In a recent Every article, Dan Shipper highlights people who replaced expensive SaaS tools with AI-built alternatives. The stories aren’t just about cost-cutting. They show how quickly software creation is becoming accessible to people who never considered themselves builders.

This is still early days. Vibe-coding — natural language prompting to generate working tools — is in the first phase of its maturity curve. It often takes a few iterations to get things right, as Shipper’s examples show, but the return on investment is already strong.

I’ve seen it firsthand, vibe-coded this blog with Claude Code, among other projects. What once felt like “real development” is now as approachable as writing instructions to a teammate.

The upside: empowering non-technical builders

For product managers, operations leaders, or team leads, AI-generated tooling already unlocks new independence. Internal automations that might have sat in an engineering backlog can now be prototyped over a weekend. The process isn’t always smooth, but the ability to iterate quickly makes experimentation worthwhile.

This democratization of software creation expands what’s possible inside organizations. Small teams can act like they have an engineering wing. Ideas move from concept to usable prototype in days instead of quarters.

The downside: disruption for SaaS vendors

The same shift creates risk for SaaS companies. Many tools exist primarily to orchestrate workflows, manage data, or wrap a user interface around simple operations. If AI can reproduce that functionality on demand, customers may question why they’re paying $30 per seat per month.

This doesn’t mean SaaS is disappearing, but it does raise the bar for differentiation. Vendors can’t rely on features alone. The moats that will matter are:

Still Defensible	Vulnerable to AI-Build
Deep integrations	CRUD apps (create, read, update, delete)
Network effects (marketplaces, data sharing)	Simple workflow orchestration
Brand trust + compliance	Single-feature utilities
UX polish + scale reliability	Internal-facing dashboards/tools
Data network advantages	Niche automations

The takeaway for product managers

For builders, the question isn’t only “should we build or buy?” It’s now “should we AI-build?” That third option changes how teams weigh cost, flexibility, and speed.

For SaaS leaders, it’s worth assuming some of your customers are already experimenting with AI-built substitutes. The challenge is to deliver leverage that vibe-coding cannot. Focus on platforms rather than the point solutions.

Conclusion

AI-driven vibe-coding is still early in its maturity curve, but the contours are already visible. It empowers non-technical builders today, while quietly pressuring SaaS vendors to rethink tomorrow. The future of SaaS may not be about providing software alone, but about providing the scale, polish, and leverage that AI-built tools may not replicate.

When we think about product adoption, the focus usually falls on the end user. Product managers map user needs with frameworks like jobs-to-be-done (JTBD), ensuring the product fits a real workflow. But in B2B, adoption doesn't always equal purchase. Deals often hinge on an informal buying committee — a shifting group of individuals who influence or approve decisions, even if they never use the product directly.

This isn’t a boardroom-style committee. It’s a loose network of roles that naturally forms around a purchase: the user who advocates, the technologist who evaluates feasibility, the security lead who vets risk, the finance partner who manages cost. Ignore them, and even the best user experience may never see scale.

Jobs to Be Done Beyond the User

JTBD applies here too. Each member of this informal committee has their own “job” in the decision:

• End User: Get the job done more efficiently or effectively.
• Technologist: Ensure the product integrates cleanly with existing systems and doesn’t add unnecessary complexity. This is especially critical with APIs, developer platforms, or middleware where integration risk is high.
• IT/Security: Protect the organization’s data, ensure compliance, and manage operational risk.
• Finance: Control budget, reduce cost unpredictability, and avoid unplanned long-term lock-in.
• Legal/Procurement: Minimize exposure and standardize contracts.

Each role brings a different definition of success. Together, they form the real decision-making environment.

Why It Matters: The Case of API Adoption

Consider APIs and developer-focused products. Developers may love a tool for its speed or flexibility, but purchase rarely happens on their enthusiasm alone. Technical architects and platform teams often step in as the evaluators. Their “job” is to protect long-term maintainability and ensure integrations won’t break under scale. Without satisfying that committee, a product risks remaining in pilot purgatory — tested but never rolled out.

Gartner research on B2B buying groups shows that enterprise decisions typically involve six to ten stakeholders, many with veto power. That’s the informal committee at work.

The Product Manager’s Takeaway

It’s not enough to solve the end user’s job. To win in B2B, product managers must anticipate the informal buying committee and their jobs as well:

• Map the committee early. In discovery, ask who else needs to sign off or influence the decision.
• Frame outcomes for each role. Translate product value into the language of risk, cost, and integration as much as user efficiency.
• Enable champions. Give end users the narratives and artifacts they need to persuade their own internal committee.

Products succeed when they work for users. Businesses succeed when they also work for the informal committees shaping the path to purchase.

When you build products, you’re making bets — not certainties. The best product teams don’t pretend to know the answer or wait until all data clears the fog. Instead, they “think in bets.” That means approaching each decision like a poker player, not a chess grandmaster.

Most people treat product roadmaps as if they’re a set of sure things: follow steps A, B, and C, and you’ll win. But real product work faces incomplete data and random outcomes. As Annie Duke puts it in her book, Thinking in Bets:

“What makes a decision great is not that it has a great outcome. A great decision is the result of a good process...”

So, what’s the process?

First, treat every new idea — a feature, a redesign, a pivot — as a “hypothesis.” Will this change actually improve something users care about? Write it down in simple terms, like: “If we build an onboarding guide, new users will activate 20% faster.”

Now, here’s where the betting mindset helps. It asks:

• How confident are we?
• What info could prove us wrong?
• What’s the easiest, cheapest way to test this in the real world?

Quick bets matter because the future is fuzzy. If you gamble 12 months on a huge launch without real-world data, you might be in for a nasty surprise. Instead, top teams borrow from poker pros and run smaller experiments — MVPs, A/B tests, surveys with context. They measure, learn, and shift their bets if the early evidence says “nope.”

Spotify is a good example. Their “bets board” is a public way to show which pitches are actual priorities, what’s being tested, and which ones move on after the first round. Nothing sits still. If the initial result flops, the team adapts or walks away.

This approach also helps avoid “resulting” — blaming or praising people based mainly on whether the final result was good or bad. Duke explains (via Evan's Notes):

“Just because you lose doesn’t mean you made a bad decision. And just because you win doesn’t mean you made a good one.”

The product is a game that combines luck and skill. Short, smart experiments let you learn fast, place more bets, and react before it’s too late.

So next time you face 20 options, resist the urge to play it safe and delay until you “know for sure.” Instead, pick a handful, write out hypotheses, and ask: “What’s our bet? How will we know if it’s working?” Ship small. Learn fast. Then double down where the evidence is strong.

That’s real product strategy: it’s not about avoiding risk, but managing it—one bet at a time.

When it comes to getting your product into the hands of customers, many new product managers start with the channel. They ask, “Should we sell through partners, go viral, or build a sales team?”

Ben Horowitz puts it simply:

“A properly designed sales channel is a function of the product that you have built and the target … that you wish to pursue.”

In other words, the product and the target market come first. The channel comes later.

Let’s break that down.

Think of the channel as the path your product takes to reach the customer. This path depends on two things:

1. What your product does
2. Who it is for

If you skip straight to picking a channel without thinking about those two things, you risk picking the wrong path.

Here’s a simple example

You are a PM at a startup called MedTrack. You’ve built a platform that helps mid-size hospitals track the status of equipment like MRI machines, ventilators, and infusion pumps. The product reduces downtime and avoids missed appointments.

Your target market is hospital administrators and biomedical engineering teams. They care about reliability, compliance, and cost savings. They work in regulated environments and require robust data security measures.

Given the product and target, what channel makes sense? Probably not a self-serve website with a credit card sign-up. Your buyers need trust and proof. They may want demos and integration planning.

A better path would be:

• Inside sales teams who can book demos with administrators
• Industry conferences where decision-makers attend
• Partnerships with hospital IT system vendors who can refer your product

Now imagine if you had skipped the product and target thinking. You might have copied a popular startup and gone for a viral, freemium model. That could bring in lots of small clinics or even individual doctors, but your platform is not designed for them. You would spend time chasing the wrong audience.

The lesson is simple.

Start with your product’s strengths and the needs of your target customers. Let those define the best way to reach them. The formula is:

Product + Target = Channel

Not the other way around.

Here’s another quote worth keeping in mind:

“When I ask new entrepreneurs what their distribution model will be… ‘distribute our product like Dropbox did.’ … demonstrates a deep misunderstanding of how sales channels should be designed.”

For new product managers, the takeaway is this: Do not pick a channel because it worked for someone else. Pick it because it fits what you are selling and who you are selling to. That is how you set up your product for growth.

The press around AI putting pressure on well-established SaaS companies is gaining some momentum.

Note: We are not discussing specific stocks and valuations. Our focus is on the impact of AI on software companies.

Analyst Ratings Published 08/11/2025…

"Melius Research downgraded Adobe… warns of ongoing multiple compression for software-as-a-service companies… ‘AI is eating software’ …”

AI isn’t a shiny add-on anymore. It’s like a sneaky wave that’s pushing SaaS valuations lower. Investors see that and say: This might be more than a passing trend.

“Redburn-Atlantic downgraded shares to Sell, warning that generative AI tools are eroding the company’s competitive advantage …”

When analysts note genAI is eating away at your moat, that’s a red flag. For product folks, that means no more resting on past glory. You have to rethink the value your product brings.

On a related note:

“Atlassian laid off about 150 employees… AI will replace many support and operations roles to cut costs and boost efficiency.”

Jobs are vanishing because AI can now handle repetitive tasks.

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For us on the product side, that points to two things: rising expectations for automation and shrinking tolerance for any friction.

These moves aren’t just headlines. They reflect a shift in how markets see SaaS. Using Adobe as an example, there are several software companies facing similar scrutiny.

When AI starts chiming that its tools can do your job faster or cheaper, investors get jittery. That changes how product managers should think: it’s no longer enough to polish features. AI-first competitors or internal automation can upend your roadmap in weeks.

The message from Wall Street? If the AI around your space is improving fast, your users could be just one model away from switching. And it’s not just about competition—it’s about cost, integration, and perceived value. If your AI isn’t sticky, it’s replaceable.

For product leaders reading this, here’s what’s buzzing in the market:

• Valuation risk is real, even for heavy hitters. If you don’t keep your competitive edge, investors will punish multiples fast.
• Expect AI to be a force multiplier or a replacement. Your value-props need to lean into what only you can do—be it deep domain context or specific workflows.
• Speed matters. Markets are reacting now, not in some distant “AI future.” You’ve got to move on to experiments, integrations, or whatever keeps your product relevant.

This isn’t about doom-scrolling or hype. It’s about reading the room. Adobe’s drop says investors are cautious. Atlassian’s restructuring signals that internal teams are already adapting to AI. By now, you should all be seeing these shifting trends in your product areas and business.

As product managers, those aren’t distant storms—they’re the new normal.