Why Google Will Win GenAI in Advertising

Even when Google seemed behind on Generative AI (GenAI), it was still the safest long‑term bet for advertisers. Today the gap on model quality has largely closed, and Google’s advantages (procurement comfort, unique ads data, capex scale, and deep research talent) make it the most likely default provider for marketing AI. Not just inside Google Ads, but as the model provider that AdTech builders and agencies run on.

What “winning” looks like (in advertising)

When I say “win” here I don't mean in terms of chatbot popularity (ChatGPT is going to stay on top there). It’s:

Being the model + platform other builders choose. Agencies and AdTech/MarTech vendors will build custom tools on top of Vertex AI + Google Cloud, rather than sharing sensitive media data with other vendors.
Embedding AI inside the ad stack. Creative generation, audience modelling, measurement, bidding and budgeting. Google already (as of time of writing, pending court cases) owns a huge amount of the digital ad stack - where better to integrate leading GenAI models.

Five reasons Google becomes the default

1) Procurement & data governance are decisive

Enterprise buyers (and agencies acting on clients’ behalf) optimise for risk reduction as much as raw capability. Google already sits inside most stacks (eg with BigQuery, GCP, Google Marketing Platform), with clear training restrictions, retention controls and data clean‑room patterns. When the choice is send ad logs and customer data to yet another vendor vs keep it in an existing, audited platform, procurement picks the latter.

What this means for you: if you’re building internal AI tools, put them where the data already lives; design for zero‑data‑retention paths and privacy‑safe joins (clean rooms) from day one.

2) Model quality has caught up where it matters

Over the last 12–18 months, Google’s models have moved fast on the dimensions marketers care about:

Long‑context multimodality (great for assets, product feeds, historic campaigns).
High‑quality image/video generation that’s fit for creative iteration and performance testing.
Latency + scale suitable for production workloads.

There are tasks where other models do better - there’s no denying that. But for most marketing workflows, Gemini (and Veo) models are now “good enough” to use for nearly every task. And you can still hook into other model providers (without sharing sensitive data) for smaller edge cases.

3) Unique ads data + activation loops

No one else has Google’s breadth of event‑level ad interactions, auction dynamics and creative performance in its own ecosystem. That produces better targeting, creative optimisation and measurement inside the walled garden itself than you’ll get via generic LLMs alone. Context is key when working with LLMs, and Google has the most advertising context to tap into.

4) Cost structure and incentives align

Training and inference at scale are very expensive. Unlike model labs like OpenAI and Anthropic, Google can fund AI with ads cashflow and gets a direct defensive benefit: if AI disrupts search and YouTube, Google wants to be the one doing that disrupting. When it comes time for the model provider startups to finally try and turn a profit, they’ll have to increase model prices (either in apps like ChatGPT or through the API). Google doesn’t have quite the same pressure to do so, if it can maintain its ads revenue and augment that with GenAI capabilities.

5) Deep research bench and talent

Remember: the modern transformer era actually started at Google. They were slow to productise early on, and OpenAI stole a march on them, but the research pipeline and infrastructure (TPUs, orchestration, safety tooling) give Google a persistent edge in shipping enterprise‑grade AI.

Doesn't the same apply to Meta?

Yes and No. Meta has the same benefits of being entrenched in the online advertising world, but doesn’t have the same Cloud infrastructure that Google does and took a very different approach early on - leaning heavily into Open Source with the Llama family of models.

They have been quickly integrated into the Meta suite of apps (Facebook, Messenger & WhatsApp), but don’t have the same adoption from developers outside their own ecosystem.

Open Source models (depending on the license) mean anyone can run them assuming they have the technical capability and physical or virtual infrastructure to do so. To run the largest Llama models (which are comparable to but not as strong as the top Google, OpenAI and Anthropic models) you need some serious computing power and GPUs (where Nvidia is making all their money).

Even for super sensitive workflows and data, where it never leaves a companies own servers this is not the advantage it once was, with Google and OpenAI working to make their models available in secure sandboxes for industries like finance.

Meta also more resembles one of the startup model providers in terms of the “drama” around its AI org - with rumours of crazy pay packets and leadership structures changing frequently, something you don’t hear coming out of Google.

Summary

This is all just my opinion, based on what I’ve seen, heard and spoken to various people about. I will claim that I have been saying this since last August (2024) when Google was obviously behind technically, and I'm glad that to have been vindicated in terms of them catching up.

I still think they’ll “win” overall - but that doesn’t necessarily mean Gemini will be the only model advertisers use. It offers the best option, I think, in terms of capability, governance and long‑term cost, but there may be cases where OpenAI’s gpt or Anthropics Claude models (or some yet to be seen new type of model) perform better. For tool builders and agencies working with multiple clients, flexibility is key while capabilities are still shifting incredibly fast early on in the GenAI race. But for AdTech specifically, if I had to put money in one place, it would be with Google & Gemini