Most of the traffic hitting websites today is no longer human. Cloudflare’s AI Insights dashboard makes this clear: the majority of crawling comes from AI bots, and the balance of power among those bots is shifting fast. For product managers, that reality changes how we think about traffic, attribution, and strategy.

Training bots dominate

Close to 80% of AI crawler traffic serves training purposes. These bots pull content to feed large language models, but they don’t bring visitors back. Unlike search crawlers, which at least create discoverability, training bots extract without referral. For content owners, this means the bulk of AI traffic is non-monetizable.

Takeaway: Track which bots are active on your sites. Segment training crawlers from user-action or search crawlers, since only the latter categories have the potential to send real traffic.

The crawl-to-refer gap

Cloudflare data highlights a striking mismatch: some AI platforms crawl tens of thousands of pages for every single user referral they generate. This “crawl-to-refer ratio” shows that most current AI traffic has limited downstream value for publishers.

Takeaway: Treat crawler monitoring as a business metric, not just a technical safeguard. The ability to measure crawl-to-refer ratios can inform decisions about when to allow, block, or charge for access.

A shifting bot ecosystem

Not all crawlers are equal. GPTBot, ClaudeBot, and Meta-ExternalAgent are now among the top training bots. On the user side, ChatGPT holds a clear lead in popularity, but competitors like Claude and Perplexity are gaining ground quickly. This dynamic suggests a multi-platform landscape where influence is distributed but volatile.

Takeaway: Don’t tie referral or integration strategies to a single AI platform. Instead, design flexible approaches—APIs, content partnerships, and bot-aware licensing—that can adapt as the ecosystem reshuffles.

Product strategy in an AI-first web

The web’s center of gravity is tilting away from direct human browsing toward machine aggregation. For product managers, the challenge is turning that shift into an opportunity. That means:

• Measuring crawler behavior with the same rigor as user analytics
• Negotiating access and licensing instead of passively absorbing bot traffic
• Experimenting with attribution and monetization models designed for AI-mediated consumption

The key insight is simple: AI bots are not just background noise. They are now first-class participants in the web economy. Recognizing their patterns—and acting on them—is becoming a core part of product strategy.

When Google teased three bananas in a post from CEO Sundar Pichai, the internet buzzed with curiosity. The reveal—Nano Banana (aka Gemini 2.5 Flash Image), a new AI image editing model. It was more than a quirky codename. It signals a shift in how we think about digital creativity. Unlike earlier AI tools that struggled to maintain consistency or required heavy post-editing, Nano Banana delivers precise, natural-language edits while keeping subjects unmistakably themselves.

This is not just another AI novelty. It is a disruptive technology with wide-ranging implications.

A New Creative Baseline

For decades, advanced image editing has been the domain of professionals using complex tools like Photoshop. Nano Banana lowers that barrier dramatically. A prompt like “remove the stain on this shirt” or “add a pet to this photo” yields high-fidelity edits instantly. For advertising and marketing teams, that means faster iterations, lower production costs, and the ability to personalize assets at scale. In entertainment and media, it blurs the boundary between professional-grade editing and consumer creativity.

The implication is clear: what once required specialized skills and hours of work is now accessible to anyone who can type.

Unsurprisingly, more sell calls from analysts on Adobe. Recently, by Rothschild Redburn:

"We spent the last few days testing the recently released preview of Google’s Nano Banana image editing model. We believe it will disrupt Photoshop, one of Adobe’s most widely used applications, adding to ongoing pressure on the company’s seat growth and pricing power. Nano Banana and Runway’s Aleph demonstrate the leap forward in the performance of generative AI tools in recent months, and the pace of improvement is a key concern: image and video generation models with fully editable outputs look increasingly likely to emerge within months, which we argue will call into question the durability of Adobe’s moat. We reiterate our Sell rating." Analyst: Omar Sheikh

I discussed this topic of AI disruption to SaaS companies.

Shifting User Expectations and Platform Power Plays

When users experience AI that edits with precision and consistency, their expectations change. They will demand frictionless, prompt-driven creativity across platforms. Legacy tools that still rely on manual adjustments risk falling behind if they cannot integrate AI with comparable ease. This sets a new baseline for how people interact with digital media—intuitive, conversational, and fast.

Nano Banana is not just a consumer feature. By embedding it in Gemini, Google positions itself as more than a chatbot—it becomes a visual creativity platform. With APIs available through Gemini, AI Studio, and Vertex AI, developers can integrate Nano Banana directly into products, workflows, and apps. This gives Google a strong ecosystem play and puts pressure on Adobe, Canva, and OpenAI to match both technical precision and platform reach.

Long-Term Implications

The trajectory is clear: image editing is becoming as simple as typing. That democratization will reshape creative roles, shifting emphasis from technical skills to conceptual direction. We are likely to see new roles emerge—AI content editors, authenticity auditors, and brand integrity managers—focused on curating and verifying output rather than manually creating it.

For product teams, the immediate opportunity is to experiment: use Nano Banana for prototyping, content workflows, and rapid iteration. But the longer-term question is one of responsibility. How do we balance empowerment with safeguards, ensuring creativity thrives without eroding trust?

Closing Thought

Nano Banana may look like a playful launch, but its implications are serious. It represents the next step in the evolution of AI—tools that are not just generative, but precise, accessible, and embedded into everyday platforms. For technologists and product leaders, this is a signal moment: the future of creativity is being rewritten, one prompt at a time.

AI in product management is no longer a question of if. It is a when. And when we say 'when,' we are not talking about years. We are talking months, given the pace of innovation and adoption.

A new study in Management Review Quarterly, “Where does AI play a major role in the new product development and product management process?” by Aron Witkowski and Andrzej Wodecki, maps out the current state of AI in product work. It synthesizes more than 190 publications and practitioner insights to show where AI is already embedded and where research has yet to catch up.

Where AI is Already Making an Impact

The research, combined with our daily experience, highlights early discovery as one of the most active areas of AI.

Product teams are already experimenting with AI-powered tools that generate product requirements documents, saving hours of manual formatting and ensuring consistency across teams.

Some PMs use AI-driven prototyping platforms that can translate a natural language prompt into a clickable interface, allowing faster validation of early ideas. Others lean on AI assistants that generate user stories directly from interview transcripts or customer feedback, making backlog grooming less about rewriting and more about prioritizing.

These examples show how AI is no longer limited to analytics or personalization. It is working its way into the daily fabric of product management—the unglamorous but essential tasks that keep product cycles moving.

Where Research is Still Lacking

The Witkowski study also identifies blind spots. There is a lack of systematic research on how AI can influence concept testing or post-launch validation. Integrative frameworks that span the entire product lifecycle are almost absent. Questions of trust and transparency, which directly affect adoption, remain underexplored.

This gap matters. The activities most critical to practitioners—how AI can accelerate experiments or help close the loop after release—are barely documented in academic work. For example, while AI can now generate dozens of prototype variants instantly, there is little evidence to suggest whether this accelerates decision-making or overwhelms teams with choices. Similarly, while AI can generate user stories, there is no structured evidence that these outputs improve product outcomes or prevent the introduction of new biases.

Why This Matters for PMs

For PMs, the takeaway is that you are the testbed. The tools exist, but the validated playbooks do not. Your experiments with PRD generators, your trials of vibe-coding prototypes, and your attempts to scale user stories with AI are not just tactical choices—they are contributions to the collective understanding of what works.

Because academic research often lags by years, product managers are often the early researchers in their own field. Each team’s learnings can ripple outward, closing the gap between practice and theory.

Closing Thought

AI is already woven into the toolkit of product managers, and its presence will only grow. The question is not whether it belongs, but how quickly PMs can adopt responsibly and share what they learn. By using today’s tools thoughtfully and documenting outcomes, product managers do not just adapt to the future—they help define it.

Claude for Chrome (now in pilot), Perplexity’s Comet, and Dia are all pushing the idea of a browser that doesn’t just display pages but acts within them. But as soon as you let an AI click, type, and execute, the hardest problem comes into view: security.

The quiet threat of prompt injection

Anthropic deserves credit for going deep on vulnerabilities in its Claude for Chrome pilot.

“Some vulnerabilities remain to be fixed before we can make Claude for Chrome generally available. Just as people encounter phishing attempts in their inboxes, browser-using AIs face prompt injection attacks—where malicious actors hide instructions in websites, emails, or documents to trick AIs into harmful actions without users’ knowledge.”

In red-team testing, Claude executed malicious instructions in nearly one in four targeted cases. With defenses such as site restrictions, confirmations, and domain blocking, the rate dropped to 11 percent. For browser-specific tricks like hidden form fields, the success rate fell from 35 percent to zero.

Publishing these numbers is significant. Transparency matters when the attack surface is any page on the internet.

The optimistic angle

This is not bleak news. First, we now have metrics—attack success rates, challenge sets, benchmarked defenses—that create a shared language for researchers, vendors, and enterprises. Second, mitigations are already in play: permission scaffolding, domain filters, and classifiers are being tested in live pilots. Third, vendors like Anthropic are treating security as a design pillar, not an afterthought—limiting early releases to small groups, requiring explicit consent for risky actions, and publishing residual risks.

Security is not a side concern for agentic browsers. It is the defining challenge that will determine whether they are trusted or discarded. The encouraging sign is that, unlike earlier waves of technology, this one is starting with adversarial testing and layered defenses built in.

The headwinds are real. But so is the progress. If the future of browsing is agentic, the real race is not who ships first—it is who builds the browser that can act safely.

The most important product decision you make is not the roadmap. It’s not the features you prioritize or the markets you enter. It is the culture you build.

Culture is not a poster on the wall or a slide in a town hall. It is how decisions get made when nobody is looking. It is how teams respond to setbacks, how they argue about priorities, how they treat customers when tradeoffs get hard. Culture is the invisible operating system of your product organization — and it shapes every outcome.

Culture Is the Real Product

Features will evolve. Technology will shift. Markets will turn. What endures is the way your teams think, decide, and collaborate. Marty Cagan has argued that great product companies don’t win because they have better ideas. They win because they have empowered cultures where teams are trusted to make decisions close to the customer.

If you want consistent product excellence, you cannot rely on strategy slides or innovation slogans. You need a culture that sustains curiosity, collaboration, and courage every single day.

Everyday Behaviors Define Culture

Culture is not abstract. It lives in small, repeated actions.

• Do roadmap discussions encourage dissent or punish it?
• Do teams treat customer feedback as a gift or as an interruption?
• Is failure treated as a data point or as a career risk?

People crave belonging, but belonging is not the same as fitting in. The strongest cultures are those where people feel safe bringing their perspectives without needing to mimic everyone else. In product teams, that balance — belonging without conformity — is what drives both speed and innovation.

Hiring and Onboarding as Culture Design

Hiring is one of the clearest ways leaders shape culture, but it is only the beginning. When you bring someone into the team, you’re not just adding skills. You’re reinforcing or eroding cultural norms.

The goal is not to hire people who simply “fit in.” That creates mirrors, not complements. The goal is to hire people who align with our core values — including curiosity about customers, comfort with ambiguity, and a willingness to collaborate — while adding new perspectives that stretch the team’s thinking.

Equally important is how you onboard. Even the best hires will drift if your rituals and norms don’t reinforce the culture you want. The way teams run standups, review designs, or handle retrospectives all shape how new people understand “how we do things here.”

Leaders as Culture Carriers

Leaders cannot delegate culture to HR or an annual workshop. They are culture carriers. Teams watch what leaders reward, what they tolerate, and what they model.

A leader who celebrates data-driven insights but ignores them in decision-making weakens the culture. A leader who claims collaboration is a value but rewards only individual heroics undermines the value of collaboration. Conversely, when leaders empower teams, welcome dissent, and stay customer-focused, those behaviors cascade across the organization.

Strong product cultures strike a balance: aligned enough to move fast, diverse enough to avoid blind spots. Belonging gives teams cohesion. Stretch gives them creativity.

Always Dynamic

Culture is not static. It is designed, reinforced, and tested every day. Hiring shapes it. Onboarding cements it. Rituals sustain it. Leadership amplifies it.

As a product leader, your roadmap matters. But the culture behind it matters more. Because in the end, the features your teams ship are built on the foundation of who they are and how they work together. Culture is the product you ship every single day.

Key Takeaway

The jury is still out on whether AI will replace or empower software developers, but dismissing junior talent is a short-sighted approach. Their curiosity and adaptability make them the best positioned to thrive in an AI-driven future—qualities that matter more than years of experience.

Why This Matters

AI is reshaping the nature of engineering work. Leaders face pressure to cut costs and experiment with automation. Some argue junior developers are the most “replaceable” role. Others counter that they are the most essential. The question is not simply about efficiency, but about who will carry engineering organizations into the next era.

Two Diverging Perspectives

The Replacement View

Several leaders predict AI will soon take over tasks typically assigned to entry-level engineers. Dario Amodei believes AI could generate up to 90 percent of new code. Sam Altman has said jobs will “definitely go away.” Geoffrey Hinton has gone further, warning that AI could eventually replace white-collar work broadly.

This argument positions juniors as expendable—valuable tasks automated, oversight left to senior staff.

The Augmentation View

Others view AI as a lever, not a replacement. Thomas Dohmke expects the smartest companies to hire more engineers, not fewer. Andrew Bosworth describes AI as expanding the capabilities of developers. Mustafa Suleyman warns more about a widening skills gap than job loss.

This view highlights a crucial fact: juniors bring curiosity and drive that seniors, conditioned by legacy practices, often lack. As AWS CEO Matt Garman noted in a podcast (video), they are the most eager to experiment with new tools. Curiosity is not a “soft” trait—it’s the core ingredient for mastering rapidly evolving technologies.

Why Curiosity Beats Tenure

Years of experience are not always a proxy for adaptability. In many cases, “20 years of experience” can mean repeating the same year’s practices twenty times. Senior developers carry the weight of how things used to be done. Juniors, by contrast, arrive without baggage, ready to adopt AI workflows, test new approaches, and ask questions that break conventional thinking.

It's not even junior or senior: are you intellectually curious, hungry to make a difference, and ready to reinvent yourself at work?

This difference matters because AI is not just a coding assistant—it’s a paradigm shift. The engineers most willing to learn, unlearn, and relearn will be the ones who define the next wave of software.

Takeaway for Product and Tech Leaders

The debate is unresolved, but the path forward is clear:

• Value curiosity as much as expertise. Juniors bring energy and openness that AI will reward.
• Build a dual strategy. Utilize AI to automate repetitive tasks while investing in mentoring early-career engineers.
• Avoid false efficiency. Cutting junior roles may deliver short-term savings, but risks hollowing out the future talent pipeline.

Conclusion

AI may change how code is written, but curiosity, adaptability, and drive are timeless assets. Junior developers embody these traits more than any other group. The leaders who cultivate them—rather than replace them—will build organizations ready for whatever this fast-moving future holds.