Nvidia CEO Jensen Huang argues it’s the “most illogical thing in the world” to believe AI will kill SaaS, and says the markets “got it wrong” in sending SaaS stocks tumbling. Why “illogical”? Because, as analyst Benedict Evans stresses, the fact that everyone can now easily spin up code actually doesn’t solve any SaaS problems, because creating code and tools is the easy part: “The hard part is knowing that you need a tool for this in the first place and then knowing what the tool should do.”

If that’s true, then what Shopify is doing with Sidekick is potentially revolutionary by embracing and extending the traditional SaaS model.

In December, Shopify introduced custom app generation through Sidekick. A merchant (Shopify customer) describes a tool, and Sidekick writes the code, using Shopify’s interface components and connecting to its Admin API. It’s a cool option and has been very popular. Shopify says merchants created almost 4,000 custom apps in the first three weeks following the release.

This isn’t surprising: Shopify is delivering the customization customers have long wanted from enterprise SaaS while ensuring they still enjoy the comfort of ongoing support and platform stability.

I suspect we’ll see more enterprise SaaS like this, despite silly predictions that AI would kill SaaS. Cheaper code gives customers a way to fix the things they dislike about an application without replacing everything they do like. For a vendor willing to accommodate that, AI could make its product considerably more useful and, hence, much “stickier.”

The feature that never ships

Cloudflare’s Jeremy Morrell discusses this in an excellent essay on extensible software. “In the past year, your users have suddenly acquired the ability to speak code into existence,” he writes, allowing for “markets of one.” His argument is that software should let customers put that ability to work, building additions around an established core.

Morrell has a commercial interest here, which he discloses: He works at Cloudflare and thinks its infrastructure is well suited to running those additions. He’s not wrong. But it’s the general opportunity to match customizability with stability that matters so much.

There are really (really) good reasons why vendors haven’t allowed customizability of their core products. Enterprise software companies limit deep customization to prevent high maintenance costs, security risks, and broken system updates. Oh, and to prevent their products from becoming convoluted bloatware. No matter how much you may want feature X, that feature would likely be irrelevant to almost everyone else, so adding it would complicate the product for the majority. No one wants that.

Customers have long paid consultants to customize software or they’ve hired developers to do it themselves (all while taking care not to violate their vendor licenses). Morrell points to Salesforce, which has spent years letting businesses build their own logic into its platform. In other words, AI didn’t invent extensibility. No, AI just makes it incredibly cheap. There’s a meaningful opportunity here for customers who can describe the change they need but have never been able to get it built. Some will save money on software or customizations they previously bought, while others will finally get software that nobody could profitably sell them.

Following the money

This change, however welcome for enterprises, won’t be comfortable for developers selling a narrow reporting tool or a small workflow improvement. Their customers may decide that an LLM-generated extension is good enough. An AI-built customization needn’t match every feature of the commercial product. It only has to do the job that the customer was paying for.

The platform underneath that extension has a different calculation. Every useful tool built against its data and APIs gives the customer another reason to stay. AI can make an individual feature less valuable to sell while making the product that hosts it more valuable to own. (Yet another reason to tsk-tsk foolish eulogies for SaaS companies.)

Ultimately, this should benefit anyone who both knows the business and can build more efficiently for that business. Benedict Evans makes this point in his newsletter, arguing that cheaper coding doesn’t solve the problem of discovering what software ought to do or persuading an organization to use it. A useful idea that spans several departments still needs those departments to agree. Even excellent code can’t sign off on a new way of running accounts payable.

Evans also describes how improvised tools eventually become important enough to need formal ownership and support. Anyone betting that companies will happily maintain everything they can generate should spend some time with the person who inherited the last departmental spreadsheet. Silicon Valley folks with little knowledge of real-world enterprises might think enterprises will vibe code all their software now, but they won’t. That’s not how software works because it’s not how people work.

This cuts both ways for established software vendors. Customers may continue to need their expertise while becoming less willing to accept a product’s limitations. A vendor that makes customization easy can benefit from that impatience, while one that blocks it has given the customer a new reason to investigate alternatives. The difficult decisions will come when customization interferes with what the vendor already sells.

I wrote in 2022 about Microsoft’s complicated relationship with open source, and how a company’s broad commitment to openness can collide with a division’s revenue targets. The same tension applies here. A CEO can be excited about letting customers build anything. The executive responsible for selling the premium reporting package may be less enthusiastic. Resolving that disagreement will determine what customers actually get to build, however capable the AI becomes.

Getting comfortable

Customers have their own calculations to make. An application shaped around the way your company works can be wonderful, but it can also be a pain to leave.

There are limits to what Shopify’s generated apps can do today. They are admin tools, not storefront or checkout extensions. Shopify also tells merchants to test them before installation because they can change customer-facing data. A generated app can be wrong, and running it inside a familiar product doesn’t relieve the merchant of checking its work.

Shopify lets merchants inspect and edit the generated code and restore an earlier version. That’s useful, but code written for Shopify’s Admin API still depends on Shopify. Having the source doesn’t give you another company ready to run it unchanged.

AI might eventually make moving those extensions easier, too. But recreating a screen is one thing; reproducing the behavior it relies on throughout a business is a considerably bigger undertaking. That includes the assumptions about records and transactions that the custom code gets to take for granted while it runs on the original platform.

None of this makes customization a bad deal. Customers can gain more control over their daily work while, somewhat ironically, becoming more dependent on a supplier. For SaaS vendors, that’s a good commercial reason to let customers finish the bits their developers will never get around to building, even when it costs them an add-on sale (and potentially some support headaches, depending on how they’re architected for extensibility). The customer is doing work that makes the underlying product more valuable.

AI, in short, has the potential to make SaaS dramatically more valuable, not extinct.

Healthy software companies typically don’t suddenly eliminate one-fifth of their workforce, but monday.com is doing just that as it bets on flatter teams, AI agents, and customer implementation expertise as the winning combination in the AI era.

Monday.com co-founder and co-CEO Eran Zinman today announced the “very difficult decision” to reduce the AI work platform company’s global workforce by about 20%, or 620 people.

The move has nothing to do with increasing margins or replacing humans with AI, he insisted in his post on LinkedIn; rather, it’s a calculated decision to trim down and hone the company’s focus as AI becomes integral to day-to-day workflows.

“This is not a distress signal; it is a deliberate reset, disclosed with its price attached,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “The industry has quietly swapped the meaning of productivity, and this filing is the clearest exhibit yet.”

A ‘significant opportunity’ in technology

In a SEC filing this week, monday.com said its restructuring plan reflects the “ongoing transformation of its product, marketing, and go-to-market strategy.” The move is intended to support a “leaner, more focused operating model” as the company continues to invest in its AI-driven strategy.

Zinman noted in his post that the company has shifted to “doing the work with AI and not just managing it,” and is focused on building environments where “people and AI agents [work] together in one workspace.”

In recent months, monday.com has evolved its products, strategy, and the way it serves its customers, and Zinman contended that “the organization we built for our previous chapter is not the organization that fits the new AI era.” Monday.com needs to “execute more decisively,” take on new challenges, and quickly respond to market changes, he said.

“We have never seen such a significant opportunity in software, driven by such exciting technology,” Zinman noted. He emphasized that the reduction is not to replace people with AI, nor to improve margins; the “vast majority” of savings will be reinvested into talent, products, and AI.

The restructuring will result in a “flatter organization” with fewer management layers and smaller, more autonomous teams, and monday.com also has a new go-to-market model, Zinman explained. Customers expect “deeper implementation support” as they deploy AI, and the company will work more closely with customers, increase its on-site presence, create new roles, and “adapt many existing ones.” In its SEC filing, the company said it expects to continue hiring in “key strategic areas” throughout 2026.

Workers will be expected to work better, “not harder,” Zinman noted. He pointed to several past examples where work could have been done in a few days, but instead took many months with “multiple meetings and endless friction.”

“This wasn’t people’s fault and everyone was frustrated by this,” he said. “Our new org changes ownership to allow people to make decisions and move fast.”

A spokesperson for monday.com declined to comment further on the staff reductions.

Monday.com’s key market advantages

Monday.com certainly isn’t struggling; the company expects 19% to 20% year-over-year growth in 2026.

“Companies in that position do not restructure because they must,” Greyhound’s Gogia noted. “They restructure because they have decided to become something else.”

Melody Brue, VP and principal analyst at Moor Insights & Strategy, pointed out that organizational redesign is important for real AI transformation, but while it can signal confidence to the market, it can still be “devastating” to humans.

While the company looks as though it’s trying to do right, that ultimately remains to be seen, she said. “There are often hidden internal bruises that can surface long after layoffs.”

Monday.com’s advantage is in its “structured substrate,” Gogia noted; its boards, permissions and typed workflows give agents something firmer to act on than just documents and chat history. The company highlights its natively built agents that can be configured by any team member, as well as connectors with Claude, Microsoft Copilot, and ChatGPT, and dedicated routes for external agents to authenticate and operate.

“For some time, the sharper enterprise question has been shifting from who has an agent to who owns the governed runtime in which an agent can safely act,” he said. “Structured work is a serious claim on that runtime.”

But parts of monday.com’s agent estate remain in staged release, and its product is ultimately “mid-transition,” Gogia pointed out; its agent builder carried a beta label as recently as March,. Also, the company’s pricing model changed in May to a hybrid model charging for seats as well as mandatory AI credits. And, while its AI-powered no-code builder monday vibe passed $1 million in annual recurring revenue within two and a half months, monday.com has not released subsequent outcomes, usage volumes, or attach rates.

Further, there’s an element of “gravity” with its competitors, he observed. Asana is reorganizing teams around agents, Atlassian is wiring agents into the developer estate, and others are simply bundling them into their offerings: Microsoft is doing so across the productivity stack, and ServiceNow across enterprise operations, each with identity and procurement built in.

“Their pull is strongest exactly where monday.com wants to grow, in the largest accounts, where control-plane depth and administrative reach decide the deal,” said Gogia.

Actions for the near-term

Going forward, buyers should focus on operating risk, not headline risk, Moor’s Brue noted. In practice, that’s continuity of service, roadmap consistency, and strength of enterprise support. Productivity should be valued as better outcomes per unit of organizational effort, not mere activity.

“It should be a measure of how much smoother, faster, and more effective the operating model becomes when AI is built into the work,” said Brue.

Gogia noted that strain surfaces first in customer service, and monday.com’s attention is being redistributed. The company’s annual report disclosed that its focus is now concentrated on the largest accounts, with support for medium-sized clients moved to an AI-first and human-supported model.

During the first month of the transition, buyers should track named account continuity and escalation times, he advised. By the first quarter, keep an eye on whether credit governance and admin controls mature on schedule, and if the roadmap beyond the AI estate keeps pace. By the half-year mark, determine whether promised implementation depth is producing outcomes or “simply more billable engagement.”

Support tiers should be enumerated in writing before renewal, and buyers should contract for “side exits,” Gogia emphasized, with overage pricing fixed in advance, the right to pause consumption, and portability for workflows and agent configuration “if the relationship sours.” Finance should also insist on monthly consumption reporting by capability. Further, integration efforts, partner dependency, and change management should be considered first-class costs of the agent era, “not as afterthoughts to a license.”

“A license was a known cost,” said Gogia. “A meter is a behavior, and behavior is harder to forecast than headcount.”

This article originally appeared on CIO.com.