Ann Miura-Ko is most useful for expert-led sessions on how AI-native startups change product, engineering, and tooling choices.
Best for
B2B software brands seeking a narrow working session for product, engineering, and innovation leaders.
Not for
Paid LinkedIn sponsorship or technical buyer education for carbon-removal infrastructure campaigns.
Key takeaways
Across five AI-native companies, she found one full-time product manager.
Build speed creates a feature-factory risk; constraints protect product taste and coherence.
AI adoption is cheap, but organizational capability is not, she writes.
Check first: Confirm LinkedIn audience demographics and engagement for comparable AI-native organization posts.
Main ideas
Cheap AI adoption does not make an AI-native company · When engineers own the product, the product manager role can collapse · Build speed creates a feature-factory risk, and constraints protect taste
Ann Miura-Ko writes and convenes around how companies put AI to work.
Her clearest contribution in the reviewed posts is showing what changes inside AI-native startups: engineers take on more product decisions, non-engineers build tools for themselves, and teams need safeguards against turning faster execution into a stream of disconnected features.
She also writes about carbon removal and takes part in broader conversations about AI and philanthropy.
The supplied material does not establish whether a book, show, or earlier body of work is central to why people search her name; that part of her public profile needs targeted research.
Miura-Ko connects organizational claims about AI to concrete examples.
In a report on visits to five San Francisco AI-native companies, she describes finding one full-time product manager across the five, then explains the strategic risk she calls the “feature factory problem”: teams can build customer requests so quickly that the product loses coherence.
She gives contrasting responses, from restricting agents to configuring existing features to routing decisions through a person or a team using north-star metrics.
Her short claim that AI adoption is cheap but organizational capability is not distills the same argument: the important change is how ideas spread and experience compounds, not simply adding agents.
These posts make her work useful to readers trying to understand organizational change, while her carbon-removal post and philanthropy conversation show that her public interests are not limited to software operations.
Ann Miura-Ko's ideas center on what changes when AI makes execution cheap.
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The scarce resource becomes organizational capability and taste: how teams decide what to build, who owns product decisions, and how quickly they learn. The reviewed posts also show priorities in practical AI adoption outside engineering, large-scale carbon removal, and early philanthropy.
Cheap AI adoption does not make an AI-native company
Ann Miura-Ko argues that buying AI tools is easy while changing how an organization works is hard.
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The frontier is not more agents but a software factory where anyone can build, the best ideas spread, experience accumulates, and the system starts noticing questions nobody thought to ask. The reasoning is that tool access is not the differentiator; the differentiator is whether an organization can spread good ideas and retain operating knowledge.
In a short post, Ann Miura-Ko contrasts cheap AI adoption with organizational capability, then describes the software factory as the real frontier.
When engineers own the product, the product manager role can collapse
Ann Miura-Ko reports that AI-native companies are pushing product decisions into engineering and design.
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Engineers talk to customers daily and own decisions end to end, so the PM role is absorbed rather than augmented. The coordination stack matters less than the organizational shift: Slack, GitHub, and Linear appear as layers for software agents, Claude Code and Codex are in use, and there is little loyalty to any single tool.
Across five AI-native companies visited in one day in San Francisco, Ann Miura-Ko found exactly one full-time product manager; one company had 40 employees.
Ann Miura-Ko presents the shift as unresolved, writing that whether the PM collapse is a feature or a bug of the new era is still being decided.
Build speed creates a feature-factory risk, and constraints protect taste
When teams can build almost anything a customer asks for in a day, founders name building everything as a strategic risk.
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The product can become a Frankenstein assembled from requests rather than a coherent vision.
Ann Miura-Ko documents three kinds of constraint: limiting what agents can change, concentrating decision rights, or using squads and north-star metrics to judge agent-generated choices.
The argument is that when execution becomes cheap, taste becomes the moat.
One company let agents configure existing features only through JSON and blocked them from writing new application code; another routed product decisions through one person; another used squads of engineers, designers, and product leaders with north-star metrics.
AI moves the bottleneck away from engineering access
Ann Miura-Ko argues that AI gives leverage to people outside engineering who were previously blocked by engineering priorities.
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The same shift compresses experimentation: teams run more tests in parallel, simulate customers earlier, and cover unknowns faster so knowledge compounds.
An enterprise account manager had asked product for months to automate account uploads, then asked an AI agent in Slack and had it done in an hour. A chief of staff produced marketing materials in under 30 minutes, a designer generated competing iterations across tabs in under six minutes, and some companies ran hundreds of research interviews in a week instead of 50 in a quarter.
Practical AI adoption outside engineering is a convening priority
Ann Miura-Ko's public convening emphasizes operating questions rather than model benchmarks: how a regulated company uses AI internally, and what an applied reinforcement-learning environment actually looks like.
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The focus is on internal adoption beyond engineering teams.
Ann Miura-Ko promoted a session on how top companies use AI internally outside engineering, naming Headway's use of AI in a regulated industry and Mercor's RL environment as topics.
Carbon removal has to be considered at large scale
Ann Miura-Ko frames carbon removal against non-linear growth in energy use and the need for an answer at scale.
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The post is an endorsement of an industrial project rather than a technical explanation, so it shows a priority and a scale argument more than a detailed climate position.
Ann Miura-Ko highlighted the Terradot and Google announcement of the largest enhanced rock weathering project ever built, praising the team's hard work, dedication, and grit.
Philanthropy should become a habit early in a career
Ann Miura-Ko's stated priority is about timing: giving should become a habit when someone starts out, not a reward deferred until after success.
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The reviewed post is a short conversational note, so it shows the priority but not a detailed program.
In a podcast conversation, Ann Miura-Ko said it is important to develop a habit of philanthropy when starting a career rather than after making it; the same conversation covered the impact of AI and childhood.
This is the most concrete starting point for understanding how Ann Miura-Ko describes AI-native companies changing product and engineering work.
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It answers the question: what actually changes when AI tools are native to the company rather than added on?
Ann Miura-Ko reports visiting five AI-native companies in San Francisco in a single day and finding exactly one full-time product manager across them; one of the companies had 40 employees.
Engineers were talking to customers daily and owning product decisions end to end.
Founders named the 'feature factory problem' as a strategic risk: when building is cheap, teams can end up assembling a Frankenstein from customer requests.
The post lists specific constraints companies used instead: one limited agents to configuring existing features through JSON so they could not write new application code; another routed product decisions through one person; another used squads of engineers, designers, and product leaders with north-star metrics.
It also describes non-engineers gaining leverage: an enterprise account manager got a long-requested account-upload automation done in an hour after asking an AI agent in Slack, a chief of staff produced marketing materials in under 30 minutes, and a designer generated competing iterations across separate tabs in under six minutes.
For brands planning a campaign
For the B2B software brief, this post supplies a ready-made talk or advisory outline: role collapse, guardrails against feature sprawl, coordination tools, and the operational gains when non-engineering teams can build. Use the named examples to make the discussion concrete rather than abstract.
This short post gives the thesis behind the longer field observations in one page.
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A newcomer can use it to understand what Ann Miura-Ko thinks the real constraint is.
The post argues that AI adoption is cheap but organizational capability is not. It says the frontier is not 'more agents' but a software factory where anyone can build, the best ideas propagate, experience compounds, and the machine starts noticing what nobody thought to ask.
For brands planning a campaign
This is a compact framing device for a session or advisory conversation: shift the buyer's attention from tool count to how ideas spread, how experience accumulates, and how an organization learns. It pairs naturally with the five-company field report.
This shows the practitioner questions Ann Miura-Ko is convening: AI in regulated industries and what an RL environment looks like.
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It answers what current convening looks like and who is being platformed.
Ann Miura-Ko promotes an event on how top companies use AI internally outside engineering, naming Headway's use of AI in regulated industries and Mercor's RL environment as topics. The post points readers to a Luma event page.
For brands planning a campaign
Suggests an event-format opportunity and a practitioner audience for AI adoption beyond engineering. Because the post is an invitation, use it to identify themes and possible collaborators, not as evidence of the event's content.
This shows a different side of Ann Miura-Ko's public work: support for large-scale carbon removal and a concise argument about why scale matters.
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It answers whether the portfolio of interests extends beyond AI operations.
Ann Miura-Ko describes Terradot and Google announcing the largest enhanced rock weathering project ever built.
The post frames the project against non-linear growth in energy use and the need for an answer to carbon removal at scale.
It praises the team's hard work, dedication, and grit for doing something that matters.
For brands planning a campaign
Useful if a campaign needs a climate or industrial-decarbonization angle, or wants evidence that Ann Miura-Ko's public interests extend beyond software operations. It is not a detailed technical case study, so pair it with project documentation before making climate claims.
This is a lighter entry point that shows the range of Ann Miura-Ko's public conversations and a stated priority: building philanthropic habits early in a career.
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It answers what kinds of broader conversations Ann Miura-Ko takes part in.
Ann Miura-Ko describes a fun conversation with Connor that covered the impact of AI, what Ann Miura-Ko was like at age two, and why it is important to develop a habit of philanthropy when starting a career rather than after making it.
For brands planning a campaign
Relevant for campaigns about philanthropy, career development, or founder values. It is less useful for the B2B software organizational-design brief than the AI-native company posts, so treat it as a secondary example of range.
Ann Miura-Ko pays particular attention to AI use in non-engineering roles.
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She describes an enterprise account manager who had asked product for months to automate account uploads, then asked an AI agent in Slack and had it done in an hour, and a chief of staff producing marketing materials in under 30 minutes. She also convenes a session on AI inside a regulated industry and on what an RL environment looks like in practice.
First-hand field observation, with named roles and specific time savings rather than general claims about productivity.
For brands planning a campaign
A good fit for a talk or workshop on deploying AI in sales, marketing, and operations, where the useful content is concrete examples and adoption barriers rather than model benchmarks.
AI-native teams and the disappearing product manager
Across five AI-native companies visited in a single day, Ann Miura-Ko found exactly one full-time product manager, including at a company with 40 employees.
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Engineers talk to customers daily and own product decisions end to end; the PM role is being absorbed into engineering and design. Slack, GitHub, and Linear show up as coordination layers for software agents, with Claude Code and Codex in use and little loyalty to any one tool.
Comparative observation across several companies visited on the same day, reported as patterns rather than single anecdotes.
For brands planning a campaign
A strong angle for an advisory session on organizational design: which roles stay, which collapse, and how decision rights change when engineers own the product.
Ann Miura-Ko reports that when teams can build almost anything a customer asks for in a day, founders name building everything as their biggest strategic risk.
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The result is a product assembled from requests rather than a coherent vision.
She documents different answers: one company limits its agents to configuring existing features through JSON and blocks new application code, another routes all product decisions through one person, and another uses squads with north-star metrics to judge agent-generated choices.
Her summary is that when execution becomes cheap, taste becomes the moat.
For brands planning a campaign
Useful for a product-strategy briefing on guardrails, decision rights, and how to keep a roadmap coherent when build costs fall.
Ann Miura-Ko describes companies iterating three to five times faster, either by compressing a single experiment or by running many at once.
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A designer generates competing iterations across separate tabs in under six minutes.
Some companies simulate customers before real ones ever touch the product; others run hundreds of research interviews in a week instead of 50 in a quarter.
She frames the effect as covering the surface area of unknowns faster so knowledge compounds.
Field interviews paired with operational numbers, used to compare speed rather than to recommend a single tool.
For brands planning a campaign
A practical session for product and research leaders on where AI shortens experiment cycles and where simulated customers still need to be checked against real ones.
Ann Miura-Ko draws a line between buying AI tools and changing how a company works: adoption is cheap, organizational capability is not.
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In her framing, the frontier is not more agents but a software factory where anyone can build, the best ideas propagate, experience compounds, and the system starts noticing what nobody thought to ask.
Short, aphoristic posts that state the argument directly and leave the operating detail to the longer field observation piece.
For brands planning a campaign
Suits an assessment or workshop on capability building, not a tool-selection pitch; the buyer is someone responsible for how work gets done after the pilot.
Ann Miura-Ko highlights the Terradot and Google announcement of the largest enhanced rock weathering project ever built, and expresses pride in the team's work, dedication, and grit.
Read more
The post links her interest in large-scale climate infrastructure to the people doing the work rather than to a technical explanation.
This is a single post and a team endorsement; it shows interest in carbon removal but not a detailed technical position.
For brands planning a campaign
Could support a climate-and-technology conversation or an event connecting software audiences with carbon-removal infrastructure, as long as it stays away from claims she has not made.
In a podcast conversation, Ann Miura-Ko talks about the impact of AI, her childhood, and the importance of developing a habit of philanthropy early in a career rather than waiting until after making it.
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A single conversational post with little detail about the giving approach; treat the topic as a stated priority rather than a program.
For brands planning a campaign
An option for a panel or interview on early giving and long-term civic commitments, best kept separate from the AI and organizational-design material.
The retained posts do not document an explicit sponsor, client, employer, owned business, affiliate, investment, or disclosed partnership.
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The material company references are to Terradot and Google in a post about a carbon-removal project; Ann Miura-Ko praised the team but did not state a role or commercial relationship with either company.
Terradot
Mention
Ann Miura-Ko wrote that Terradot and Google were announcing the largest enhanced rock weathering project ever built and said she was proud of the team.
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The post does not state whether Ann Miura-Ko is an investor, adviser, employee, or otherwise connected to Terradot.
Ann Miura-Ko's posts are best explored as a short sequence: start with the field report on five AI-native companies, then the short argument that AI adoption is cheap but organizational capability is not.
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From there, the event post and the climate and philanthropy notes show the wider set of priorities.
The tags point to AI-native organizations, organizational design, product management, AI adoption, software agents, carbon removal, and philanthropy.
For adjacent reading, Brian Dils is closest on how AI changes product and design work, Max Thilén on operating playbooks for AI-native teams, and Andreas Moekesch on agent governance in enterprise software.
Both examine how cheaper AI execution changes product, design, and engineering work.
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Brian Dils writes about organizing Figma files for AI tools and about talent-led teams replacing product-led growth; Ann Miura-Ko reports on AI-native companies where engineers own product decisions and the product manager role collapses.
Brian Dils works from hands-on design-system and file-organization practice, while Ann Miura-Ko works from cross-company field observation and organizational strategy.
Both focus on what an AI-native team actually does differently, not just which tools it buys.
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Max Thilén lays out operating practices for an AI-native marketing team, including self-improving loops, orchestration, and quality checks; Ann Miura-Ko argues that organizational capability, not AI adoption, is the scarce resource.
Max Thilén gives a function-level marketing playbook, while Ann Miura-Ko looks at product and engineering organization design across companies.
Both are concerned with how AI agents change how enterprises and software teams operate.
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Andreas Moekesch writes about agent governance, audit trails, and vendor consoles; Ann Miura-Ko writes about agents as part of a software factory and about coordination layers such as Slack, GitHub, and Linear.
Andreas Moekesch concentrates on enterprise governance and procurement questions across vendors, while Ann Miura-Ko concentrates on startup organization, product decision rights, and experimentation speed.
Ann Miura-Ko is a plausible topic match for a LinkedIn placement aimed at B2B software leaders thinking about AI-native organization design, but the available evidence does not yet show that the audience contains those buyers.
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The posts give a clear content signal: AI adoption inside companies, the collapse of product-management work into engineering and design at AI-native startups, the “feature factory” risk when building becomes cheap, and the coordination tools teams use.
That aligns with the brief.
The measured audience evidence is limited to a LinkedIn follower count of roughly 11,000, which gives a reach figure, not a composition profile.
It does not establish followers’ job function, seniority, industry, company size, or geography.
Before placing a sponsored post or promoting a talk to product, engineering, and innovation leaders, request recent LinkedIn audience demographics and engagement results for two or three comparable posts on AI-native organization and feature strategy.
If LinkedIn demographics are unavailable, ask for an audience survey recording job function and seniority.
No sponsorship, paid-partnership, or brand-collaboration offer is documented in the material reviewed for Ann Miura-Ko, so this section records an absence rather than a rate card or a format menu.
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What the posts do show is someone who convenes and promotes peer-learning sessions: one post advertises an upcoming event on how companies such as Headway and Mercor apply AI outside engineering, with a public event-registration link.
That is event organizing, not a published partnership product, and it should not be read as a media kit.
The posts also regularly name companies and projects positively, as in a post congratulating the Terradot team on a large enhanced-weathering carbon-removal project with Google.
Such mentions are evidence of interest and endorsement, not evidence of payment, and nothing indicates which, if any, of these relationships were commercial.
For planning purposes, treat this creator as someone a brand would approach with a point of view to discuss rather than a defined inventory to buy.
A sponsor policy, disclosure practice, pricing, deliverables, exclusivity terms, and an inquiry route remain unverified and would need to be confirmed directly before any budget is committed.
Brief: a B2B software company wants a working session or advisory conversation for product, engineering, and innovation leaders on how AI-native startups are changing roles, feature strategy, and tooling choices.
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Fit decision: yes, with a narrow, expert-led format rather than a broad paid-media buy.
The strongest evidence is the field report from five AI-native companies visited in one day.
Ann Miura-Ko found exactly one full-time product manager across those companies, including one with 40 employees, and describes engineers talking to customers daily and owning product decisions end to end.
That is direct material for a discussion about which product-management work stays separate, which decision rights move into engineering and design, and what a company should deliberately keep.
The same post gives a usable feature-strategy frame: founders named the feature factory as a strategic risk when building becomes cheap, and the companies used different constraints—agents limited to configuring existing features through JSON, all product decisions routed through one person, or squads with north-star metrics evaluating agent-generated choices.
The line that execution becomes cheap and taste becomes the moat is a clear session thesis.
The tooling material is useful but should be presented as observed practice, not a product recommendation.
Slack, GitHub, and Linear appear as coordination layers for software agents; engineers use Claude Code and sometimes Codex; loyalty to any one tool is low.
The non-engineering examples make the session practical for a wider leadership audience: an account manager got a long-requested automation done in an hour through an AI agent in Slack, a chief of staff produced marketing materials in under 30 minutes, and a designer generated competing iterations in under six minutes.
The audience evidence is thin for paid reach: the available measure is about 11,000 LinkedIn followers, with no job-function, seniority, industry, or company-size data.
A good campaign would use Ann Miura-Ko as the expert voice in an invited session or advisory engagement and request LinkedIn audience demographics and engagement figures before adding paid promotion.
No sponsorship menu or inquiry route is documented, so the commercial shape should be a co-created session, workshop, or advisory conversation, not a standard media buy.
When to choose a specialist
Brief: a carbon-removal infrastructure company wants a paid LinkedIn sponsorship to educate technical buyers, promote an enhanced rock weathering project, and generate qualified pipeline.
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Fit decision: poor for paid sponsorship and technical buyer education.
The reviewed material contains one post praising the Terradot and Google announcement of a large enhanced rock weathering project and expressing pride in the team.
That shows interest in large-scale carbon removal, but it does not show a technical explanation of the method, cost, measurement, durability, or project performance.
There is no evidence in the reviewed posts of Ann Miura-Ko advising carbon-removal buyers or producing technical infrastructure content.
The audience evidence is also too thin for a paid pipeline campaign: one follower count of roughly 11,000, with no breakdown by industry, seniority, or buying role.
A climate-infrastructure sponsor would be buying an unverified audience against a topic where the creator has shown endorsement, not demonstrated technical depth.
There is no documented sponsorship offer, rate card, disclosure policy, exclusivity term, or inquiry route.
A single positive project post is not enough to support a paid technical campaign.
If the goal is a brief expression of support or a climate-and-technology conversation, that is a different and much smaller ask; it should not be sold as a technical buyer-education campaign.
A worked campaign example
Campaign angle for the B2B software brief: “When building is cheap, taste is the moat.” Use Ann Miura-Ko in a 45-minute talk plus 30-minute working session for product, engineering, and innovation leaders, with a follow-up advisory option.
Open with the field observation: five AI-native companies in one day, one full-time product manager, and a 40-person company where engineers own customer conversations and product decisions. Ask the room to name which product-management work would disappear in their organization and which decision rights they would refuse to move.
Move to feature strategy.
Explain the feature-factory risk: when a customer request can be built in a day, the product can become a pile of requests rather than a coherent vision.
Use the three observed constraints as the working material: limit agents to configuring existing features through JSON and block new application code; route product decisions through one accountable person; or use squads with north-star metrics to judge agent-generated choices.
Ask each table to choose one constraint to test for 30 days and name the signal that would show it is working.
Then cover tooling and coordination.
Describe Slack, GitHub, and Linear as observed coordination layers, Claude Code and Codex as tools in use, and low tool loyalty as a planning assumption.
Do not turn this into a product comparison; the point is that tool choice is less important than how ideas move and how experience compounds.
Close with non-engineering leverage.
Use the account manager who got an automation done in an hour, the chief of staff producing marketing materials in under 30 minutes, and the designer generating competing iterations in under six minutes.
Give the room a 30-day experiment: pick one non-engineering workflow to unblock, one feature constraint to enforce, and one decision-rights rule to test; define a baseline and a review date.
Hand off a one-page decision memo and an optional advisory session.
Because audience composition is not verified, run this as an invited session first and request LinkedIn demographics and engagement data before any paid promotion.
Choose Brian Dils when the session needs to move from org design into the practical tooling layer: how design files, design systems, and prototypes must be organized so AI coding tools can use them.
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The posts argue that AI is becoming a primary consumer of Figma files and give a concrete checklist—group components by domain, use sticker sheets with instances, remove hidden layers, apply semantic layer names, use variables for tokens, and add code syntax to variables.
They then extend that into a team argument: replace fixed roles with capabilities and domains when execution gets cheap.
That is a better fit if the buyer's real bottleneck is design-to-code readiness or restructuring a product team around capabilities rather than job titles.
The evidence is a design-system and team-structure argument, not field research across AI-native startups.
Brian Dils does not show the same direct observation of product-management role collapse or founder interviews that Ann Miura-Ko's field report does, and the material is concentrated on design and Figma workflows.
Ann Miura-Ko remains the stronger choice for a leadership session on product decision rights and feature strategy across engineering, design, and product.
Choose Max Thilén when the buyer wants an operating playbook for an AI-native marketing team rather than a product and engineering org diagnosis.
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The posts give a detailed list of practices—self-improving loops, one context layer over business communications, agent access to ad accounts and the CRM, skills instead of how-to docs, orchestration, agents proving their work, safety rules in code, task-based model choice, and human plus agentic quality assurance.
They also discuss buying AI seats before prices rise and building recurring automated jobs.
That is more useful if the session is for marketing operations and the question is how to run the function day to day.
The material is about marketing workflows and tool procurement, not startup product-management structure, feature-factory risk, or engineering decision rights.
It offers a strong implementation menu but less evidence on how AI-native companies organize product and engineering roles.
For the stated B2B software brief, Max Thilén is a narrower substitute: better for marketing operations, weaker for cross-functional product org design.
Choose Andreas Moekesch when the buyer's question is enterprise agent governance and the buying process for B2B software.
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One post describes taking down the demo wall with a free Vevos AI account and argues that buyers should test an ugly real process before procurement; the other compares SAP, ServiceNow, and GitLab agent-governance approaches and asks whether agent inventory and audit trails can be exported and seen across systems. That is a better fit for a session on enterprise adoption, governance ownership, and product-led trial design.
This is enterprise software go-to-market and governance experience, not a study of AI-native startup org charts.
It does not supply the direct observations of product-management absorption, feature-factory constraints, or tooling choices inside startups that the brief asks about.
Ann Miura-Ko is stronger for the startup organizational-design discussion; Andreas Moekesch is stronger if the buyer pivots to enterprise governance or demo-free product evaluation.
This profile draws on the full text of five LinkedIn posts by Ann Miura-Ko: a field report on visits to five AI-native companies, a short post on AI adoption and organizational capability, an event invitation about AI use outside engineering, a carbon-removal project announcement, and a note on a podcast conversation.
Dates were not supplied with the retained text.
Audience evidence is limited to a LinkedIn follower count of about 11,000, with no job-function, seniority, industry, or company-size breakdown.
The page also uses the accepted ideas, relationships, discovery, and campaign-fit sections.
No transcripts, event recaps, media kit, sponsor policy, or linked project documentation were available.
Targeted identity research is still needed to establish whether books, shows, owned businesses, or earlier career work are central to name searches.
Recent engagement
Across their last 4 LinkedIn posts, the median post got 20 reactions and 1 comments. Checked 2026-11-02.