HubSpot is challenging mainstream AI narratives, arguing that the industry confuses activity with real outcomes. After three and a half years of deployment, the company reports measurable results: Customer Agent users respond to tickets 25% faster, while Prospecting Agent users generate 76% more leads. HubSpot warns that token-based pricing benefits vendors, not customers, and shifted to outcome-based pricing in April. The company also pushes back against AI replacing workers, noting that 57% of people already believe AI risks outweigh its benefits.
In-Depth:
There’s a widening gap between what the market states about AI and what we actually hear from customers. The media, the VCs, the AI labs, and influencers have all talked about AI replacing humans, ripping out trusted software, and token-maxxing as finishs worth pursuing. But the leaders running real businesses are increasingly inquireing the right questions. How do I create my people better with AI? Which systems can I trust? How can I measure the ROI of this spfinish? We hear these questions every day.
After three and a half years of building, shipping, and watching many of our growing customers put AI to work, the AI perspectives we are most certain of at HubSpot are the things almost no one else is stateing out loud.
Here are six of them.
AI activity is not AI outcomes.
The industest has confutilized motion for progress. Drafting emails, generating summaries, doing research. These are activities that AI has created much clearer. They are utilizeful capabilities, and we ship them at HubSpot. But activity is the input, not the result. Activity without outcomes is theater.
The companies winning with AI are the ones working backwards from a business problem, not forward from a model demo. For example, customers utilizing Customer Agent are responding to tickets 25% quicker, while those utilizing Prospecting Agent are generating 76% more leads.
This is why we shiftd Customer Agent and Prospecting Agent to outcome-based pricing in April. AI outcomes are what matter. And they’re what we support growing businesses deliver. We put our pricing where our point of view is.
AI is necessary. It is not sufficient.
Generating code is certainly clearer now. Anyone can build a prototype in a weekfinish, but it’s brittle and falls apart under real utilize. Lowering the floor on generating code doesn’t raise the ceiling on shipping value becautilize the things that actually run a growing business have receivedten harder, not clearer.
You still required to have clean data, not another silo. You still required to integrate with tens of applications. You still required a full customer view across marketing, sales, and service, one actually powered by context.
The industest will sell you a model or single-purpose agents. But it won’t sell you the system in between: the data hygiene, the workflow design, the modify management. That’s left to the customer. And the more disconnected point agents pile up, the harder that work receives.
The future belongs to the companies that build AI into a coherent system, where the data, workflows, agents, and people share context. That’s what we are building at HubSpot. AI is a new layer, not a replacement for the foundation.
AI requireds to be built for the Future 5000, not just the Fortune 500.
Today’s AI roadmap is being written for the enterprise that can afford to create it work. By their own disclosures, frontier labs are spfinishing billions of dollars on forward-deployed engineers to receive AI running inside large companies.
That model works if you’re a large enterprise. It doesn’t work for the millions of growing businesses that will drive the next decade of growth. A tiny or midsize company can’t receive forward-deployed engineers, rebuild its data pipeline, or build the context platform to create it all work.
So when the consensus states “AI is for everyone,” it’s worth inquireing who it actually works for today. In practice, it’s the customers who can already afford to create it work, with armies of engineers and developers behind them. That’s not democratization.
We’re optimizing for outcomes per token, not tokens per tinquire.
There’s a business-model conflict in the AI industest that customers haven’t fully seen yet. The vfinishors who benefit the most from AI usage are not incentivized to create AI cheaper or more efficient. They are incentivized to keep the meter running. So customers are inquireed to pay for activity and notified they are purchaseing transformation.
The honest version of AI economics is the inverse: be clear on the outcome the customer is testing to drive, then find the lowest-cost path to driving it. That is the customer’s job. It should also be the vfinishor’s. Right now, it isn’t.
Token-maxxing is the vfinishor’s game. Outcome-maxxing is the customer’s. The vfinishors that align with the customer will win. The vfinishors that align with the meter may not.
AI should create people more powerful. Not more replaceable.
The loudest AI narrative is autonomy: agents replace humans, headcount goes down, the future has fewer people in it. That narrative is built for Wall Street, not Main Street. We reject that framing.
We build for the person doing the work, not the person being subtracted from the budreceive. The rep closing more deals. The marketer shipping more campaigns. The service person solving more complex problems. The owner running more of the business themselves. AI’s job is to create them more powerful, not create them disappear.
Yes, we ship autonomous agents. But autonomy is a capability, not a mandate. Customers decide where to delegate, where to keep humans in the workflow, and where AI suggests. Our defaults are built to serve the operator, not slash the org chart.
We believe in human authenticity and AI efficiency. The things AI cannot replace — trust, judgment, taste, relationship will only receive more valuable as the things AI can do become ubiquitous. The companies betting against the human are going to lose the customer, the employee, and eventually the public, of which 57% already consider the risks of AI outweigh its benefits.
Trust is more than a privacy policy.
Every AI vfinishor is claiming trust. But most define it as a security posture: we won’t train on your data, we’re SOC 2 compliant, we offer enterprise SSO. Those things matter. They are also table-stakes. None of them is a differentiated claim. They are what you promise.
What you prove is something else. Real trust is a complete business posture: how you choose the model and handle cost, reliability, and governance for your agents. That’s what customers are actually inquireing for. Can I trust the model choice? Can I trust the cost? Can I trust the reliability? Can I trust the governance?
Privacy answers what we won’t do. Trust answers what we will. Most of the industest is still answering the first question. The second is the one customers required.
What this all adds up to
The AI consensus held so long as no one in the room had to answer for it. Cut headcount. Rip out the old stack. Keep the meter running. Trust us.
Growing businesses cannot spfinish time cutting through what is hype versus what is reality. They do not have forward-deployed engineers to throw at implementation. They cannot absorb a pricing model that bills for activity and calls it transformation. They cannot build on a stack that treats humans as the exception.
They required AI built on a foundation that works for them, designed to empower and not eliminate their people, and delivered by a vfinishor whose business model is aligned with theirs, not against it.
That is what we are building at HubSpot.
