At Dreamforce, buyers said last year's model is enough
Ricardo Argüello, September 23, 2026
CEO & Founder
General summary
Dreamforce 2026 ran two conversations at once. On stage, Marc Benioff sat with Sam Altman, Dario Amodei and Jensen Huang debating whether model development is moving too fast. On the floor, 50,000 attendees said models from a year or two ago already cover the sales and service work in front of them.
- Tim Sanders of G2 said the majority of agentic outcomes are driven by last year's AI, not frontier capability
- Kevin Lee of Nice said his company runs models at least one generation behind the frontier
- Ram Reddy of Nagarro said his engineers wait roughly three months before integrating a newly released model
- Salesforce does not run Agentforce bots on Claude Fable 5.1 or GPT-6 Astra
- Jensen Huang, on stage with Benioff, urged the frontier labs to run as fast as they can
Picture swapping the engine in your delivery van every quarter because a stronger one came out. The shop bills you, the driver relearns the vehicle, and the packages were arriving fine on the old engine. The route was the constraint, not the engine. That is where a lot of marketing teams are with AI models right now.
AI-generated summary
Salesforce hosted the conference where three frontier lab CEOs argued about how fast AI should move.
Salesforce does not run Agentforce on the frontier. Its bots sit on earlier generations, not on Claude Fable 5.1 and not on GPT-6 Astra.
Start there, because everything else at Dreamforce this week follows from it.
The floor disagreed with the stage
Marc Benioff had Sam Altman, Dario Amodei and Jensen Huang on stage debating pace and safety. Huang’s position was the simplest of the three. Run as fast as you can.
Then CNBC went and talked to the 50,000 people who paid to attend, and got a different answer entirely.
Tim Sanders, chief innovation officer at G2: the majority of agentic outcomes are not driven by frontier capabilities. They are driven by last year’s AI.
Kevin Lee, technology chief at Nice: the models already deployed are highly performant and effective at doing the things their customers need. His company deliberately stays at least a generation back.
Jaya Rohit Vuyyuru, a VP at SummitX: the frontier models are way ahead already, and a lot of the customer base is still getting its feet wet.
And Alec Bronston, a senior Salesforce director at Spins, said the thing most people only say privately. It is already hard enough to keep up, and a slowdown would be a chance to even catch up.
For marketing, this is a budget story
The license is the cheap part of a model upgrade.
The expensive part is everything that follows. Prompts that worked last quarter get retested. Brand voice guidance gets recalibrated because the new model has different default rhythms. Internal documentation goes stale. The team gets retrained. Two or three weeks of senior attention go into a change nobody asked for.
Meanwhile, the thing that was actually broken is still broken, because it was rarely the model. An ad that does not convert is not waiting for a smarter model. It is waiting for a clearer offer. An email that reads like a template reads that way because nobody ever wrote down what the brand sounds like.
We made that case in marketing does not need its own AI model. Dreamforce is the same argument, told by the buyers instead of by us.
Steal the three-month rule
Nagarro’s policy is the most useful thing in the coverage, and it is boring, which is exactly why it works.
Three months. A model ships, the team keeps working, and somebody evaluates it when the calendar says to evaluate it.
The marketing version writes itself. One fixed window per quarter for reviewing tools and models. Outside that window, nobody swaps engines regardless of what the feed is doing. Inside it, you run the candidate against what you already have, using briefs you have already run, and you only switch on a clear win.
What that buys you is a quarter spent on the offer and the message instead of on relearning the tool. It buys brand voice enough time to stabilize. And it means you can compare Q3 to Q4 without half the variance coming from having changed models in November.
The one thing worth doing now
Waiting is not the whole answer, and I would be selling you short if I stopped there.
There is one thing worth looking at this week, and it has nothing to do with which model you run. Who reviews what goes out? If your team publishes AI-assisted work with no judgment step in the middle, the newest model on earth does not fix that, and last year’s model does not make it worse. That is where the brand risk actually lives, and we wrote it up in your AI marketing needs a verifier.
If you are putting the Q4 plan together right now, that is the order I would use. Verifier first. Quarterly window second. Frontier model when something genuinely requires it.
See how we run marketing with AIFrequently Asked Questions
Several technology leaders told CNBC they run models that are one or two years old. Tim Sanders of G2 said most agentic outcomes come from last year's AI. Nice runs models at least a generation behind the frontier, and Nagarro waits about three months before integrating anything newly released.
Usually not. The marketing work that automates well, such as lead classification, ad variant drafting and call summarization, runs fine on previous-generation models. The constraint is almost always the brief, the brand voice definition and the review step rather than raw model capability.
The visible cost is licensing. The hidden cost is retesting prompts that already worked, recalibrating brand voice, updating internal documentation and retraining the team. None of that shows up on an invoice, and it gets paid in weeks of people being busy instead of productive.
Agentforce is Salesforce's agent platform for sales and customer service tasks. According to CNBC's reporting from Dreamforce 2026, Salesforce does not build those bots on the newest frontier models such as Claude Fable 5.1 or GPT-6 Astra, relying on earlier generations instead.
Related Articles
AI price per token lies. Measure cost per job.
Gemini 3 Flash is listed 80% cheaper than GPT-5.4 and costs 38% more to run. The list price is marketing. The bill depends on how many tokens each model burns.
Salesforce Headless 360: The Seat Is No Longer the Unit
On April 16 Benioff said 'Our API is the UI'. Four words, 4.6M views, and a reprice on every Salesforce seat sitting on your P&L this quarter.
No US Ban on Chinese AI Models Yet, But the Mechanism Is Loaded
Washington has not banned Chinese AI models. But the executive order and Entity List addition are already drafted, ready without a Congressional vote.
If Anthropic Doubled Prices Tomorrow, We Wouldn't Switch
A CEO on the 20VC podcast killed a $600K Salesforce contract in 3 weeks and won't change Anthropic usage at 2x pricing. The moat moved to the workflow.
Enterprise AI Economics Changed in 2026
Frontier AI models dropped from $15 to $3 per million tokens. With million-token context windows, projects that didn't pencil out a year ago are now viable.
A $1,500 cap on AI treats the symptom, not the cause
Uber capped AI spend at $1,500 per person and one company burned $500M on Claude in a month. The cap treats the symptom, not agents turned loose.