When Reddit’s stock tumbled this week on concerns about traffic and AI exposure, headlines focused on the numbers. Stock prices fluctuate all the time. But the more interesting story is not Reddit’s market cap. It is the shifting landscape of how people and platforms connect to knowledge in the age of Answer Engine Optimization (AEO).

Promptwatch reportedly showed that on September 30, Reddit content was cited in just 2 % of ChatGPT responses — down from ~9.7 % one month earlier and a ~14 % peak earlier in September.

For years, platforms like Reddit served as cornerstones of the “long tail” internet. Their archives of user discussions helped train large language models, and their pages often dominated traditional search results. That dual role made them both a valuable content source for AI systems and a key destination for users. But as AI-generated answers become the first stop for information seekers, the dynamics of discovery are changing.

From SEO to AEO

Search engine optimization (SEO) shaped how content was created and distributed for two decades. Now, answer engine optimization (AEO) is taking its place. Instead of designing content for Google’s ranking algorithm, platforms must think about how their data is used, filtered, and surfaced within AI models. The question is no longer “How do I rank on Google?” but “How do I remain visible in AI-powered conversations?”

AEO is sharper and more volatile than SEO ever was. A single change in how AI systems prioritize, summarize, or cite sources can dramatically alter traffic flows overnight.

(Note: some practitioners also describe this as “AI-enabled optimization” or “generative engine optimization,” but AEO is the most widely recognized term.)

The Fragile Middleman Position

Platforms that generate content but don’t control how it is represented in AI sit in a precarious position. If AI systems choose to de-prioritize or summarize their content rather than link back to it, the referral funnel shrinks. That not only affects advertising revenue but also bargaining power in data licensing negotiations.

This fragility highlights the bigger AEO challenge: organizations need strategies not just to create content but to ensure that content is represented fairly, cited accurately, and surfaced consistently by AI systems. In other words, optimization now means building trust and visibility with algorithms that explain the world to users.

Key Strategic Questions

The lesson here goes beyond any single platform. Product managers and technologists should ask:

• How is our content being represented in AI systems?
• What is our strategy for AEO visibility, not just SEO ranking?
• How do we build resilience if AI platforms shift how they cite or filter our data?

The market wobble we saw this week is a symptom of a deeper reordering. As AEO overtakes SEO, the winners will be the platforms and products that proactively shape their role in the AI discovery stack rather than waiting for visibility to disappear.

OpenAI’s Sora 2 is not just a model upgrade. It’s text-to-video with sound, physics that make sense, and a social app where anyone can remix clips. That shifts AI video from a lab demo to something that can spread in the wild.

The following screenshot is from the video generated realistically with this prompt (shared by the Sora team):

"A person is standing on 2 horses with legs spread. make it not slowmo also realistic. the guy fell off pretty hard in the end. single shot."

(Source: Sora 2 launch page)

See several more examples on the launch page and video.

What’s newly possible

• Shot-quality clips. Prompts produce motion that feels natural. Think ads, explainers, or concept reels generated in minutes, not days.
• Sound baked in. Dialogue, effects, and ambience sync with the video. No more layering audio later.
• Consent-based likeness. People can upload their face as a “cameo” and decide where it can be used. That makes participatory campaigns and employee-driven training safer.
• Remix culture. The feed is built for branching from someone else’s clip. That’s viral loops and A/B tests built into the product.
• Guardrails visible. Every clip carries a watermark and metadata. Public figures are blocked unless they opt in.

It’s not long-form filmmaking. But it’s fast, convincing, and designed for scale.

Who gets disrupted first

• Stock libraries. Generic B-roll loses its edge when a prompt can deliver the same thing. That’s why Shutterstock and Getty are pivoting toward indemnified models and training-data deals.
• Low-end production. Explainers, social ads, concept reels—the first budgets to move from film crews to prompt crews. Competing tools from Runway, Luma, and Google’s Veo will drive costs toward zero.
• Creative ops. Generation gets cheap. Measurement gets hard. Teams that can run weekly head-to-head tests across variants will pull ahead.
• Talent contracts. Cameos, watermarks, and union rules mean likeness rights shift from static agreements to consent you can revoke. Expect more negotiation around compensation and control.
• Social feeds. A remix-first graph competes with TikTok and Reels for attention. If it compounds, the distribution power could shift from recommendation algorithms to prompt networks.

That’s the consumer side. But the enterprise side might hit harder.

The B2B angle

• Internal comms. Training and onboarding videos in hours, not weeks. No camera crew required.
• Marketing and sales. Instead of one polished video per quarter, imagine a hundred variants—each tuned to industry, region, or buyer persona.
• Product demos. Hardware teams show how something might work before it exists. SaaS teams pitch vision features as moving clips.
• Customer support. Text-heavy FAQs become ten-second clips with voice and motion. Easier to follow, faster to scale.
• Compliance. Watermarks and provenance matter. Enterprises can adopt synthetic video while staying inside disclosure and risk frameworks.

For B2B, the value isn’t viral reach. It’s efficiency and personalization. And that’s where budgets shift fastest.

The bigger unlock

AI video just crossed into everyday work. Stock footage, low-end ad production, and social feeds feel it first. Enterprises will follow, swapping static comms and slow production cycles for fast, dynamic, personalized clips.

The opportunity is obvious: more ideas, faster. The question is whether talent, compliance, and measurement catch up before the content flood arrives.

The World Economic Forum’s Future of Jobs Report offers a clear signal for product managers, technologists, and business leaders: the skills that matter most in the coming decade are not the same as those that powered the past. Well, the report is confirming what we are already seeing in full force:

By 2030, success will hinge less on manual or routine capabilities and far more on adaptability, creativity, and fluency in technology.

(Source: Future of Jobs Report - 2025)

From Core to Critical

Employers already prize analytical thinking, creativity, and resilience. The survey shows these will only grow more important. Skills such as technological literacy, creative problem solving, and flexibility in the face of change are expected to be among the most valuable differentiators. For teams building products, this translates into cultivating people who can handle ambiguity, generate novel solutions, and adopt new tools quickly.

The Rise of Emerging Skills

The data also highlights a set of “emerging skills” that are not yet widespread but will be central to the 2030 workplace. These include AI and big data, networks and cybersecurity, and environmental stewardship. Each reflects broader economic and social shifts:

• AI is rapidly embedding itself into product development, decision-making, and customer experiences.
• Cybersecurity is a baseline expectation as connected systems multiply.
• Sustainability is no longer a niche concern but a core business responsibility.

For product leaders, these areas suggest where future hiring, training, and partnerships will be concentrated.

The Decline of Traditional Capabilities

In contrast, several skills are fading. Abilities like manual dexterity, endurance, precision, and quality control are projected to decline as automation, robotics, and advanced manufacturing systems take over repetitive work. While these were once competitive advantages, they will matter less in knowledge-driven industries.

Human Skills Still Anchor Teams

Not everything is shifting. Human-centric skills—empathy, leadership, active listening, and service orientation—remain steady. They may not grow as fast as digital literacy or AI expertise, but they are essential to building high-performing teams and customer trust. These relational strengths are harder to automate and continue to distinguish effective organizations.

The Takeaways

The big picture is clear: the next decade rewards those who combine human adaptability with technological fluency. Product managers and technologists should:

• Invest in continuous learning to stay ahead of new tools.
• Build resilience and creativity as team norms, not individual exceptions.
• Balance technical training with human skills like empathy and influence.
• Anticipate sustainability and ethical awareness as mainstream product requirements as AI gains broader adoption.

The workforce of 2030 will not be defined by who can execute tasks the fastest, but by who can adapt, learn, and create value in systems where humans and technology work together. (Pro tip: 2030 is here and now; these skills are required today)

Seven hundred million people use ChatGPT every week. That’s not just a user base, that’s a distribution channel that makes traditional retail look small. With its new Instant Checkout feature, OpenAI isn’t just adding payments. It’s signaling that AI platforms are on their way to becoming full-blown storefronts.

For product strategists, this marks a shift as significant as the arrival of the App Store. Distribution itself is being rebuilt inside AI platforms.

From Infrastructure to Distribution

Hyperscalers like AWS, Microsoft Azure, and Google Cloud already dominate the infrastructure layer. But discovery and distribution have always been more fragmented — handled through search engines, websites, cloud marketplaces, or direct sales.

OpenAI’s Instant Checkout changes that. Instead of sending users off-platform to make a purchase, ChatGPT now enables purchasing within the flow of conversation. A user asking for recommendations can complete a purchase without leaving the chat.

When someone asks a shopping question—“best running shoes under $100” or “gifts for a ceramics lover” — ChatGPT shows the most relevant products from across the web. Product results are organic and unsponsored, ranked purely on relevance to the user.

If a product supports Instant Checkout, users can tap “Buy,” confirm their order, shipping, and payment details, and complete the purchase without ever leaving the chat. Existing ChatGPT subscribers can pay with their card on file, or other card and express payment options.

This is the same platform playbook Apple perfected: pair a ubiquitous interface with seamless transactions, and you control the distribution channel.

How Instant Checkout Works

The feature is powered by the open-standard Agentic Commerce Protocol, built with Stripe. For merchants already on Stripe, enabling it takes a single line of code. Others can participate through a shared payment token API or delegated payments spec.

Security is handled through encrypted tokens that work only for specific amounts with specific merchants, with users confirming every step. That balance of simplicity and control is designed to speed adoption.

For now, it’s limited to Etsy sellers and select Shopify merchants, with single-item checkout only. But multi-item carts and broader merchant support are already in the works.

What It Means for Product Managers

This shift isn’t about replacing existing channels like websites or apps. It’s about adding a new distribution path where user intent is unusually high. When someone asks an AI assistant for recommendations, they are often ready to act.

Three takeaways matter most (until things change, which they will sooner or later):

1. Discovery is moving inside AI interfaces. Placement in ChatGPT’s responses may become as important as Google rankings or app store charts. Instant Checkout availability is now a ranking factor alongside price and quality. Answer Engine Optimization (AEO) is real.
2. Merchants still own the customer relationship. OpenAI only shares the minimum order data, with explicit user permission. That’s different from marketplaces that tightly control customer data.
3. Economics are favorable. There’s no upfront cost. Merchants only pay a small fee on completed transactions, making it easy to test.

History suggests caution. The App Store showed how quickly a platform can exert control over pricing, margins, and discoverability. Developers risk lock-in if they rely too heavily on a single AI ecosystem. And because recommendation algorithms are opaque, visibility could be hard to manage.

The Bigger Picture

Today, Instant Checkout looks like a convenience feature. But in reality, it’s the start of AI-native commerce — where discovery, recommendation, and purchase happen inside the same conversational flow.

The landscape is changing quickly.

For product managers, the strategic question is no longer just what to build, but where to distribute. The companies that figure out how to align with AI-native distribution early will define the next era of digital commerce.

The question isn’t whether this will scale. It’s whether you’ll be ready when it does.

The sugarcoating is over. Walmart’s CEO Doug McMillon says, “AI is going to change literally every job.” Accenture’s CEO Julie Sweet is blunt too — some employees will be retrained, others will be exited. The world’s biggest employers are making it clear: if workers don’t adapt, they risk being left behind.

The Existential Risk

The risk is not just about losing jobs, but about jobs losing relevance. At Walmart, warehouse automation is already cutting some roles, while new positions like “agent builders” are emerging. At Accenture, entire categories of white-collar work are being redefined, and those who can’t adapt are being left behind.

This is the existential moment for the workforce. Level up, or get sidelined.

Why There’s Room for Optimism

This isn’t just a story of loss. Walmart plans to keep its headcount flat at more than 2 million employees over the next three years. The mix of jobs will change, but people will stay at the center, especially in customer-facing roles. McMillon’s goal: “create the opportunity for everybody to make it to the other side.” Walmart’s leadership is signaling that transformation means repositioning workers, not eliminating them.

Accenture is saying the same. It has retrained over 550,000 employees in generative AI and doubled its specialist ranks. Its report stresses that AI is a collaborator that redesigns work and augments human capacity. Companies that invest in people will win more than those that focus only on systems.

What Needs to Happen Next

For workers: the mandate is simple. Learn and adapt. AI fluency plus human skills like empathy, problem-solving, and resilience will define employability. Employers may offer retraining, but it’s your career. Own it. Reinvent before the market forces you to. The alternative is painful.

For leaders: balance the spend between tech and people. Don’t just deploy tools. Map roles — which are declining, stable, or growing. Build clear paths for employees to shift. Be transparent about what’s really changing.

The Takeaway

AI is an existential wake-up call. Existential doesn’t mean fatal, but it does mean urgent. The choices made now will decide who thrives and who gets left behind. The future of work is not man versus machine, but man with machine — if we’re willing to level up.

The next 18 to 36 months will be decisive. AI is already rippling through industries, and skills are aging fast. Workers are not powerless. The future belongs to those who take control, learn continuously, and lean into the strengths machines cannot replicate. Reinvention is not just survival. It’s the path to relevance and opportunity in the age of AI.

Last year, I wrote about two product management mindsets: the Architect who blueprints everything upfront, and the Gardener who plants seeds and discovers what grows.

That framework made sense when humans did all the work. Not anymore (or not very soon).

AI is changing the game. It can architect better than architects—generating requirements, writing specs, and creating test cases. It can garden better than gardeners—running thousands of experiments, adapting in real-time, finding patterns we'd never see.

So what's left for product managers?

The Orchestrator Emerges

Think of a head chef in a modern kitchen. They don't chop vegetables—machines do it faster. They don't perfect recipes—AI optimizes them better. But they do something crucial: they decide what experience to create and ensure all the pieces serve that vision.

That's the Orchestrator. You're not competing with AI at execution. You're conducting a hybrid team of humans and machines to create something meaningful.

What Orchestrators Actually Do

They manage paradoxes. AI says remove that barely-used feature—it's dragging down your metrics. But you know it's why your power users stay. AI suggests surge pricing would boost revenue 23%. But you understand it would break trust with your community. The Orchestrator holds these tensions.

They protect the soul. Every AI recommendation optimizes for something measurable. But not everything that matters can be measured. That delightful animation that adds 200ms load time? The AI wants it gone. The Orchestrator knows when efficiency becomes sterility.

They design for emergence. You're not building a product anymore. You're building a platform where AI agents generate features, ML personalizes everything, and the experience morphs for each user. The Orchestrator ensures it still feels coherent, not chaotic.

The Daily Reality

• Your AI generates 15 feature variations based on user behavior. All test well. You pick the three that strengthen your product's identity, not just its capability.
• AI identifies an optimization that would improve conversion 8%. You recognize it would also make you look exactly like your competitors. Pass.
• A platform partner wants integration. The AI models show clear revenue gain. You dig deeper—would this partnership amplify your core value or dilute it?

This isn't about being anti-AI. It's about being strategic with AI.

Building Orchestration Skills

Develop taste. You need to recognize quality without creating it. Study products that resonate. Understand why some features feel right even when metrics disagree.

Get comfortable with indirect control. You're not writing every requirement or reviewing every design. You're setting boundaries, defining principles, and shaping the environment where good decisions emerge.

Learn to speak multiple languages. You translate between AI capabilities and human needs. Between engineering constraints and business goals. Between data insights and customer intuitions.

The New Success Metrics

Forget velocity. Measure coherence—does your product feel like one vision or a grab bag of optimizations?

Forget just retention. Measure resonance—do users feel something, or just return out of habit?

Forget coverage. Measure courage—are you brave enough to say no to AI recommendations that would boost metrics but erode meaning?

What This Means for You

The Architect and Gardener were about how to build. The Orchestrator is about what's worth building and why.

AI will make product management easier in some ways—less grunt work, faster validation, better insights. But it makes leadership harder. When you can build anything quickly, choosing what to build becomes everything.

The companies that win won't be those with the best AI. They will be those with the best Orchestrators—leaders who ensure that in our rush to optimize everything, we don't optimize away what makes products worth using.

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