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Zillow's CEO argues the company's future is transaction software and workflow that makes buying and selling easier, not just a listings app. In an AI-driven world, Zillow's advantage is execution and integration across agents and financing, while pushing for transparent, broadly marketed listings.
Creating a manager agent to coordinate coding agents reduces multitasking overhead and accelerates development, but it requires giving up some direct control over code quality.
The SaaS selloff reflects a belief that AI is breaking seat-based pricing and eroding point-solution moats. Software will keep growing, but value will shift to AI-native, outcome-driven layers that capture context and orchestrate work across systems.
Great design comes from love, courage, and consistency more than tools or process. Ideas travel through polish and alignment, and designers thrive by treating feedback as dialogue while balancing reliability with bold experimentation.
AI is breaking classic SaaS assumptions by shifting moats from interfaces to context and compressing timelines for ROI and adoption. The winners will design for agents, governance, and organizational intelligence rather than traditional seat-based workflows.
Streaming commoditizes music, limits artist leverage, and rewards scale over loyalty. The future belongs to artists who own their audience and monetize deep fan relationships, not playlist placements.
SaaS companies are chasing AI features while overlooking churn driven by human and competitive signals. Retention now depends on integrated risk monitoring, not just better product releases.
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Facebook's clutter is a byproduct of metric incentives, not design neglect.
A production AI agent is a data product with users, quality requirements, and a roadmap driven by real usage signals.
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Great AI products don't work in isolation - they operate across a user's entire workflow.
Software is shifting from human operators to AI agents, making Agent Experience more important than traditional User Experience. Metrics, pricing, and product design must move from seat-based human logins to programmatic, agent-driven integration.
Early-stage startups should choose one defining metric that proves the business is working and focus the entire company on growing it. Relentless discipline around that number prevents distraction and increases the odds of reaching product-market fit.
Engineering taste is the ability to calibrate product, system, and quality decisions based on a deep understanding of what is possible. As AI automates coding, this judgment becomes the key differentiator of great engineers, even though it is rarely taught or formalized.
Product builders should think like franchisors by building systems that enable others to deliver value consistently. Real scale comes from clear processes, focused pilots, and disciplined growth rather than individual effort.
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Even a low monthly churn rate of 5% means losing 46% of customers annually. But, not all is lost. Churnkey helps companies like Superhuman, Gamma, and Veed reduce both voluntary and involuntary churn. Take action with the free Churn Metrics [12] product. Also read our Stripe benchmarks report [13] for tactical advice.
As AI makes software easier to clone, durable advantages will come from non-code moats such as scale, proprietary data, and trust. Long-term value depends on what you uniquely know and who relies on you, not just how fast you ship.
Vibe PMing connects AI agents to analytics and workflow tools so PMs can describe problems while agents analyze data, draft specs, and route work. This shifts the PM role from manual coordination to higher-level thinking and decision-making.
Technical SLOs show whether systems are running, but business SLOs show whether users are getting value. Teams should define and measure outcome-based metrics that reflect real user success.
AI-native B2B companies are shifting from standardized products to systems tailored to each customer's workflow. Scale now comes from quickly modeling and encoding real business structures instead of imposing shared abstractions.
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Great product managers eliminate features that do not solve real problems.
Product teams often treat users as metrics instead of capable people.
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You're probably being asked to “use AI” more, but finding real impact is still harder than it sounds.
Cursor lets PMs execute, not just spec, but it works best with code literacy and patience for integration issues. Real leverage comes when AI workflows extend beyond one PM to the whole team.
AI agents turn conversation into action and push software costs toward zero, making unique insight, fast decision-making, and agent “skills” the real sources of value. Long-term abundance is possible, but the transition, especially around jobs and alignment, will be disruptive and must be handled carefully.
SaaS is not dead, but the old B2B playbook of category lock-in, heavy sales spend, and autopilot expansion is finished. AI-native companies are winning by shipping faster, delivering clear ROI, and earning retention through real innovation instead of switching costs.
AI makes building faster and cheaper, but it cannot replace human accountability, judgment, and community. As output becomes abundant, lasting advantage comes from ownership, differentiation, trust, and shared meaning, all of which remain human.
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Ever wish your team could get on the same page before committing to design? In this Miro session on March 3rd [9], Björn Ehrlinspiel, Product Owner at LUFTHANSA'S MILES & MORE loyalty program, will share how his team went from a two-week ideation process for an unvalidated product design to creating validated concepts in less than a day.
The Codex team is a small, autonomous group that embeds AI into every stage of development, from planning to code review and onboarding. By empowering full ownership and treating AI as a core teammate, they move at startup speed while maintaining high quality and scale.
PMs can design with AI by using structured workflows that turn ideas into prototypes quickly, then iterating with users and stakeholders. The key is choosing the right tools for each step and evaluating outputs for real problem-solving, accessibility, and feasibility, not just pretty screens.
Code has not been the real bottleneck in software productivity for decades. Weak user research and fractured organizational systems are. As AI speeds up coding, teams risk building the wrong things faster unless they prioritize human-centered research and sustained ownership over output.
Gmail won by reshaping both the economics and experience of email, combining cheap infrastructure, contextual ads, and a fast search-first interface to offer abundant free storage. Incumbents, trapped by scarcity mindsets and banner-ad models, could not adapt quickly enough to compete.
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People routinely misjudge what is happening inside tech companies because they lack the internal context behind decisions.
True fit reveals itself through organic word-of-mouth growth, not paid acquisition.
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The CEO wants to see that AI feature shipped yesterday. The code works. The model works.
AI will pressure SaaS, but it is not killing it. Large vendors still hold durable advantages in enterprise infrastructure and proprietary data, even as AI reshapes pricing and growth dynamics.
Product-market fit shows up as real pull, and it shows up quickly. Founders win by hiring underestimated talent, protecting their vision, working proactively, and applying advice with judgment rather than dogma.
Zero to one is about pruning, not speed. Success comes from knowing what you must own and cutting everything else.
AI agent pricing has not stabilized, so Salesforce is letting customers self-select across multiple models. The market is drifting toward hybrids that combine predictable seats with usage or outcomes, especially to avoid automation shrinking seat-based revenue.
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When everything is urgent, nothing is. Planview's ebook [9] on prioritizing what matters offers an AI-driven framework for identifying high-value work that drives real impact - not just work that feels important. It's a useful read for PMs tired of defending