Chris Vanderhook

Co-Founder & COO @ Viant. Co-Founder @ XUMO. Steward of Myspace. Family & Baseball for fun.

[Chris Vanderhook @cvanderhook]

Elon and Jensen are tight with each other. More importantly they have a true partnership even if they may appear to compete on GPU’s. They bet on each other for each other’s success. This type of partnership is extremely rare in the digital advertising industry. Everyone wants to eat everyone 🤔

[Elon Musk @elonmusk]

SpaceX has committed to using Nvidia GPUs exclusively because they are the best

[Chris Vanderhook @cvanderhook]

If inventory is the same and 43% cheaper on OpenPath but CPA/ROAS is ~30% worse then it’s either not the same inventory, not the same audience or it’s actually 30% more expensive all-in when you include all other fees if attribution methodology is consistent across OpenPath and SSP path 🤔

[Chris Vanderhook @cvanderhook]

@ShaanVP Lazy take

[Chris Vanderhook @cvanderhook]

Software is on sale at a major discount . I think we will see more of these incredible “opportunity buys” in the short term. The Saaspocalype trade will abate and software multiples of strong businesses will rebound. Bending Spoons could do very well on this trade.

[Deedy @deedydas]

Wow. Airtable, founded in 2012 and once valued at $11.7B, is getting acquired by Bending Spoons, founded 2013, at 2.7x ARR.

A once hot startup; now an unfortunate victim of the SaaS bust. It raised $1.4B only to be sold for $1.285B EV ($2.25B equity value), just clearing its preference stack.

That implies common and early holders split the remaining ~$850M, a ~10x haircut from peak.

[Chris Vanderhook @cvanderhook]

@ProgrammaticJoe @AdtechGod @EricTilbury_RTB @Cassin75 @TimVHook You’re literally describing how every O&O asset does off platform identity today. And yes, we (“charlatans”) were early movers of that strategy in 2013. It worked really well until traffic became too low which ended MySpace as a part of our identity strategy.

[Chris Vanderhook @cvanderhook]

@boardyai I only talk to AI’s

[Chris Vanderhook @cvanderhook]

Adtech and other vertical SaaS companies that own real customer workflows are positioned to thrive, not get disintermediated. The “LLMs will eat all software” thesis is cracking fast.

Kimi K3 and the accelerating open-source frontier are reshaping the AI narrative. While Kimi K3 is currently 90% less expensive per token compared to frontier models, it's more expensive per task than GPT-5.6 variants due to lower token efficiency, that gap is expected to close rapidly as open models improve on intelligence density per token. The bigger story is the surge in credible competition at the model layer, which is eroding the idea that 2-3 vertically integrated AI companies would eventually replace most software companies. This creates real optionality and healthier economics across the full 5-layer AI cake. Lower model-layer margins mean more value and capital flowing to infrastructure, chips, cloud, and, yes, the vertical application layer.

[Gavin Baker @GavinSBaker]

Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. Although the real “Sputnik moment” would be an open-source frontier model that was also token efficient unlike Kimi K3 which is 50-70% more expensive to run than GPT 5.6 per Artificial Analysis.

Rationale:

A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers. 

Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software.

This is why Jensen is so supportive of open-source. An open-source model requires the *exact* same amount of compute to run as a closed frontier model of similar size and architecture. Kimi K3 is roughly the same price as GPT 5.6 Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time.

Lower margin % at the model layer = more margin $ at every part of the infrastructure layer and is a godsend for software. This can happen either through open-source models like K3 at the frontier *or* having a vertically integrated model company like Meta, SpaceX or Google at the frontier. Both outcomes result in a lower margin % at the model layer as vertically integrated model companies don’t really care where the margin $ come from. This is why it was so painful for OpenAI and Anthropic when Google was right there with them from a model competitiveness perspective and why Grok 4.5 and Muse 1.1 were just as important as Kimi K3.

The reason Kimi K3 is only *potentially* negative for Anthropic and OpenAI is 1) the @ericvishria point that the Claude and ChatGPT products and harnesses may be more important than their models today and 2) the hypothesis that they have much more advanced model checkpoints internally that are already being used for RSI. In the latter scenario, reaching RSI even a few months ahead of other labs might be enough to cement a permanent lead.

Time will tell on both points. And likely fairly quickly.

Caveat would be that since Kimi K3 is not token efficient and thereby actually more expensive than ChatGPT 5.6, we may need to see a more token efficient open-source model at the frontier or see Grok 5/Composer 4/Muse 2 at multiple points on the Pareto frontier for this potential risk to Anthropic and OpenAI to play out. And I am sure they will both vertically integrate as quickly as possible while continuing the product/harness strength they have shown over the last 8 months.

[Chris Vanderhook @cvanderhook]

Great analysis. Most importantly, "Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software." Imagine the stakes of only 2-3 players owning everything in the 5 layer cake of AI...bad for everyone.

[Chris Vanderhook @cvanderhook]

@AdtechGod @MiamiMasterson TVision has incredible TV data for all advertisers including political ads. See which creatives are running across linear and streaming and which networks, apps, shows, dayparts, audience demographics, etc.

[Chris Vanderhook @cvanderhook]

@keithepetri You are supposed to be in the office at 8am!

[Chris Vanderhook @cvanderhook]

@davidsenra What a flex!

[Chris Vanderhook @cvanderhook]

Why are all of the tech bros wearing mandarin shirts? I missed the memo.

[David Senra @davidsenra]

This is a combination of Steve Jobs’ (1) The storyteller is the most powerful person in the world and Jeff Bezos’ (2) Invest in things that won’t change:

“A company is just a fiction

You make some filing and you say "We're a corporation today even though we're just two people sitting around a couch and we have a few dollars in a bank account.”

And if these fictions are intended to grow to tens of thousands of people it should be because there is some common purpose.

There is something that in aggregate, the sum of the parts of all the people working together should be able to move something further than anyone alone could do.

And the role of a leader is to go and assert how will we measure ourselves?

If we are seeking to be our best and get better every day how should we think about that, and how can we understand our forward progress?

I don't know what Ramp will look like exactly in 100 years but I think just as it was true 100 years ago people always wanted more out of every dollar and more out of every hour, I think people will absolutely still want that in 100 years —in whatever form that may take.”

[Chris Vanderhook @cvanderhook]

We are nearing the end of the AI apocalypse narrative. Value is at the AI application layer with unique data and company or industry specific workflows running across open-source and frontier models in combo while protecting company alpha from being stolen by weirdos like Dario.

[Aaron Levie @levie]

A few thoughts on what we will see in AI structurally for the foreseeable future:

* Frontier intelligence continues unabated and pushes the industry forward continuously. The top labs will continue to buy the best and the most data, build the most compute, be at the forefront of improved training breakthroughs, and so on. A few different approaches stratify the market on pricing and capability, but overall competitive pressure brings down pricing on a per task basis. That said, we just ask more from the models over time - as one thing gets cheaper, we just use more - so frontier spend and use remains robust.

* Open weights rapidly absorbs frontier breakthroughs (and drives other breakthrough directions given the constraints), offering both lower cost intelligence and the ability to be post trained for specific workflows and domains. This creates a healthy counter balance to the frontier as you can run models “at cost” on a hyperscaler at any time, and tune models just for your tasks.

* The Applied AI layer has a huge opportunity to combine frontier intelligence with open or cheap closed models to orchestrate workflows in any given domain. Due to evals, deep domain context, being trusted with enterprise data and workflows, this layer can maximize performance and cost combination. The applied AI layer will also often have their own RLed models especially for high volume, predictable tasks in their systems.

* Individual enterprises will generally focus on their enterprise context, making sure they can get any AI system the right data and information to work with, in a continuously improving way. Some will go off and train their own models for specific areas of work (large banks, pharma, etc.) where they can get real alpha from doing so given the many tradeoffs, but most will spend energy on making sure they can get all of the gains from AI breakthroughs on their data and workflows.

Net net: even though some of this gets framed as zero sum, there’s just a ton of opportunity for all layers of the stack and approaches.

[Chris Vanderhook @cvanderhook]

Zuckdog calling out the commoditization of frontier models. The value is moving up the chain to the application layer. Separately, he’s really committed to Meta glasses. He clearly has worn them in the sun for too long.

[Karl Mehta @karlmehta]

Mark Zuckerberg explains the 405B teacher-model flywheel that could make one giant AI the wrong end state

"People are gonna wanna do inference directly on the 405 because it's, you know, by our estimates, it's gonna be about 50% cheaper, I think, than GPT-4o to do that directly."

"Because it's open weights, the ability to take the model and distill it down to whatever size that you want, to use it for synthetic data generation, to use it as a teacher model."

"Our vision is that there should be lots of different models. I think every startup out there, every enterprise, governments, they all kind of wanna have their own custom models."

"Right now, as open source basically closes the gap, I think you're just gonna see this wide proliferation of models where people now have the incentive to basically customize and build and train exactly the right size model for what they're doing, train their data into it."

"They're gonna have the tools to do it because of a lot of the partner integrations that the companies like Amazon are doing with AWS or Databricks or different folks like that who are building these whole suites of services for distilling and fine-tuning open models."

The counterintuitive edge is that the 405B model may be most valuable as raw material, not an endpoint.

The open model compresses into the right size, absorbs proprietary data, and turns one frontier release into thousands of company-specific systems. Distribution of intelligence beats centralization.

- Mark Zuckerberg (@finkd), CEO of Meta, with @rowancheung

[Chris Vanderhook @cvanderhook]

@tbpn @morganhousel There is an attention war going on and it is incumbent on creators and advertisers to create better content (or ads) to earn the attention of the consumer.

[Chris Vanderhook @cvanderhook]

Comcast is spinning out NBCU while Roku is selling to Fox. It's not that Content + Pipes is wrong but not all pipes are created equal. Content + Distribution is the correct model but traditional cable is in structural decline and content assets have never been priced higher (see WBD @ $110B enterprise value).
Roku pipes > cable pipes.

[Digital Content Next @DCNorg]

Comcast is breaking up with NBCU. Why did it ever buy it in the first place?
Business Insider’s Peter Kafka on the Comcast split, and why the dream of content plus pipes keeps failing.
https://t.co/T6bzYZ3k9R

[Chris Vanderhook @cvanderhook]

@jtoonkel @benfritz Live TV represents the majority of TV ad supply, it's also what commands the highest CPM's. Netflix is exploring how to step up their ad game without forcing all of their subscribers to watch tons of ads in VOD like Amazon did. They are going to buy into Live TV.

[Chris Vanderhook @cvanderhook]

We also need to be honest with application layer (claude code, codex, cursor). it requires an engineering skillset to create agents. when agentic workflows can be created from start to finish by laymen then we will finally open up the use cases and we will then see token costs drop

[Chris Vanderhook @cvanderhook]

Glad companies are waking up to from being fed into a shredder. Google and Amazon already accomplished this in the cloud business (they can sniff every data packet in their cloud) and a handful of companies are now trying to own all of your data via AI. It's becoming obvious that the "productivity" tradeoff for your company intelligence is not penciling out.

[Chris Vanderhook @cvanderhook]

@101Programmatic @EricTilbury_RTB Iris is the only technology making show level data available and it's only available at Viant.