Why behind AI: The OpenAI sales memo
Enterprise GTM is no longer an afterthought
It’s been a painful six months for OpenAI. After riding the high of massive Enterprise adoption through Azure and becoming the most important consumer app on the market, there was a lot of hubris on the leadership team.
This resulted in a disjointed and often delusional strategy, with pivots further into the consumer space, subsidizing global footprint expansion and getting into the building/scaling of AI infrastructure.
The peak of this strategy was the period of “stock growth by announcement”, when any company would get a significant stock bump if they announced a new contract with OpenAI. The peak of the “press release economy” was Oracle hitting 1T on the back of a massive hypothetical buildout, mostly backed by smiles and letters of intent.
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In the meantime, Anthropic focused on building the best coding agent on the market and the rest, as they say, is history. We sit in a very peculiar moment, as the valuations for both companies are similar, and more importantly, so is their self-reported revenue. OpenAI’s leadership seems to have taken the feedback and is scaling down aggressively many of the distractions that have wasted time and energy in 2025.
This is their strategy for going head-to-head against Anthropic as articulated by their CRO, Denise Dresser.
The System That Will Win Enterprise AI
As we start Q2, I want to begin where we always should: with our customers. I have been spending time with leaders across our largest enterprises, most influential startups, and key venture firms. The message is clear. People are excited about what we are building, and they want a deeper view into our roadmap so they can plan with confidence and stay ahead of the market.
Enterprise AI is entering a more mature phase. Raw capability still matters, but it is no longer enough. Customers want fit: how well AI plugs into their workflows, knowledge, controls, and day-to-day operations, and how effectively it can be deployed, trusted, and improved over time. They want a system they can trust and build on.
We are building that system: the best models for work, a platform for agents, deep integration with business context, and the ability to deploy and improve at scale. And customers are validating that direction in the clearest possible way. Multi-year, multi-product, nine-figure deals are rising, and existing customers are expanding as they standardize on our capabilities across more of their organizations.
I am incredibly proud of how this team is showing up. We are earning trust through the depth, quality, and care we bring to the work. The opportunity ahead is massive, and our biggest constraint right now is not demand. It is capacity. That is why talent remains a top priority in Q2. We will keep hiring deliberately, keep the bar high, and keep building a team that matches the excellence our customers expect from us and we expect from each other.
We have everything we need to extend our lead from here. We have the compute. We have the products. We have the customer pull. This is the moment to lean in and make the case, clearly and confidently, that OpenAI is the platform enterprises should trust to build, deploy, and scale with.
I think that it’s important to call out that in my opinion OpenAI is offering the better models across most cases and the only real gap is a Cowork alternative. I pay for the Pro/Max plans for both OpenAI and Anthropic, as well as get similar access at my big corpo gig. OpenAI offers a better coding model (GPT 5.4 on xhigh), great app for it (Codex) and more advanced “big boi” model in the form of GPT 5.4 Pro.
The advantage of Anthropic is that Claude models are more pleasant to talk to for consumer use cases and work well with user memory, while Cowork is right now probably the best way to do account work in terms of a continuous conversation that references multiple documents, while also generating new input and connecting with the majority of Enterprise applications.
I don’t see Cowork as being difficult to replicate as an experience and the upcoming “unified mega ultra” app for ChatGPT has to address most of these gaps.
Here are five customer-backed priorities I want us to focus on.
1. Win the model layer for work
Enterprises buy business outcomes. They pay for models that help employees write faster, analyze better, code more productively, support customers more effectively, and make higher-quality decisions. They pay for higher revenue per employee, faster cycle times, lower support costs, and better execution.
Spud is an important step in the intelligence foundation for the next generation of work. Early feedback from our customers is very positive. Spud is not only our smartest model yet, but it also delivers on everything that matters for high-value professional work: stronger reasoning, better understanding of intent and dependencies, better follow-through and more reliable output in production.
Better model performance lifts the rest of the stack. Spud will make all of our key products significantly better. It expands the workflows we can own and gives customers another reason to consolidate around us. This is our iterative deployment strategy in practice: push the frontier, deploy it into real products, learn from real usage, and compound those lessons into better systems on the path to the super app.
Our compute advantage sets us up to deliver continuous leaps in capability. Customers already feel it in real product terms: higher token limits, lower latency, and more reliable execution of complex workflows. Every step forward in compute lets us train stronger models, serve more demand, and lower the cost per unit of intelligence. That is durable business leverage.
This is the biggest shift for OpenAI in the field and the most important transition for the company. Historically, OpenAI was never built with the intention of capturing Enterprise demand, the origins of the company are fundamentally researcher- and “AGI-pilled”-oriented. Once ChatGPT launched, it mostly saw itself as the consumer company bringing AI to the masses, while playing a dangerous game between its original charter (AI for everybody, even if they can’t pay) and the realities of scaling the most important product of its time.
The Azure team did the majority of heavy lifting for driving early Enterprise adoption but that relationship has deteriorated over time, both due to Satya being too cautious after making an extremely successful asymmetric bet, as well as an overall poor execution at the application layer from Microsoft as the “distribution engine for OpenAI models into Enterprise”.
2. Win the agent platform layer
The market has moved from prompts to agents. That shift is a massive opportunity for us.
Customers want systems that can reason, use tools, operate across workflows, and perform reliably inside real business environments. That means orchestration, control, observability, security, integration, and governance.
Frontier allows us to own the platform layer. We need to position Frontier as the default platform for enterprise agents – the core intelligence layer enterprises use to build, deploy, manage, and scale systems.
This is where our advantage can compound. Frontier ties model intelligence directly to agent performance. As our models improve, the platform gets more valuable. As the platform gets embedded, switching costs rise. As customers run more workflows through the system, OpenAI becomes harder to replace and more central to how work gets done.
That is how we move from product vendor to operating infrastructure.
Let’s repeat the last part again. The goal is to move away from being a product vendor to operating infrastructure. The vertical integration play that Anthropic clearly demonstrated as THE PLAY, is now becoming the default playbook for OpenAI. Whether they are able to execute on this without the anti-developer/consumer decisions that Anthropic has been making is a different topic.
3. Expand the market through Amazon
Our Microsoft partnership has been foundational to our success. But it has also limited our ability to meet enterprises where they are – for many that’s Bedrock.
Since we announced the partnership at the end of February, inbound demand from our customers for this offering has been frankly staggering. We are firing on all cylinders to establish this as a scaled distribution channel.
The Amazon Stateful Runtime Environment matters because it expands access and upgrades the product surface at the same time. By enabling memory, context, and continuity across interactions, we move beyond stateless model access toward systems that can operate reliably over time and across complex business processes.
This will expand our market in three ways: 1. It lowers adoption friction for AWS-native customers. 2: It strengthens our position with regulated and security-sensitive buyers by running inside their AWS environment and existing governance model. 3. It further integrates our platform from model access to production runtime for long-running, multi-step agents.
It’s difficult to overstate how unusual the current pace of changes in the Enterprise ecosystem is. Relationships and strategies worth billions are being destroyed and remade on an almost monthly basis. This development is also partly driven by the significant challenges at Azure to actually operate at the technical level needed to handle this demand.
I recommend going through this series of articles from a technical Microsoft insider on how they’ve reached a dysfunctional state of operations that is now starting to impact growth.
4. Sell the full AI-native stack
Customers want a platform not point solutions. That’s what we have: ChatGPT for Work is the front door for knowledge work. Codex is the system for software and agentic development. The API is the engine for embedded intelligence inside customer products and workflows. Frontier is the agent platform. The Amazon runtime extends our reach into production-grade, stateful execution.
That breadth is a major strategic advantage because customers do not all start in the same place. Some start with employees. Some start with developers. Some start with internal systems. Some start with external products. Our job is to meet them wherever they enter and then expand them across the full stack.
This is the flywheel we should be building around: better models drive more usage, more usage drives deeper integration, deeper integration drives multi-product adoption, and multi-product adoption makes us harder to replace.
We should stop thinking like a company with separate product lines. We should think like a platform company with multiple entry points and one integrated enterprise offering.
The platform play is the play, AI edition.
5. Own deployment
The biggest bottleneck in enterprise AI is no longer whether the technology works. It is whether companies can deploy it successfully and at scale.
DeployCo gives us the chance to turn product demand into repeatable enterprise transformation. It will be a deployment engine that helps companies prove value faster, reduce risk, and scale adoption across the organization.
This can become a force multiplier across everything else we are building.It helps customers move faster. It sharpens our feedback loops. It surfaces repeatable deployment patterns. It improves product, sales, and customer success all at once. And, alongside our Frontier Alliance partners, it gives us a serious path to scale execution across the market.
The companies that win enterprise AI will not just have the best models. They will have the best ability to get those models deployed into real workflows, inside real organizations, with real measurable value. We should be the best in the world at that.
“Forward deployed” is the new mental model for getting outcomes in software. The longer orgs struggle to understand this, the more likely it is they’ll get eaten alive by competitors who stopped behaving as if putting our smartest and most capable people at the forefront of implementation/adoption is a business failure.
Good riddance to the “sell SaaS and dip” era.
A note on the competitive landscape
The market is as competitive as I have ever seen it. I believe that is ultimately a good thing. It means the opportunity is immense and important. However, there is no question it can be noisy, volatile and distracting at times. Competition inspires us and will make us all better and most importantly our customers will feel that benefit. To that point, as you have not heard me say many times, the number one focus should be spending time with our customers. When we spend time with our customers, listening to what their problems and ambitions are, focusing on how we can invest in them and help, everything else gets quiet and comes into focus.
With that all being said, here are a few things worth keeping in mind, especially on Anthropic.
Their story is built on fear, restriction, and the idea that a small group of elites should control AI. Our positive message will win over time: build powerful systems, put in the right safeguards, expand access, and help people do more.
Their strategic misstep to not acquire enough compute is showing up in the product. Customers feel it through throttling, weaker availability, and a less reliable experience. We saw the exponential compute curve earlier, acted on it faster, and now have a real structural advantage.
Their coding focus gave them an early wedge. But you do not want to be a single-product company in a platform war. As AI spreads beyond developers into every team, workflow, and industry, that narrowness can become a real liability.
Their stated run rate is inflated. They use accounting treatment that makes revenue look bigger than it is, including grossing up rev share with Amazon and Google. Our analysis shows that this overstates their run rate by roughly $8 billion (at the current $30 stated). We report Microsoft revshare net, which is more inline with standards we would be held to as a public company.
This is some good FUD (fear, uncertainty, doubt) fodder, with a mixture of truths and misrepresentations.
Anthropic positions fear of AI as its core messaging: I think that they definitely recruit predominantly effective-altruism-adjacent employees, who mostly believe that AI is too dangerous to be allowed to be used by the masses in an uncontrolled manner. The execution at the Enterprise layer, however, has been ruthless, focused and consistent, with most of the GTM team coming from Stripe and Salesforce, two companies that did not let ideological differences prevent the business from flowing.
They did not acquire enough compute: Based on the recent moves from Anthropic like banning OpenClaw usage and what appears to be downgrading model performance over API, this is not an unfair statement. Dario didn’t want to blow up the company by being overly bullish and it’s not surprising that, well, everybody was surprised and unprepared for how much demand there really is.
Claude is only good for coding: Obviously not true, as Claude has performed strongly both in consumer-facing agentic workflows (it powers the Slack bot that actually works), as well as general analysis and workflows (otherwise Cowork wouldn’t have exploded as it did). The better FUD here is “we are the better coding tool and more developer-friendly”, but I’ll forgive Denise for not being technical.
Their run-rate is fake: Oh, oh, oh. I think there are two ways to approach this. In one case we can argue the semantics of whatever final number Anthropic decides to IPO with and whether it will lead to getting sued by investors. The other is the practical reality, which is that even if they are overstating their revenue by $8B, they very clearly can still beat OpenAI this year.
Let’s Go Build
Finally, one of the best things about the work we do is the people we get to do it with. I am so proud of this company and our team. It is a privilege to work with all of you and to be alive at this moment in the epicenter of the future. Lets all stay focused, work as one team and operate at the highest level of excellence and row in the same direction.
The market is ours to win, let’s execute accordingly.
Alea iacta est, my fellow OpenAI and Anthropic sellers. This is going to be a ride of a lifetime, winning on every single parameter and still feeling like failing when things don’t go your way.