Casey Winters writes about product strategy, marketplaces, and building companies from zero to one. Winters is a co-founder of SuperMe, an AI-native professional network.
AI-native means doing what was previously impossible, not speeding up an old process · Zero to one is about pruning fast, not building fast · AI agents will rewrite marketplace economics, and waiting is the expensive choice
Casey Winters writes about product strategy, marketplaces, and building companies from zero to one.
They co-founded SuperMe, an AI-native professional network designed to connect people with advice from professionals through their work and AI profiles.
Their writing pairs strategic frameworks with practical questions for founders: what to build, what to cut, and how to respond when technology changes a market.
Winters makes difficult product choices easier to reason about.
In “Zero to One Is About Pruning Fast”, they recast the choice between building broadly and polishing early as a question of learning speed and pruning burden.
They suggest judging features as hits, distractions, or potential compounders—and asking whether each supports the company’s long-term vision.
In “Now You’re Thinking with Portals”, they distinguish between preserving an old workflow, improving it with AI, and designing for tasks that were previously impossible.
The examples show that the most advanced approach is not always practical yet, and predicting which products will endure remains difficult.
Winters also applies marketplace thinking to AI agents: a LinkedIn post on agents and marketplaces frames discovery, transactions, and supply as points where agents could reshape marketplace economics.
These ideas can help founders and product leaders weigh roadmaps, AI strategy, and marketplace durability.
A SuperMe launch post connects those questions to a product vision: finding relevant professional perspectives rather than returning a single generic answer.
Casey Winters argues that the important question for a product team is not whether it uses AI, but whether it uses AI to do something that was previously impossible.
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That conviction runs through this writing: a tier system for judging AI-native work, a discipline for cutting features rather than shipping them, a warning that AI agents will reprice marketplace customer acquisition, and a product built to test the idea that search should return people rather than one averaged answer. The through-line is a preference for step changes over incremental improvements, paired with concrete operating tests teams can apply to their roadmaps.
AI-native means doing what was previously impossible, not speeding up an old process
Casey Winters treats the wave of AI manifestos from tech CEOs as a real strategic signal buried under backlash, and translates it into a single test: is a product or process doing something that could not be done before, or only making an existing process slightly faster?
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The distinction comes from the video game Portal, where a portal gun lets you reach places that were otherwise inaccessible; the game's AI narrator tells you, once you solve the puzzle, that "now you're thinking with portals."
Winters splits AI work into three tiers — the old way, the half measure, and the one shot — and argues that teams should watch for the moment a one-shot solution becomes possible, rather than becoming specialists in a transitional technique that will be obsolete within a year or two.
For products, the warning is sharper: do not build a half measure that cannot sequence into a one-shot solution you own, because a competitor or incumbent will build the one-shot on top of your market research.
Winters calls those escalators to nowhere and notes that growth eventually is neither recurring nor revenue.
Data retrieval, in three stages: write a SQL query yourself (the old way); ask ChatGPT to write the SQL for you (the half measure); have Cursor write a Python script that gets the data (the one shot).
Winters says writing SQL was fun but they will probably never do it again.
Image generation follows the same progression: Midjourney in early 2022 needed elaborate prompt engineering, while image generation in ChatGPT now often satisfies a plain sentence in one attempt.
On the product side, AI notetaking and AI prototyping are half measures that will not be used standalone as models advance.
Customer support tools such as Decagon, Sierra, and Fin, and coding tools such as Cursor and Windsurf, look more like one shots, becoming more agentic and reliable over time.
Winters says the specific classifications could be wrong and that predicting AI's trajectory has been humbling; the tiers are a decision aid, not a settled forecast.
Zero to one is about pruning fast, not building fast
Writing about building SuperMe with Ludo, a co-founder Casey Winters has also worked with at Pinterest and advised at Whatnot, they say zero to one is not mainly a question of speed.
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It comes down to two early bets: how much surface area to build, and how much design or brand polish to invest in.
Winters lays out a matrix of well-known companies that started with maximum or minimum features and maximum or minimum design, and argues neither choice is automatically right: more features speed learning but create a bigger pruning burden, while more polish strengthens positioning but slows learning.
The operating consequence is a formal experiment review: is this a hit, and if so, what comes off the roadmap to focus on it?
If not, is it a distraction or something whose value will compound as network density grows?
If it is a compounder, do you iterate now, let it sit, or defer it until density is higher?
Winters adapts four questions from an earlier platform essay written with Gilad — what the company needs to own, what it wants to compete to win, what it wants to attract, and what it wants to reject — and says a feature firmly in the first category is a compounder, while any other answer makes it either a hit or a distraction.
The summary is blunt: zero to one is not about building fast, but pruning fast.
If you do not know what you need to own, you will not know what to remove.
The Instagram founders cut their original social app Brbn down to its photo filters, and Pinterest did something similar with saving from its earlier shopping app Tote.
Both are examples of recognizing a hit and clearing the table of everything else to triple down on it.
Winters also notes the counter-pressure: for a feature maximalist, sunsetting other experiments is painful for builders and early users.
In consumer networks, the line between distraction and compounder is blurry because value rises with density.
AI agents will rewrite marketplace economics, and waiting is the expensive choice
Casey Winters argues that AI agents handling discovery, transactions, and even parts of supply will collapse the marketing funnel, rewrite customer-acquisition-cost math, and threaten the network effects that historically made marketplaces valuable.
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They break the threat into three agent types — Discovery, Transaction, and Supply — each eroding a different traditional moat.
The specific consequence they highlight is that payback-period CAC assumptions stop holding once an agent owns the user relationship.
The recommended response is a survival playbook: go agentic yourself, tighten supply integration, and rethink monetization.
This is a timing argument rather than a technology argument: founders who adapt early can build stronger businesses than the previous generation, while those who wait risk becoming fulfillment pipes for someone else's AI.
The LinkedIn text links to a fuller essay developing the three agent types and five-point survival playbook; the detailed recommendations are in the essay, not the post.
Search should return people, not one averaged answer
The SuperMe 1.0 launch doubles as an argument about search.
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Casey Winters says AI assistants answer questions without revealing the humans behind the knowledge, and that as blue links give way to one-size-fits-all answers, distinct perspectives get lost.
SuperMe's stated mechanic is to take a question, find relevant professionals, and have their AI profiles answer instantly based on their real work; Perspective Search shows several answers from people who have already solved the problem, with the option to go deeper by engaging individual profiles.
Winters presents this as how advice actually works in business: advice is multiple grounded perspectives, not a single synthesized one.
A $6.8M seed round led by Mike Duboe at Greylock is announced alongside the launch, with named product leaders, founders, growth leaders, operators, and investors involved.
Because this is an announcement, ask for usage and retention data.
The concrete design choice is Perspective Search itself: instead of collapsing an answer into one response, the product surfaces multiple answers from named professionals whose profiles are grounded in their own work, and lets the asker continue with a specific person.
For a newcomer, this is a useful starting point for Casey Winters’s approach to building from scratch: it explains how founders decide what to build, what to cut, and what the company needs to own.
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Winters contrasts two early product choices: how much feature surface area to build and how much design polish to invest in.
They give examples of companies in each quadrant, then explain the tradeoffs: more features can speed learning but create a larger pruning burden; more polish can strengthen positioning but slow learning.
The most practical section proposes reviewing experiments by asking whether a feature is a hit, a distraction, or a potential compounder as network density grows.
They connect that decision to the company’s long-term vision: what it needs to own, win, attract, and reject.
For brands planning a campaign
For a product-strategy talk or advisory session, ask Winters to lead a feature-review exercise using a real roadmap.
A useful outcome would be a shortlist of experiments to scale, defer, or stop, with the reasoning tied to the company’s intended advantage.
This example supports a session on zero-to-one execution, not just moving quickly.
This essay answers what Winters means by thinking AI-natively.
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Start here to see how they distinguish a step-change in a workflow from adding AI to an existing process.
Winters uses the video game Portal’s “thinking with portals” idea to describe using AI for tasks that were previously inaccessible, rather than making small improvements to familiar workflows.
They turn that distinction into three stages—“the old way,” “the half measure,” and “the one shot”—and illustrate it with data retrieval: writing SQL, asking ChatGPT to write SQL, and having Cursor write a Python script.
They apply the same lens to startups, distinguishing products that can progress toward a one-shot experience from half measures that may not lead to a durable position.
The essay acknowledges that predicting which tools will win is uncertain.
For brands planning a campaign
For an AI-native product session, have Winters map a company’s current workflow against the old-way, half-measure, and one-shot stages. The exercise could help a team decide whether to improve a transitional product or invest in a more complete experience.
This post gives a quick entry point into Winters’s marketplace thinking.
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It frames the strategic question as what happens when AI agents control discovery, transactions, or parts of supply.
Winters names three agent types—discovery, transaction, and supply—and says each can weaken traditional marketplace advantages.
They highlight a specific consequence: if agents own the user relationship, familiar customer-acquisition-cost payback assumptions may change.
The post points to a five-part survival playbook but does not include it.
For brands planning a campaign
For a marketplace strategy discussion, ask Winters to assess which agent type most threatens the company’s current customer relationship and moat, then identify what the business should own or integrate more tightly. Ask to review the detailed playbook before commissioning a session centered on its recommendations.
This announcement shows how Winters describes SuperMe’s product and its proposed response to a change in search.
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It is a useful starting point for understanding the business context behind their writing on AI and networks.
The post presents SuperMe as an AI-native professional network: users ask a question, the product finds relevant professionals, and AI profiles answer based on those professionals’ work.
Winters argues that AI answers can obscure the people behind knowledge, and positions Perspective Search as a way to surface multiple human perspectives and enable follow-up.
They also announce a $6.8 million seed round.
Since this is a launch announcement rather than independent evidence of performance, ask for performance data.
For brands planning a campaign
For a session on product positioning or network design, use the announcement to discuss how a company can make human expertise discoverable through AI while preserving distinct perspectives. Ask Winters to distinguish the product’s intended experience from what the team has observed in use.
Analytical approach: frameworks, named examples, and operating steps4
AI-native product thinking and the 'thinking with portals' frame1
Zero-to-one execution as a pruning problem1
How AI agents reshape marketplace economics and go-to-market1
SuperMe and Perspective Search: an AI-native network in practice1
AI-native product thinking and the 'thinking with portals' frame
Casey Winters argues that AI memos from tech CEOs carry a real strategic signal, but most teams still are not AI-native.
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The test is whether a product or workflow does something previously impossible, not whether it slightly improves an old process.
They lay out three tiers: the old way, the half measure (for example, using ChatGPT to write SQL), and the one shot (for example, having Cursor write Python scripts).
They warn against building half-measure products that do not sequence into a one-shot solution the company can own, calling them escalators to nowhere.
Examples include AI notetaking and AI prototyping as half measures, while customer support tools like Decagon, Sierra, and Fin and coding tools like Cursor and Windsurf are treated as potential one-shot products that become more agentic and reliable over time.
Uses a video-game metaphor (Portal) to make an abstract product-strategy distinction concrete, then ranks current product categories by how close they are to one-shot behavior.
Winters says the prediction could be wrong and that the AI trajectory has been humbling; the specific tool classifications are a snapshot, not a settled taxonomy.
For brands planning a campaign
Strong basis for a talk or advisory session on AI-native product strategy. A B2B SaaS team can use the three-tier model to decide which AI features are durable product bets and which are temporary workflow patches before committing roadmap and go-to-market resources.
In a post about building SuperMe with co-founder Ludo, Casey Winters says zero to one is not just about speed; it is about surface area and conviction.
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Founders make two early bets: how many features to build, and how much design or brand polish to invest in.
They present a matrix of companies that started with max or min features and max or min design, and argue each choice has trade-offs in learning speed and positioning.
For consumer networks, the variables are value prop, target audience, and network density.
The operating discipline is a formal experiment review: is a feature a hit, and if so, what gets removed to focus on it; if not, is it a distraction or a compounder; if it is a compounder, whether to iterate, let it sit, or defer until density is higher.
They summarize: zero to one is not about building fast, it is about pruning fast, and you cannot prune well if you do not know what you need to own.
Combines a two-axis founder matrix with an experiment-review process and four ownership questions adapted from a platform essay.
The framework is illustrated with well-known consumer and enterprise companies; it is not presented as a validated model with measured outcomes.
For brands planning a campaign
Useful for an advisory session on prioritization and product governance. A B2B SaaS leadership team can apply the experiment review to decide what to cut, what to double down on, and how to keep roadmap scope aligned with the company's long-term ownership position.
How AI agents reshape marketplace economics and go-to-market
In a LinkedIn post pointing to a longer essay, Casey Winters argues that AI agents are changing the rules for marketplaces.
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OpenAI's new agent and others will handle discovery, transactions, and even supply workflows.
They say this collapses the marketing funnel, rewrites CAC math, and threatens the network effects that made marketplaces valuable.
The post names three agent types: Discovery, Transaction, and Supply, and says each erodes traditional moats.
They also argue payback-period CAC is dead once agents own the user relationship, and outline a five-point Survival Playbook that includes going agentic, tightening supply integration, and rethinking monetization.
The warning: founders who adapt early can build stronger businesses than the last generation; those who wait risk becoming fulfillment pipes for someone else's AI.
Segments the threat by agent type, then links each to a business metric or moat and a survival action.
Ask for the full essay to assess the five-point playbook in detail.
For brands planning a campaign
Strong fit for a talk on AI agents in marketplace and platform go-to-market. A B2B SaaS company selling into marketplace or network businesses can use the agent-type breakdown to pressure-test CAC assumptions, defensibility, and product integration strategy.
SuperMe and Perspective Search: an AI-native network in practice
Casey Winters uses the SuperMe 1.0 launch to show an AI-native product thesis in market.
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SuperMe works by asking a question, finding the right professionals, and having their AI profiles answer instantly grounded in their real work.
The launch also introduces Perspective Search, which shows multiple answers from people who have solved the problem before, so users can go deeper with individual profiles.
They frame this as a response to search gaps: AI assistants answer without revealing the humans behind the knowledge, and perspectives get lost as blue links become one-size-fits-all answers.
The post also announces a $6.8M seed round led by Mike Duboe at Greylock, with named product leaders, founders, growth leaders, operators, and investors involved.
Uses the product launch itself as the argument, pairing a search critique with a concrete network mechanic and a public list of backers and operators.
Ask for independent usage data and retention metrics for SuperMe or Perspective Search.
For brands planning a campaign
Useful as a case study for a B2B SaaS audience evaluating AI-native product design, search, and network effects. It gives a real example of how one founder translates the AI-native thesis into product mechanics, positioning, and early go-to-market.
Analytical approach: frameworks, named examples, and operating steps
Across the four posts, Casey Winters tends to start with a strategic question, build a small framework or set of tiers, and populate it with named companies and products.
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Examples include the Portal metaphor and three AI-work tiers, the founder surface-area matrix, the three agent types, and SuperMe's question-to-profile mechanic.
The writing usually ends with an operating instruction or next step: avoid half measures that cannot become one shots, run formal experiment reviews, adapt before becoming a fulfillment pipe, or inspect how AI profiles answer from real work.
This makes the material easy to translate into a talk outline or advisory workshop, though the posts vary between essay analysis and product announcement.
For brands planning a campaign
For a B2B SaaS company, this approach supports a practical talk or working session rather than a broad inspirational keynote. The frameworks can be used as structured decision tools for product strategy, AI feature prioritization, and go-to-market planning.
Casey Winters writes about AI-native product strategy, zero-to-one execution, and how AI agents change marketplace economics.
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Two newsletter essays offer the clearest entry point: one argues that AI-native work should do what was previously impossible rather than speed up an old process; the other treats zero-to-one building as a pruning problem.
LinkedIn posts extend the same thinking to marketplace customer acquisition and SuperMe's Perspective Search.
Read the essays for the frameworks, then the shorter posts for how those ideas show up in products and market shifts.
For adjacent perspectives, compare them with Robert Nowell on applied AI in ecommerce email, Jack Maguire on paid social and AI-assisted retail measurement, and Juozas Kaziukėnas on AI agents in ecommerce marketplaces.
AI-native product strategyZero-to-one executionMarketplace strategyProduct frameworksGrowth and customer acquisition costNewsletter essaysLinkedIn postsB2B SaaS product leadersFounders and operators
Robert Nowell
Winters and Nowell both write about applying AI to commercial workflows, using concrete operating examples rather than abstract commentary.
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Nowell tests how AI can change email campaign creation and testing; Winters examines its effects on products, marketplaces, and company-building choices.
Nowell works in ecommerce email marketing, writing about Klaviyo and Shopify programs, AI-generated campaign design, and testing structures. Winters focuses on product strategy, AI-native product thinking, zero-to-one execution, and marketplace economics.
Both analyze how AI agents affect ecommerce and marketplaces, reasoning from platform behavior and incentives rather than generic AI commentary.
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They treat agent adoption as a practical strategic shift for brands and platforms.
Kaziukėnas focuses on retail marketplace supply, agent shopping behavior, and platform partnerships such as ChatGPT with Walmart, Etsy, and Shopify. Winters focuses on product strategy, marketplace moats, and zero-to-one execution.
For a B2B SaaS product-strategy and go-to-market campaign, Casey Winters’s posts target product, growth, and founder-level readers rather than a broad consumer audience.
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They have 32,487 LinkedIn followers, observed on October 7, 2026.
The four posts here are long-form product-strategy essays and short LinkedIn summaries covering marketplaces, AI-native product design, zero-to-one execution, and the SuperMe launch.
The SuperMe 1.0 post names the product leaders, founders, growth executives, and investors involved with and backing the company, illustrating the professional world Winters writes in and with.
Before committing budget, ask for newsletter subscriber numbers, performance data on comparable strategy essays, and any first-party audience survey covering reader roles, company size, and seniority.
No partnership formats are documented in Casey Winters’s posts.
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Casey’s posts discuss product strategy, AI-native product thinking, marketplaces, and SuperMe, but they do not state sponsorship packages, rates, deliverables, review requirements, deadlines, or a partnership inquiry route.
For a buyer, there is no verified basis in these posts to plan a paid collaboration with Casey.
This is an absence of documented formats in the posts here, not a finding that Casey never accepts partnerships.
Actionable gap: current official sponsor policies, specifications, disclosed examples, and inquiry routes are not available in the posts here and should be collected before a campaign plan relies on them.
A B2B SaaS company seeking a talk or advisory session on AI-native product thinking and zero-to-one execution is a strong match for Winters's posts.
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They work through the decisions a company needs to make: which AI features are durable product bets rather than temporary fixes; how to tell whether a half measure can sequence into a one-shot product; how to run an experiment review that decides what to scale, defer, or stop; and how AI agents shift acquisition math and defensibility in marketplace and network businesses.
Two essays and a launch post show the frameworks applied to named companies and tools, making them usable as working-session material rather than just a keynote theme.
The posts support a fee-based talk or advisory engagement, not a sponsored placement.
Fees, availability, and session scope need to be confirmed directly.
For reach planning, the available audience figure is 32,487 LinkedIn followers, observed October 7, 2026; ask for a newsletter subscriber figure.
When to choose a specialist
A consumer packaged-goods brand seeking a sponsored short-form video series promoting a household product to general shoppers is a poor fit based on Winters's posts.
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They publish long-form product-strategy essays and LinkedIn posts for product, growth, and founder readers; the posts show no video, short-form, or consumer-facing work.
The only measured audience figure is 32,487 LinkedIn followers, with no consumer reach, view counts, or engagement data.
The posts do not document rates, deliverables, usage terms, disclosure practice, review timelines, or an inquiry route, so a sponsored deliverable cannot be planned or priced from them.
The subject matter centers on B2B software, marketplaces, and networks, making a household-goods hook an unverified stretch rather than a demonstrated strength.
A worked campaign example
A mid-market B2B SaaS company wants to sharpen next year's AI roadmap and go-to-market before committing budget.
Engagement: one 90-minute working session plus a short written follow-up.
Client pre-reads: the two essays, 'Zero to One Is About Pruning Fast' and 'Now You're Thinking with Portals'.
Agenda: First, they walk the team's live roadmap through the three tiers from the portals essay: the old way, the half measure, and the one shot.
Items that only improve an existing workflow get labelled as half measures, then tested for whether they sequence into something the company can own; half measures that do not are candidates to cut.
Second, they run the experiment review from the pruning essay on three to five live features: is it a hit, and if so, what gets removed to focus on it?
If not, is it a distraction to kill or a compounder to iterate, park, or defer until density rises?
Third, they close with the ownership questions named in the same essay: what the company needs to own, compete to win, attract, and reject.
Output: the client's roadmap returned with each feature marked scale, defer, or stop and the reason recorded, plus one named feature the team agrees to stop working on.
Why this angle works: both essays are available to read, the exercise uses the client's real roadmap rather than a generic case, and the deliverable is a decision artifact the client can act on.
Before selling the session, obtain the fuller AI-agents essay behind the LinkedIn post before including a segment on agent-era marketplace economics, and confirm the fee, availability, session length, and recording rights directly rather than assuming them.
Robert Nowell is the better choice when the need shifts from general B2B SaaS product strategy to ecommerce email and lifecycle marketing—specifically, when a company wants to test and automate campaign creation inside Klaviyo, Shopify, or Mailchimp, or build a self-optimizing email program.
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Nowell's posts lay out a TikTok-style testing ladder for email: make ten campaigns, send them to 0.5% of the audience, promote winners to 2% and 4%, then send the winner to the remaining 80%. A second post describes Kopi AI generating a one-shot branded email for ARMRA, then proposes an autonomous system that creates, schedules, A/B tests, and promotes winning campaigns into welcome flows.
The tradeoff is scope.
Nowell's posts focus on email marketing and Shopify-store growth, not AI-native product strategy or zero-to-one roadmap pruning.
A session built on these posts would focus on lifecycle marketing and campaign automation, rather than the product bets, feature pruning, or ownership questions Casey Winters's essays address.
Jack Maguire is the better choice when the need is paid social, retail media, or in-store measurement rather than SaaS product strategy.
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Maguire's posts show a simulated Target campaign that connects AI-assisted shopping to local inventory, reservations, and pickup, with a $32M budget, 16 campaign weeks, 24 DMA clusters, geographic holdouts, and incremental contribution as the primary KPI.
The same posts quote Tobi Lütke on environment, autonomy, and craft.
For a company testing whether paid social drives incremental store visits or how an AI assistant can move a customer from ad to pickup, Maguire's posts are closer to the buyer task.
Maguire's posts focus on media incrementality and agentic commerce, not B2B SaaS product strategy or go-to-market advice for a SaaS roadmap.
They explicitly describe the Target campaign as a personal simulation, not Target disclosures.
Ask for separate examples if you need advice on AI-native product strategy or zero-to-one execution.
Juozas Kaziukėnas is the better choice when the need is ecommerce marketplace strategy and agentic shopping behavior rather than B2B SaaS product strategy.
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Kaziukėnas's posts analyze how AI engines choose products, including a finding that they avoid 'Sponsored' tags even when those results rank at the top.
They argue that ChatGPT has broad ecommerce supply through Etsy, Shopify, and Walmart, so brands should sell on those platforms rather than wait for an Amazon integration.
For a company deciding how to be discovered by AI shopping agents or how marketplace supply and demand will shift, Kaziukėnas's posts are closer to the buyer task.
Kaziukėnas's posts focus on marketplace and ecommerce platform analysis, not SaaS product management, feature pruning, or zero-to-one company building.
A session based on them would focus on agentic commerce and marketplace positioning.
Ask for separate examples if you need a product-roadmap or ownership decision framework.
Casey Winters's posts here include four posts: two newsletter essays on AI-native product thinking and zero-to-one execution, and two LinkedIn posts on AI agents in marketplaces and the SuperMe 1.0 launch.
The posts here do not state publication dates; the account was observed October 7-9, 2026.
The AI-agents post summarizes a longer essay and a five-point playbook, neither presented in full here, and one essay ends mid-sentence.
The posts here contain no video, transcript, sponsorship-rate, or speaking-fee information.