Greg Coticchia writes and advises on product management, with a focus on connecting product decisions to customer problems and market needs.
In his posts, he challenges teams to look past executive opinions, feature requests, and impressive technology to ask who a product serves and what problem it solves.
Readers interested in product strategy, AI products, or innovation can follow his work for plain-language arguments illustrated with business examples and lessons from his own career.
Coticchia’s distinctive contribution in these posts is turning a broad product principle—start with the customer’s problem—into practical tests for decisions.
They ask teams to weigh who a feature helps, how many people it helps, how painful the problem is, and what the feature would displace; they also describe saying no to a major customer to protect strategy.
In another post, they explain why an impressive AI-agent demo may not translate into demand: capability is not the same as a value proposition.
The acquisition post adds a useful counterpoint, asking whether a buyer preserves the customers and purpose that made a startup valuable.
The posts favor clear arguments and memorable examples.
For a booking decision, ask for concrete acquisition examples and links to the longer articles, which aren’t available to readers here; the acquisition post names no deals.
Greg Coticchia’s writing returns to one argument: product decisions should start from a customer problem, not from what a company can build, what its most senior person wants, or what its experts already believe they know.
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Across four posts, they apply that test to executive feature requests, AI agents, acquiring an AI startup, and the value of entering an industry as an outsider.
The tone is deliberately plain: short declarative lines, a gating list of questions, and a closing question to the reader.
Each post points to a longer article rather than developing its cases in full.
A feature request is data, not a decision
Greg Coticchia describes a call where a CEO told a CTO to build a feature because it would make the product stickier—without data, customer input, or a clear problem to solve.
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The CEO’s opinion carried something data did not: authority.
Coticchia draws a hard line between letting customers design your solution and understanding the problem they’re trying to solve.
They treat executive intuition, a large customer’s demand, and a sales emergency as three forms of input that are each insufficient on their own.
Before deciding to build, they ask: Does it solve a meaningful problem, and for whom?
How many customers have it, and how painful is it?
What information exists?
Does it strengthen differentiation or advance strategy?
What won’t be built instead?
Saying yes to everything produces what they call Frankenstein: a collection of individually defensible features that collectively make no strategic sense.
Their priority is protecting strategy from the loudest voice in the room.
The graphical user interface.
Nobody submitted a feature request for a GUI, but it attacked real friction in how people interacted with computers — so the absence of a request is not proof that a problem is not worth solving.
Greg Coticchia also says they once told their company's largest customer no, because saying yes would have violated the strategy: "It wasn't fun.
Writing about AI agents, Greg Coticchia argues that the gap between Silicon Valley enthusiasm and ordinary users isn’t a technology problem but an old product management failure: technology in search of a problem.
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They describe a pattern they say they’ve watched across more than 100 product launches: engineering builds something impressive, the demo lands inside the building, everyone assumes it’s obvious, the product ships, and the market shrugs.
The problem isn’t bad technology; nobody started with a customer problem worth paying to solve.
Their rule: “It can” isn’t “I need to.”
Winning teams work backward from a real job, a real frustration, a moment when someone would pay to never do something again.
Sequence matters to them: problem first, customer first, capability in service of both.
The post opens with a WIRED piece reporting that Silicon Valley is building payment rails for agents, automating jobs with them, and racing to stop them hacking each other, while most people in the real world have never touched one. Greg Coticchia uses that mismatch as the setup and closes by asking readers which agent use case they would actually pay for.
Knowing too much can be why a company misses a market
Greg Coticchia argues that the companies that miss a market aren’t inexperienced; they know too much.
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Expertise stops them from asking the basic question that matters.
They list healthcare, legal technology, enterprise software, manufacturing, and cybersecurity as fields where they’ve watched this play out.
They frame their own lack of domain knowledge as a structural advantage, letting them see assumptions insiders could no longer notice.
Greg Coticchia credits Clayton Christensen with the McDonald’s milkshake story, rather than presenting it as their own: McDonald’s sells milkshakes to 70 million customers a day across 45,000 locations, started because of a milkshake machine, and still couldn’t work out how to sell more of them.
Coticchia adds two firsthand claims: they built an eBilling company that was sold to Thomson Reuters without a legal background or knowledge of how law firms bought software, and moved into data center product management from factory automation.
They say the move made them look like a genius because insiders’ assumptions were invisible to them.
On AI startup acquisitions, Greg Coticchia says the technology is the less interesting question; the customers are more interesting.
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The pattern they report from coaching CEOs through deals is that acquisitions struggle not because the AI wasn’t good enough, but because the acquiring company changes who the product is for—often exactly what made the startup valuable in the first place.
They frame the tradeoff as a question, not a settled answer: should a large acquirer preserve what made a startup unique, or is changing it simply the price of scaling?
They say they’ve observed both approaches and one works far better, without naming which in the post.
Their priority is to put customer definition and product identity on the table before locking in changes to scale.
The post names no specific deals, so treat it as a framework to test, not documented case evidence.
Greg Coticchia describes the failure: an acquiring company redefines the customer, and the original product identity is lost.
For details, ask for the longer article linked in the post.
Answers the first question any product team faces when a new request arrives: should we build this at all?
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It gives teams a working vocabulary for separating a customer problem from a customer-designed solution.
Opens with a call where a CEO told a CTO to build a feature because it would make the product stickier, despite having no data, customer evidence, or stated problem.
The CEO defended the request: 'If we spend our time understanding customers' current problems, we'll never innovate.' The counter is the difference between having customers design your solution and understanding the problem they are trying to solve.
The graphical user interface illustrates the point: nobody submitted a feature request for a GUI, but it removed real friction in how people used computers.
The post applies the same test to sales requests from large customers and describes telling one of the company's largest customers no because saying yes would have violated the strategy.
The named failure mode is 'Frankenstein': individually defensible features that collectively make no strategic sense.
For brands planning a campaign
A workshop module on intake and roadmap triage: take a live feature request from the client's backlog, rewrite it as a customer problem statement, then score it by whom it helps, how many people it helps, how painful the problem is, whether it strengthens differentiation or advances strategy, and what it displaces.
Explains in accessible terms why a capable AI product can ship successfully and still stall in the market.
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Starts with a WIRED piece describing agent payment rails, job automation, and agent-security races being built in Silicon Valley while most people in the real world have never touched an agent.
Coticchia calls this the classic 'build it and they will come' product management failure: technology in search of a problem.
They say the pattern has repeated across more than 100 product launches: an impressive capability demos well and ships, but the market shrugs—not because the tech is bad, but because nobody started with a customer problem worth paying to solve.
The post draws a line between 'capability is not a value proposition' and the distinction between 'It can' and 'I need to.' Teams win with agents, they argue, by starting with a real job, a real frustration, a moment when someone would pay to never do something again, and working backward to the technology.
The sequence matters: problem first, customer first, capability in service of both.
It closes by asking readers what agent use case they would actually pay for.
For brands planning a campaign
Use as the opening provocation in an AI product session: before showing any capability roadmap, ask the client's team to name the agent use case they would personally pay for, then map the roadmap against their answers.
Moves the acquisition conversation from model quality to customers and puts the preserve-versus-scale tradeoff in plain terms.
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Argues that companies ask the wrong question when they acquire an AI startup: everyone talks about the technology, but the customers matter more.
Drawing on coaching CEOs through acquisitions, Coticchia describes a pattern: deals that struggle usually fail not because the AI was not good enough, but because the acquiring company changes who the product is for—often the very thing that made the startup valuable.
The post asks whether a large acquirer should preserve what made a startup unique or change it as the price of scaling, and says both approaches have been observed, with one working far better.
Ask for named deal examples before treating the argument as documented case evidence.
For brands planning a campaign
Useful for post-merger or acquisition planning: have the acquiring team write down who the product is for before the deal and who it is for afterward, then separate deliberate changes to the customer base from drift.
The most personal of the four posts, and the clearest statement of why an outsider's perspective can be a product advantage rather than a weakness.
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Opens with McDonald's selling milkshakes to 70 million customers a day across 45,000 locations, notes that the company began with a milkshake machine, and asks why it still could not sell more of them.
The answer: it knew too much.
Coticchia connects this to Clayton Christensen's milkshake story and says the pattern recurs in healthcare, legal tech, enterprise software, manufacturing, and cybersecurity: companies that miss the market are not inexperienced; they have stopped asking the question that matters.
They add two firsthand examples: building an eBilling company sold to Thomson Reuters without legal expertise, a law background, or knowledge of how law firms bought software; and entering data center product management from a factory automation background, which made them look like a genius because insiders' assumptions were invisible.
The post frames that ignorance as a structural advantage.
Ask for the longer article to see the cases developed in full.
For brands planning a campaign
Use as a counterweight to insider expertise in discovery workshops: have the client's team list what it assumes it already knows about customers, then test those assumptions through direct observation or interviews before committing to a strategy.
A consistent form: story, counterintuitive claim, open question4
Customer problems before feature requests1
Capability is not a value proposition1
Outsider knowledge as a structural advantage1
What to preserve when a product changes hands1
Customer problems before feature requests
Greg Coticchia describes a call where a CEO ordered a CTO to build a feature because it would make the product stickier, without data, customer evidence, or a clear statement of the problem it solved.
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The post draws a firm line between asking customers to design your solution and understanding the problem they are trying to solve.
It uses the graphical user interface as an example: nobody requested a GUI, but it addressed real friction in how people used computers.
Feature requests, demands from large customers, and executive intuition all count as data, but none alone is strategy.
Saying yes to each one, the post argues, produces a product made up of individually defensible features with no strategic sense.
They note having told the company's largest customer no when saying yes would have broken the strategy.
Before a build decision, work through a set of gating questions: Does it solve a meaningful problem, and for whom?
How many customers have the problem, and how painful is it?
What evidence exists?
Does the feature strengthen differentiation or advance strategy?
What will not be built instead?
For brands planning a campaign
For a B2B SaaS team, this is live workshop material, not a general talk: bring real backlog items or executive requests and run them through the gating questions to separate evidence from authority. Position the engagement as a build-decision review that ends with a documented yes or no and a list of displaced work.
Writing about AI agents, Coticchia argues that the gap between Silicon Valley enthusiasm and ordinary users reflects an old product management failure, not a technology problem.
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Engineering builds something genuinely impressive, the demo lands well internally, and everyone assumes the value is obvious.
Then the market shrugs because nobody started with a customer problem worth paying to solve.
The post states plainly that “it can” is not “I need to.”
Teams that succeed with agents, they argue, will start with a real job or frustration and work backward to the technology, putting the problem and customer first and capability in service of both.
The post uses a published news piece as its setup, then connects the pattern to the author's claimed track record across more than 100 product launches. It closes with an open question asking readers which agent use case they would actually pay for.
For brands planning a campaign
Useful for a product team deciding whether to add AI or agent features. It supports a positioning or roadmap review that starts with paid customer jobs rather than model capability, and gives a brand a credible frame for sponsoring or co-hosting a session on AI product decisions.
Using Clayton Christensen's milkshake story at McDonald's, Coticchia argues that companies miss markets not because they lack experience, but because they know too much: expertise stops them from asking the basic question that matters.
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The post lists healthcare, legal technology, enterprise software, manufacturing, and cybersecurity as places where this plays out. They cite building an eBilling company sold to Thomson Reuters without a legal background, and moving into data center product management from factory automation, calling that ignorance a structural advantage.
The post pairs a widely known case study with personal career examples, grounding the argument in a familiar story and firsthand claims rather than data.
For brands planning a campaign
Relevant when a company is too close to its own category. It supports an outside-in review, customer interviews framed for non-experts, or an external perspective to unsettle assumptions in a stuck market.
On AI startup acquisitions, Coticchia says the technology is the less interesting question; the customers are more interesting.
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The pattern they describe is that struggling deals usually fail not because the AI was weak, but because the acquiring company changes who the product is for—often exactly what made the startup valuable in the first place. The post frames the tradeoff directly: preserve what made the company unique, or accept changing it as the price of scaling.
Coticchia says they have observed both approaches and that one works far better, but does not name it in the post, instead pointing readers to the linked article for details.
For brands planning a campaign
For a company integrating an acquisition, this frames an advisory conversation around customer definition and product identity before leaders lock in changes at scale. It also gives the buyer a concrete question to put to leadership.
A consistent form: story, counterintuitive claim, open question
Across the four posts, the structure repeats.
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Each opens with a concrete hook—a CEO on a call, McDonald's and the milkshake machine, or a WIRED piece on AI agents—then corrects a common assumption and states an alternative in short, declarative lines, often as a checklist or sequence.
Each ends by pointing to a longer article and, in some cases, asking the audience a direct question.
Hashtags identify topics such as product management, product strategy, and innovation.
For brands planning a campaign
The format is easy for a brand to co-produce: a short, story-led post names a familiar product mistake and points to a longer resource. That resource is a natural place for a workshop invitation, diagnostic, or report download.
Greg Coticchia's posts do not mention a sponsorship, affiliate arrangement, or client engagement.
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One concrete business connection appears: in a post about product leadership, they write that they built an eBilling company that was sold to Thomson Reuters.
Other brand references in the same posts—McDonald's as an illustrative case study and a WIRED article cited on AI agents—are examples in an argument, not commercial relationships.
Ask them to confirm whether any current sponsorship, affiliate arrangement, or client engagement exists.
Thomson Reuters
Mention
Coticchia says in a post on domain expertise and product leadership that they built an eBilling company and sold it to Thomson Reuters despite having no prior legal-industry background.
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The post describes a completed transaction, not an ongoing role, contract, or partnership; ask whether any current relationship exists.
Greg Coticchia's material is easiest to explore through a recurring test they apply to product decisions: Does this solve a meaningful problem, for whom, how painful is it, what supports the decision, what does it do for differentiation and strategy, and what will not get built instead?
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The post on executive- and sales-driven feature requests lays out that test most fully.
Posts on AI agents and acquiring an AI startup apply it to newer subjects, while the McDonald's milkshake post turns it into an argument for outsider knowledge.
The ideas span product strategy, customer-problem discovery, AI product development, and acquisitions.
Each post is a short LinkedIn piece pointing to a longer article, rather than a full development of its cases.
For readers looking to go further, the similar-creator list offers three directions: deeper AI product method, a legal-tech view of AI buying, and regional startup-ecosystem coverage, with the tradeoffs noted for each.
Product managementProduct strategyCustomer problem discoveryAI product developmentAI startup acquisitionsDomain expertise and outsider advantageB2B SaaSBuild-decision reviewsLinkedIn postsFramework and checklist postsProduct leadersFounders and executivesB2B SaaS product teams
Omar Chaudhry
Both write for product people about AI, and both argue that the shift from tools that assist to agents that execute is a business-model change, not a feature upgrade.
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Chaudhry's posts stay close to method, including synthetic discovery, a technique that uses AI personas to stress-test an idea before real users are involved.
He keeps returning to customer problems as the starting point for what gets built.
That method-level focus makes him the closest match to Greg Coticchia in ideas and teaching approach.
Chaudhry writes as a practitioner promoting a firm's hands-on operational work.
His heavily formatted, prescriptive posts end with a calendar link and service pitch; the payoff he describes is workflow redesign around autonomous agents.
Greg Coticchia's posts are shorter narrative pieces that correct a common assumption, make no pitch, and point to a longer article.
Their focus is decision discipline and strategy gating rather than removing people from workflows.
Both use short LinkedIn posts built around a concrete story to make a commercial argument about AI.
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Seader contrasts Spirit Airlines' lowest-fare identity with the $10 million sale of its internal data in bankruptcy to question what a low price really costs.
Greg Coticchia uses a CEO ordering a feature with no evidence to question what authority costs a roadmap.
Both ask readers to look past the surface claim to the customer consequence, and both write about AI in areas where buyers are making high-stakes choices.
Seader writes from inside an established legal-tech vendor, arguing for data control, continuity, and keeping customer work out of training sets.
The material is shaped by one product and one professional buyer group.
Coticchia writes across industries, including healthcare, legal, manufacturing, and cybersecurity; their subject is the product decision itself, rather than the trust and continuity case for a particular tool.
Both write frequent, short LinkedIn posts about getting new ventures and technology into real markets, and both name institutions and established players rather than speaking in the abstract.
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Ling's posts center on the Wasa Future Festival programme, the Vaasa startup ecosystem, and a session on Vaasa's connection to Silicon Valley; Greg Coticchia builds his around a specific executive conversation and a named case study. The overlap is topical adjacency, not a shared method.
Ling's material is largely event and ecosystem promotion from Finland and the Nordics, with panel lineups and sign-up links; it does not analyze product decisions or teach a repeatable selection method.
Readers looking for product-strategy methods or build-decision frameworks will find less here.
Those looking for Nordic startup and energy-transition events will find less in Greg Coticchia's posts.
The only measured audience number in the posts here is scale on one channel: Greg Coticchia's LinkedIn following of 12,382, recorded 8 October 2026.
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That tells a buyer how many people could see a post; it does not show who they are.
The posts here do not say anything about platform demographics, audience surveys, or other composition data, so geography, company size, job function, seniority, and industry mix are undetermined.
Content appeal is a separate question, and here the appeal is legible: the four posts here are written for people who own product decisions — product leaders, founders, CEOs, and the executives who fund or scope product work — and they argue about customer-problem discovery, AI product strategy, acquisitions, and resisting feature requests that lack evidence.
A B2B SaaS buyer assessing a product-strategy workshop should treat that as a sign of the topic and register the writing is aimed at, not as a sign that product leaders actually make up the following.
The practical next step for a B2B SaaS buyer assessing a product-strategy workshop is to ask for two things: recent results (views, comments, reshares, and any click or lead data) for posts on product strategy, customer discovery, or AI product decisions, so the buyer can compare like posts rather than the whole feed; and any audience survey or first-party list data that documents job function, seniority, and company size, which LinkedIn itself does not disclose here.
Until those arrive, the safe placement decision is that this is a plausible channel to reach product-minded senior readers, with audience fit unverified.
Greg Coticchia’s four LinkedIn posts cover product management practice: customer-problem discovery, AI product adoption, acquisitions, and domain expertise.
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They don’t describe sponsored content, paid collaborations, media-kit terms, rates, or a route for business inquiries.
The posts mention advising CEOs through acquisitions, teaching product management, building and selling an eBilling company to Thomson Reuters, and turning down a largest-customer feature request to protect strategy.
These are business credentials, not partnership offerings.
Before planning a brand campaign, ask for a media kit, sponsored-post history, or partnership contact.
A B2B SaaS company looking to strengthen product strategy and customer-problem discovery is a strong fit for Coticchia in a workshop, advisory, or co-produced teaching engagement.
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Four full posts show material already shaped like working sessions, not opinion pieces.
The feature-request post offers a gating sequence teams can apply to their backlog: Does the request solve a meaningful problem, for whom, and for how many customers?
How painful is it?
What evidence exists?
Does it strengthen differentiation or advance strategy?
What is not being built instead?
Coticchia also describes refusing a largest customer's request because approving it would have broken the strategy—the behaviour a buyer may want to instill in their own organisation.
The AI-agent post offers a second exercise: before showing a capability roadmap, ask the team which agent use case they would personally pay for, then test the roadmap against their answers.
The acquisition post offers a narrower exercise: write down who the product is for before a deal and who it is for after, separating deliberate customer changes from drift.
The McDonald's and milkshake post supports the discovery side of a workshop or advisory engagement, using outside observation to challenge assumptions clients may not see.
Before contracting, buyers should settle the delivery format.
No media kit, rate card, contact route, or documented engagement structure is described in the posts.
The fit rests on the substance of the thinking and a topic register aimed at people who own product decisions; confirm commercial terms directly.
When to choose a specialist
A campaign built on paid distribution is a poor fit based on Coticchia's posts.
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They offer no support for sponsored posts aimed at a specified B2B audience with impression or engagement guarantees and audience-targeting criteria.
The only measured audience figure is a LinkedIn following of 12,382 recorded 8 October 2026; there are no demographic data, audience surveys, engagement counts, view or click data, or lead figures.
Reach alone is not enough to plan a campaign.
The posts also contain no sponsored-content history, disclosure of paid collaboration, media-kit terms, rate structure, or business inquiry route for brand work.
The business experience described in passing, such as advising CEOs through acquisitions and teaching product management, is a credential, not a sponsorship package.
A campaign requiring verified case evidence or analyst-style reporting is also a poor fit.
The acquisition post names no specific deals and describes a pattern rather than documenting outcomes.
Coticchia states two career claims—an eBilling company sold to Thomson Reuters and a move from factory automation into data center product management—but the posts do not corroborate them.
Two longer articles are referenced but not described, so a campaign depending on that detail cannot be assessed.
A single-channel presence also limits multi-channel campaign plans.
A worked campaign example
Run the B2B SaaS product-strategy brief as a half-day working session, with a decision review rather than a talk.
Before the session, ask the client to send ten live items from its intake queue: executive requests, large-customer asks, sales escalations, and any AI or agent features already on the roadmap.
Open with a provocation drawn from Coticchia's AI-agent argument: each participant writes down one use case they would personally pay to stop doing, then the group compares those answers with the roadmap it has already funded.
The exercise is deliberately uncomfortable and quickly surfaces the gap between capability and value.
In the main block, run each of the ten real items through the gating questions, one at a time and in the client's own language: What problem does it solve, for whom, and for how many customers?
How painful is it?
What evidence exists?
Does it strengthen differentiation or advance strategy?
What work does it displace?
The output is a decision log showing which items earn a place and which do not, plus a written displaced-work list that makes the trade-off visible.
Close with intake discipline: how a big-customer demand or executive hunch gets recorded as data without becoming a requirement before it has earned a place.
The strongest material comes from the feature-request post, which names the failure mode as a collection of individually defensible features that together make no strategic sense.
Follow up with a one-page summary the client can circulate to sales and leadership, attaching the decision log and scheduling the next session to review deferred items.
Keep sponsorship and paid distribution out of scope.
This is advisory and teaching work.
Agree commercial terms, length, and format directly; no published format list or rate structure was available to review.
Choose Chaudhry when the need is specifically for agentic AI, not product-strategy discipline in general.
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Their posts draw a hard line between tools that assist and agents that execute: a tool waits for a prompt, click, or decision, while an agent detects a signal, decides, acts, and learns.
A tool reduces effort; an agent absorbs responsibility.
The closing argument is that the winner will have the fewest workflows still needing a human in the loop.
Both posts work through recommerce and logistics examples, including pricing inventory in real time, negotiating freight, and rerouting supply.
The second post offers a discovery method for a slow research cycle: simulate buyer personas with AI before spending on real users, then ask the model to attack the idea, identify where the economics fail, and surface edge cases.
A B2B SaaS team with an agentic roadmap and a discovery process that takes weeks may find this closer to its problem than a general feature-gating exercise.
The posts support a consulting offer.
Both close with a calendar link and a direct-message invitation, and their examples come from recommerce and logistics operations rather than B2B SaaS.
The framing also pushes to remove human dependency, presenting it as a headcount-to-revenue shift—an approach that may land badly at companies that want product teams to retain judgment.
The posts offer less on recording large-customer demands or executive hunches as data without turning them into requirements, or on rejecting a big customer to protect strategy.
Treat persona simulation as a pre-screen; ask how its findings will be validated with customers.
Choosing Chaudhry means choosing agentic ambition over the steadier decision discipline Coticchia teaches.
Choose Seader when a B2B SaaS company sells to legal, compliance, or professional-services buyers and needs to understand what they weigh when evaluating AI.
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Writing from inside a legal-tech vendor, Seader makes two buyer-facing arguments concrete: a low sticker price can hide the true cost, illustrated by Spirit Airlines' internal data sold in bankruptcy court to train AI models; and model control and continuity matter as much as capability, illustrated by firms building their own GPU clusters and lawyers writing their own tools.
For a team whose product decisions hinge on how in-house counsel think about data ownership and vendor lock-in, this is useful voice-of-customer material.
Like Coticchia, Seader argues from consequences rather than capabilities.
The posts serve the writer's employer's positioning: the Spirit story argues for the vendor's data policy, while the AI sovereignty piece argues for its own model training.
They offer no product-management method, so a workshop on roadmap and discovery discipline would need to come from elsewhere.
The focus is legal technology, making the transfer to a general B2B SaaS roadmap indirect.
Both posts are short and promotional, with less teaching depth than Coticchia's four posts.
Choosing Seader means getting a specialist read on legal buyers, not a product-strategy engagement.
Choose Ling when the task is to convene the surrounding ecosystem, not run an internal product-strategy workshop.
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Their posts are organizing notices: a Sunday Walk & Talk ahead of Wasa Future Festival, and the festival's startup programme, run with the University of Vaasa and the Vaasa startup ecosystem, including a session on the connection from Vaasa to Silicon Valley.
A brand seeking presence and relationships in a Nordic energy-transition and startup cluster, or wanting its name attached to a regional innovation event, fits this material well.
The posts show how a local ecosystem is brought together, who the organizers are, and what the programme looks like.
Coticchia's posts cover event logistics and programme announcements, not product strategy or customer-problem discovery.
They offer no method or worked example for the buyer task described here.
Where they offer a repeatable decision process a product team can apply to its backlog, Ling offers a room of people.
Choosing this alternative means shifting from advisory work to event participation or sponsorship—a different budget line and approval path.
Greg Coticchia's public posts cover four topics: executive and sales-driven feature requests and how to test them before building, why ordinary users have not adopted AI agents, what to preserve when a large company acquires an AI startup, and domain expertise as a reason companies miss markets.
The posts carry no publication dates, so nothing about chronology or how the ideas developed over time is claimed.
Each post points to a longer article; the arguments here are described only as far as the posts state them.
The post on acquisitions names no specific deals, and the two career claims in the post on expertise — an eBilling company sold to Thomson Reuters and a move into data center product management from factory automation — are stated by Greg Coticchia and are not independently confirmed here.
The posts show one completed business transaction and no sponsorship, client, or rate structure.
Tag choices and similar-creator comparisons are based on the post texts, not on follower counts or engagement data; the only audience figure used is a LinkedIn following of 12,382 recorded on 8 October 2026.
The three creators compared on this page (Omar Chaudhry, Eric Seader, and Lars Ling) are each represented by two full posts dated between March and August 2026.
Greg Coticchia's posts do not say whether they have signature books, shows, or businesses beyond the work described here, and that remains an open gap before publication.
