never see them listed on the leaderboards. It just doesn’t seem a valid comparison - and that’s precisely the point.

Achieving radical differentiation through category design is not just about creating a category name. Done properly it starts with identifying a unique POV, and comes full circle with engineering that POV into the product itself.

Here are some quick examples of the opportunities these giants are currently missing…

Anthropic consistently position their work as putting “Safety at the Frontier.” They even have created what sounds like a new category name of “Constitutional AI” for an objectively different approach of 'RL from AI Feedback' (RLAIF) instead of RLHF.

As a user, I enjoy using Claude more and find the writing much better than ChatGPT. Most of my friends and peers agree. So their claims of being “better” seem fair, even without the support of a league table.

The question is why? What I would love to hear from Anthropic is specifically how their POV of Constitutional AI engineered into the product is what makes it better.

Currently Anthropic misses the opportunity. They create company PR that is all about Constitutional AI, but product marketing that is just about “better” stats and features.

Much has been written about Google’s existential threat from AI Answer Engines such as Perplexity. We can already see Google’s AI answers rolling out for select searches as a defensive bid.

But Google also has a unique opportunity to differentiate their LLM to API customers through understanding the search firehose itself. Much as Grok has a unique ability to see what people are tweeting about, Google has an even greater opportunity to see what people seek, whether that’s an answer, a product, an experience, or anything else.

Rather than compete at OpenAI’s game as yet another chatbot, or vs Perplexity as an answer engine, Google has the potential to do something very different that leans into their leadership in search.

Who cares what Gemini’s MMLU score is, if it is the ONLY product that could bring real-time search insights into an experience?

We are currently witnessing the world’s most innovative and remarkable technologies, released at unprecedented pace by the world’s smartest people at the best-funded companies of all time.

And yet, we witness yet another commoditization of the technology and a pricing race to the bottom.

If it can happen to them, it is virtually guaranteed to happen to you. Unless, that is, you make a deliberate decision to differentiate your product to avoid competition.

It’s important to note that positioning is of course not the only factor influencing the commoditization and price pressures facing LLMs.

Additional forces such as compute availability and stickiness of user experience are obviously pivotal. Coincidentally just as I wrote this article, Harry Stebbings’ 20VC released a brilliant podcast episode on precisely this topic.

However, compared to the various external forces mentioned on that episode, the act of deliberately positioning yourself as radically different is far easier, and more within your control.

This means this is where you should start too.

We kicked off the NYC Chapter of The Gen AI Collective this spring, and it’s been an incredible journey so far! Shoutout to our co-leads Prithvi Rajasekaran and Sriram Ramakrishnan for hosting two incredible events in the last couple of weeks bringing together the best and brightest in the NYC ecosystem! We’re just getting started, and we want to thank all of our new community members in the Big Apple for supporting us on this journey to create meaningful relationships and further thoughtful discussions around the future of Generative AI! 🚀

Our SF North Bay (Marin and Sonoma) hosted 2 events in one day at the newly renovated Avant Space in downtown San Rafael. By day we co-worked together, before an evening “Open Mic” style demo night, including everything from side projects to funded startups and unicorn enterprises. 

Are you passionate about AI, meticulous about details, and a wizard at organizing events? The GenAI Collective is on the lookout for an Events Leader to bring our exciting events to life! This is your chance to dive deep into the AI community, enrich your network, and showcase your skills. If you’re ambitious, organized, and ready to make a splash in the AI scene, we want to hear from you. Let’s create unforgettable events together! Reach out at [email protected] 😄

About Chris Guest

Chris Guest is a 3-time startup Chief Marketing Officer turned advisor, specializing in category design positioning for AI products. Chris is also the founder and lead of the SF North Bay (Marin & Sonoma) Chapter of the Gen AI Collective, and writes the Traction Design publication here on Substack.