TL;DR: Salesforce Koa, Harvey Tenet, and Shopify’s specialist AI models show how software companies are teaching models to perform domain-specific work. In my System of Execution framework, the strategic question is who controls the workflows, outcome evidence, and permissions needed to turn that experience into learning - and whether the resulting advantage can endure.
As AI makes code cheaper to produce, much of software is focused on building more of it. This has been obvious for quite some time now, as coding has become the hot use case.
However, this dynamic is taking another leap forward, as seen in three recent product announcements involving specialist models: Salesforce’s Koa, Harvey’s Tenet, and recent Shopify ML team experiments. These point to a second shift underway, where some expertise that used to be written as procedure can now be learned into model weights.
This evolution matters economically. A software company can take a capable open-weight model, train it for a defined job, and potentially lower the cost of delivering intelligence inside its product. Weights become another way to package and deploy expertise.
In that sense, weights are the new code.
Last month in The System of Execution, I argued that an emerging source of enterprise value lies in controlling the loop from intent and authority through execution, verification, and outcome. I called the evidence of this system the “receipt”: a record of what was authorized, what happened, and what resulted.
And yet, in defining this concept, I left the next step largely unexplored: What can the system learn from that record? After attending Dreamforce last week, I was inspired to come back to that unasked and unanswered question. I discussed Koa and what Dreamforce signaled with Gemma Allen of theCUBE Research in AGNT Podcast Ep. 15.
My argument here is that as specialist models become easier to build, lasting advantage may depend less on the weights themselves than on the process that improves them: the environment in which they practice, the judgment of what counts as success, and access to real outcomes with permission to learn from them. Frontier models remain essential.
The question is how much defined, repeated work software companies can take into their own hands, and whether doing so creates a lasting advantage.
Salesforce Koa and the specialist AI shift
At Salesforce’s Dreamforce 2026 conference at the Moscone Center in San Francisco, CEO Marc Benioff delivered his two-hour keynote while strolling around the audience in the auditorium rather than standing above them on stage. After delivering a series of strategic and product updates, he called upon special guests emblematic of the company’s agentic reinvention.
The first was Anthropic CEO Dario Amodei. A short time later, Nvidia CEO Jensen Huang jumped out of a chair and joined Benioff to walk through the crowd. “We’re all following you, bro” Benioff joked.
The exchange between the two CEOs captured the larger story that unfolded throughout the week: the head of a software empire following the man supplying open models to help him enter a new era.
Earlier, Benioff had dismissed talk of a SaaSpocalypse as “crazy nonsense” and described an interface shift in which Salesforce increasingly appears through Claude and Slack while its records and business logic sit underneath. Huang called AI “a layer on top of software”.
I agree with both. However, the more important announcement for me was Salesforce Koa. On September 15, Salesforce introduced its first CRM reasoning model.
For me, Koa raised a question that extends beyond the interface: how much of the expertise inside an application can a software company teach to a model it controls?
Let’s look at that announcement, as well as two other related ones that I think illuminate this issue:
Salesforce Koa. This is Salesforce’s first CRM reasoning model, built by further training NVIDIA’s open-weight Nemotron 3 Super. Salesforce controls the resulting weights and runs the model within its own infrastructure.
What matters is how Koa was taught. Salesforce’s AI leaders said imitating transcripts of successful work produced limited gains on the complex interactions they wanted to improve, so they “made it do the work”. The team built simulated workflows across more than fourteen industries. Synthetic customers arrived with different requests; tools answered the agent’s calls; a judge scored whether the task was resolved; repeated attempts supplied the feedback used to improve the model.
Salesforce says it used no customer data. Its accumulated knowledge entered through the environment: which situations the model encountered, which actions were available, and what counted as success. A company founded in 1999 with “No Software” as its slogan was turning some of its business-process knowledge into learned behavior.
Harvey Tenet. The legal AI company previewed Tenet on August 20, using Moonshot’s open-weight Kimi K3 as its base. Harvey trained the model on extended legal tasks, evaluated its work against expert rubrics, and used those scores to improve subsequent performance. The company also says it used no customer data. By Harvey’s own measurement, Tenet completes nearly twice as many held-out tasks as the underlying model. It is a research preview, not a product. The result supports the potential of specialization. But it has not yet established how the model will perform across customers’ everyday legal work.
Shopify. The e-commerce company offers a view of the next step: learning from deployment. Its engineering team describes a daily process that turns production failures into training material. The team evaluates, corrects, and replays difficult conversations. Successful repairs become examples for subsequent training. The improvement depends on the machinery around the model.
A narrower experiment shows how far specialization can go. On September 1, CEO Tobi Lutke shared a slide from a buyer-profile model with just 0.8 billion parameters. Over one week, as the training set expanded, the student moved from below Shopify’s production model to above the frontier model that taught it. Lutke called it a “very specialized task”.
These examples establish different parts of the argument. Salesforce and Harvey show how domain expertise can shape environments and evaluations. Shopify shows how production experience can feed subsequent updates. Whether that becomes a durable advantage depends on who can keep improving the system.