Most product and marketing teams already know SEO. Search engine optimization has been the backbone of digital visibility for decades. But a new acronym is creeping into conversations: AEO, or Answer Engine Optimization.

I’m still digging into it, but here’s what I’ve learned so far—and why it matters.

From Search Engines to Answer Engines

SEO is about ranking high in search engine results. When a buyer types a question into Google, the goal is to appear in the top results so they click through to your page.

AEO shifts the game. Instead of search engines returning a list of links, answer engines like ChatGPT, Google’s Search Generative Experience, or voice assistants like Siri generate direct answers. The challenge is not just being visible, but being included in the AI’s answer itself.

One strategist put it simply: SEO makes you discoverable, AEO makes you quotable.

Why It Matters

For both consumers and businesses, the shift is significant.

On the consumer side (B2C), people increasingly expect quick, direct answers. If someone asks ChatGPT, “What’s the best smart thermostat?” they’re unlikely to scroll through ten blue links. They want a clear recommendation, ideally with trustworthy sources. Brands that structure their content to be cited directly stand a better chance of being the chosen answer.

On the business side (B2B), traffic quality often matters more than volume. Research from Lenny’s Newsletter found that traffic coming from ChatGPT or similar tools converts at much higher rates—about six times better than Google search traffic. That makes sense: someone asking an AI assistant about “the best tools for enterprise compliance” is already deep into problem-solving mode.

For startups especially, AEO may even be a faster path to visibility than SEO. Traditional SEO is slow and often favors big brands with domain authority. Answer engines, on the other hand, reward clarity, originality, and relevance.

What Works in AEO

I’m noticing a few tactics that keep coming up:

• Concise, answer-ready content: Lead with the definition or solution, then expand.
• Structured data and FAQs: Schema markup, how-to guides, and help center content are easier for AI to parse.
• Fresh, authoritative sources: AI favors content that looks recent, trustworthy, and not over-optimized.
• Presence in communities: Reddit, forums, and even YouTube transcripts are often cited by answer engines.

The Road Ahead

This feels early. Measurement is messy—there’s no clear equivalent to SEO dashboards yet. And no one knows exactly how these models choose sources. But the shift is happening. According to Amsive, one in ten U.S. internet users now begins searches with generative AI, and AI Overviews already appear in 16% of Google desktop searches.

For now, my takeaway is simple: SEO is still table stakes. But it’s worth experimenting with AEO—structuring content around the kinds of questions an AI might be asked, and making sure your product is the one it recommends.

Product managers love metrics. Dashboards, OKRs, funnel charts — these tools are everywhere. They give us a sense of control, objectivity, and accountability. But metrics have limits. They can only measure what already exists. They tell you how a current feature is performing, but they can’t tell you what to build next.

This is where intuition comes in.

What Intuition Really Means in Product Work

In product management, “intuition” often gets dismissed as gut feel. But good product intuition isn’t about hunches or ego. It’s the pattern recognition that comes from deep exposure to customers, market dynamics, and technology shifts.

Think of it as informed imagination: the ability to see possibilities before they can be quantified. Intuition draws from:

• Customer empathy — observing pain points in context, not just in survey scores.
• Analogies — connecting lessons from other industries or products.
• Experience — knowing what has and hasn’t worked in past launches.

Metrics can confirm or disprove hypotheses, but intuition generates the hypotheses in the first place.

Why Metrics Alone Fall Short

Relying only on metrics creates two common traps:

1. Local optimization: You improve the efficiency of what already exists (e.g., making a checkout flow one click shorter) but miss the chance to reimagine the flow entirely.
2. Blind spots: Metrics can't measure what hasn't been built yet. If you only follow numbers, you only follow the past.

These patterns mirror the cognitive biases that derail product teams — tunnel vision that filters for validation rather than genuine discovery.

A/B testing is the classic example. It can tell you whether a blue or green button performs better, but no test would have invented Slack, Spotify, or Figma. Those products emerged because teams trusted their intuition that people wanted to collaborate in new ways. This is where hypothesis-driven thinking becomes crucial — treating product ideas as bets to be tested rather than certainties to be executed.

Examples of Intuition at Work

• Consumer case: Netflix’s decision to invest in streaming. At the time, DVD rentals were still profitable. Metrics showed customers were satisfied with fast shipping and broad inventory. But leadership intuited that convenience would eventually mean no discs at all. Metrics couldn’t prove it; they had to make a leap.
• B2B case: Atlassian’s early focus on self-serve enterprise software. Industry metrics suggested long sales cycles and heavy customization were the standard. Intuition told them that small teams wanted to adopt tools without procurement overhead. That insight created a wedge that later scaled to the enterprise.
• Workflow/API case: Stripe’s early bet on developer experience. Payment processing was already “measurable” in uptime and transaction volume. But founders intuited that if APIs felt like consumer-grade products, developers would prefer Stripe — and that preference became market dominance.

Balancing Metrics and Intuition

The real skill isn’t choosing one over the other, but knowing when each applies.

• Use metrics to refine: once a product has traction, metrics can expose bottlenecks, conversion gaps, and churn drivers.
• Use intuition to invent: in early exploration, when the signal is too weak or the opportunity is undefined, intuition helps you generate and frame ideas worth testing.

A simple way to ask yourself: Am I solving an optimization problem or an imagination problem?

• If optimization, lean on metrics.
• If imagination, lean on intuition.

This framework complements other decision-making approaches that help product leaders choose the right level of analysis for each situation.

Takeaway for PMs

Metrics keep you honest about what’s working today. Intuition opens the door to what might work tomorrow. The best product managers don’t dismiss either. They cultivate intuition through user exposure, cross-industry learning, and reflection. And they validate intuition with metrics once a path is clear.

Conclusion

Metrics refine the present. Intuition invents the future. The danger lies in over-relying on one at the expense of the other. Great product managers balance both — knowing when to trust the numbers, and when to trust the deeper sense that customers are ready for something new.

Steve Jobs once asked John Sculley, “Do you want to sell sugar water for the rest of your life or come with me and change the world?” That question pushed Sculley to leave Pepsi for Apple, and it has lingered ever since as a reminder of the difference between comfortable success and transformative ambition.

The launch of the iPhone 17 makes the metaphor newly relevant. On paper, Apple delivered a strong upgrade: a Promotion display for the base model, a new camera-compute module in the iPhone Air, better cooling systems, larger batteries, and advanced sensors. Yet Ben Thompson captures the paradox neatly:

“Apple, to be fair, isn’t selling the same sugar water year after year in a zero-sum war with other sugar water companies. Their sugar water is getting better, and I think this year’s seasonal concoction is particularly tasty.”

The products are objectively stronger, but the reaction has been muted. For many, the updates feel like just another incremental step.

Sustaining Innovation vs. Disruptive Change

In innovation theory, sustaining innovation improves existing products while disruptive innovation creates new markets. Apple, at least with the iPhone, is firmly in the sustaining camp. Year after year, it makes the best smartphone slightly better.

There is nothing wrong with this. The iPhone remains one of the most profitable products in history. But perception matters. Apple once held the cultural position of reshaping industries, not just refining them. The company introduced touchscreens that replaced keyboards, the App Store that launched ecosystems, and sensors that powered whole new categories of apps.

Today, that mantle of frontier-shaping innovation seems to belong elsewhere.

Apple and the AI Gap

The current wave of excitement in technology is centered on artificial intelligence. OpenAI is defining consumer experiences with ChatGPT, Google is pushing AI into search, Microsoft is embedding copilots across productivity, and Anthropic is competing with rapid iteration. These companies are setting the tone for where the industry is headed.

Apple, by contrast, has been quieter. Its announcement of Apple Intelligence and its emphasis on on-device privacy are important, but they have not yet redefined the conversation. Thompson notes that this reflects long-standing choices: by not investing in large-scale cloud services or search, Apple protected its privacy reputation but limited its data leverage for AI.

This does not mean Apple is absent from the race. Its pattern has often been to enter late but redefine categories with a user-friendly design and integration. Still, perception matters. If Apple is seen as lagging in AI, it risks ceding cultural and strategic ground, even as it continues to sell record numbers of iPhones.

The Sugar Water Trap

This is the danger Thompson highlights: Apple risks becoming a luxury hardware company that iterates on refinement rather than taking the next leap. The iPhone 17 may be excellent, but it feels safe.

Profit incentives reinforce this pattern. By introducing the iPhone Air at $999, Apple created room to raise Pro prices and strengthen margins. Services revenue continues to climb, fueled by the installed base. The ecosystem lock-in is powerful. All of this makes Apple healthier financially than ever before.

But success creates inertia. When quarterly growth and customer loyalty are guaranteed, the urgency to disrupt yourself diminishes. That is the essence of the sugar water trap: being satisfied with selling something very good, while losing the role of reshaping the future.

For Product Managers

There are clear takeaways for anyone building products:

• Mind the perception gap. Internally, incremental improvements may feel monumental. Externally, they can look minor. Storytelling is part of the product.
• Balance bets. Mature products need sustaining innovation, but companies must also invest in future disruptions. This balance is hard, but essential.
• Beware of comfort. Success can make teams focus on optimization rather than reinvention. Leaders must actively fight complacency.
• Think beyond revenue. True innovation is not just about quarterly results. It is about planting seeds that could define the next decade.

The Bigger Picture

Apple is not selling sugar water — its products remain unmatched in quality. But the reaction to the iPhone 17, combined with the company’s cautious posture on AI, shows how perception shifts when a company leans too heavily on sustaining innovation. For Apple, the risk is losing its cultural position as the company that makes the future arrive early.

For product leaders everywhere, the lesson is sharper: incremental progress is not enough. To avoid the sugar water trap, we must balance the discipline of refinement with the boldness of reinvention.

Product leaders know what happens to a product without a strategy. It becomes a treadmill of backlog items, bug fixes, and reactive feature requests. The same thing happens to your career if you treat your job as nothing more than a stream of execution tasks.

Just like a product needs vision, prioritization, and trade-offs, so does your work. But here’s the challenge: execution will always crowd out strategy unless you intentionally design for it.

Execution Lives on the Manager Schedule

Paul Graham, in his classic essay, described two types of schedules. The manager schedule breaks the day into hour-long slots for meetings and quick tasks. The maker schedule protects large blocks of time for deep, creative work.

Execution naturally fits the manager schedule. Your calendar fills with updates, cross-functional syncs, and urgent asks. Strategy, however, requires the maker schedule. You cannot build vision or frame trade-offs in 30-minute increments between stand-ups.

When product leaders let the manager schedule take over, they become excellent at delivery but invisible in shaping direction. The organization sees someone reliable at execution, not someone ready for bigger leadership.

Treat Your Job Like a Product

The way out of this trap is to think of your role as a product. Products drift without a strategy. So do careers. Managing your job like a product means:

• Vision: Define what kind of leader you want to be known as. Are you the one who drives clarity in ambiguous bets? The one who scales cross-functional collaboration?
• Roadmap: Decide what outcomes matter most in your role. Not every meeting or email contributes. Prioritize the initiatives and conversations that advance your vision.
• Trade-offs: Recognize that every yes is also a no. Time spent on another status meeting is time not spent shaping a long-term decision.

When you apply product thinking to your job, it becomes obvious that execution-only work is equivalent to shipping features without a roadmap. You’re active but not strategic.

How to Reset the Balance

Escaping execution mode requires a deliberate reset. A few practical steps:

1. Audit your calendar. Look at the next two weeks as if you were doing a product backlog review. Which items deliver real strategic value? Which are just noise?
2. Block maker time. Create at least two half-day blocks per week where you do not take meetings. Use these for strategy thinking: defining trade-offs, clarifying bets, and preparing conversations.
3. Redesign conversations. Use part of your maker time to float early-stage ideas with stakeholders. These don’t need to be polished—think of them as prototypes for strategy.
4. Hold the line. Just as you wouldn’t let every feature request hijack a product roadmap, protect your maker time from ad-hoc asks.

The Leadership Shift

Execution keeps the lights on. Strategy moves the business forward. Product leaders need to do both, but only one requires you to be intentional about carving out space.

Treat your job like a product. Give it a vision. Prioritize its roadmap. Protect the maker time that allows you to think, design, and steer. Otherwise, you risk being excellent at execution but absent in leadership.

Following up on my earlier piece: Build, Buy, or AI-Build, in which I noted Marty Cagan's view that AI will not easily replace enterprise solutions, even in the age of “vibe coding.” His reasoning is sound: enterprise products are deeply embedded in intricate workflows, with business rules and integrations that can’t be swapped out overnight. Today, tools like Copilot or low-code builders tend to play a helper role rather than a wholesale replacement.

But this window may not stay open forever. While complexity and business rules protect enterprise vendors today, poor user experience erodes that advantage over time. Teams will always find creative ways to avoid clunky workflows, even when tools are mandated. The danger for product managers is assuming that mandates equal adoption.

Today, in this piece, my focus is on enterprise systems used by the employees.

Captive Audience, Hidden Costs

Enterprise software often treats UX as an afterthought. The assumption is that because IT or executives require the system, employees will use it regardless. In practice, small UX friction — extra clicks, confusing workflows, poor defaults — compounds across large organizations:

• Lost productivity: Minutes wasted per task multiply across thousands of employees.
• Shadow IT: Teams quietly adopt easier alternatives when friction gets unbearable.
• Weak adoption: Data quality and feature usage lag, eroding the intended ROI.

This isn’t just about tools with a user interface. Even workflow solutions orchestrated via APIs can create friction. If APIs don’t align cleanly with the way teams work, developers build brittle workarounds, or worse, they bypass the official system altogether.

Everyday Examples

Expense management systems are a classic case. Legacy tools require multiple steps to upload receipts, so employees procrastinate or submit incomplete data. Finance teams chase reports, audits become harder, and everyone loses. Meanwhile, lighter consumer-grade apps with auto-scanning features become the quiet alternative, even in “mandated” environments.

The same pattern shows up in CRMs. If logging an activity takes too many clicks, sales reps stop doing it. The CRM may be deployed company-wide, but the data quality is unreliable, undercutting pipeline forecasting.

The Risk Ahead

Cagan is right that enterprise solutions are not trivial to disrupt. But the UX gap is a growing vulnerability. AI and automation are advancing fastest, where they can simplify repetitive workflows and connect disparate systems. That’s exactly where small UX friction accumulates.

If PMs continue to treat UX as optional, they risk leaving the door open for AI-first competitors who don’t need to replace an entire system at once. They just need to replace the pain points — and once adoption shifts, switching costs fall.

The Internal Disruption Threat

Even if external competitors struggle to break in, disruption often starts inside the same company. In large organizations, different teams experiment with progressive solutions that sit closer to real customer pain points. These aren’t full system overhauls at first — they’re lightweight workflows, AI helpers, or specialized tools that relieve specific friction.

Over time, those internal solutions build momentum. They spread across teams, win executive sponsorship, and before long, they’re positioned as the “better way.” The once-mandated solution is gradually sidelined. It’s not easy, and it doesn’t happen overnight, but it does happen — especially when users are frustrated by persistent UX pain.

Takeaway for PMs

• Don’t mistake mandates for adoption. Measure friction in time, errors, and workarounds, not just logins.
• Treat both UI interactions and API-driven workflows as UX challenges. Orchestration that feels natural to developers and operators is as critical as clean screens for end users.
• Make the case for UX improvements by tying them directly to business outcomes: faster workflows, fewer tickets, higher data quality.

Conclusion

Mandates buy you an entry point with users, but not engagement. The enterprise moat of complexity is real, but it’s shrinking. PMs who treat UX as strategic, not cosmetic, can keep their solutions indispensable. Those who don’t may find that “helpers” — whether external AI products or internal workarounds — become replacements faster than they expect.

Claude just got a big upgrade. According to Anthropic’s announcement, it can now create real files: Excel spreadsheets, Word documents, PowerPoint decks, even PDFs—straight from your prompts (whether you're working in Claude.ai or the desktop app).

Here's how Anthropic puts it:

- Turn data into insights: Give Claude raw data and get back polished outputs with cleaned data, statistical analysis, charts, and written insights explaining what matters.

- Build spreadsheets: Describe what you need—financial models with scenario analysis, project trackers with automated dashboards, or budget templates with variance calculations. Claude creates it with working formulas and multiple sheets.

- Cross-format work: Upload a PDF report and get PowerPoint slides. Share meeting notes and get a formatted document. Upload invoices and get organized spreadsheets with calculations. Claude handles the tedious work and presents information how you need it.

Why this matters

This isn’t just another minor UI tweak. It represents a subtle—but powerful—step toward AI that delivers real outputs, not just ideas. Instead of explaining how to set up formulas or structure a deck, Claude just hands you a working file in one go.

Early chatter was enthusiastic. People are saying it’s a shift from AI as an assistant to AI as a coworker—especially useful in enterprise workflows, such as finance, where models and reports can be built, formatted, and ready in record time.

Absolutely—let’s focus on explaining how Claude’s file creation feature actually works, based on Anthropic’s documentation. I’ll keep it clear, precise, and handy for readers.

How Claude’s File Creation Feature Works

Claude’s new file generation feature runs on a private, sandboxed computing environment that allows it to write and execute Python or JavaScript code, enabling it to produce polished, functional outputs like spreadsheets, documents, and slide decks—all from your natural-language prompts.

Unpacking the Details

1. A Secure, Code-Capable Sandbox

Claude is now equipped with an isolated computer environment—sandboxed with limited internet access—so it can safely run code to process data and generate files. It uses trusted package sources (like npm and PyPI) but cannot freely roam the internet.

2. How File Creation Works

You interact with Claude by either uploading data, describing what you need, or letting it source details as part of the conversation. Claude then writes code—typically in Python or JavaScript—to:

• Clean and analyze data
• Build spreadsheets with working formulas and multiple sheets
• Create charts, reports, and formatted documents
• Deliver a file (e.g., .xlsx, .docx, .pptx, PDF) ready for download and use

3. Practical Controls & Safety Features

Claude provides you with transparency into what it’s doing—summaries of actions, real-time monitoring, and an easy stop/cancel option. Anthropic has also implemented several protective measures:

• Auditable actions: Claude summarizes its process so you can see what code is running and why.
• Execution limits: There are time and resource constraints to prevent infinite loops or misuse.
• Prompt injection safeguards: Anthropic built classifiers to detect and halt suspicious or malformed instructions.

I just got the access, and I’m about to give it a spin—will report back know how it goes.