Gowtham Arivarasan is a product manager who writes about product discovery, AI, and go-to-market strategy. They say they lead product for heddl, a GTM platform for early-stage founders, and have shared work-in-progress on Ask Avi, a WhatsApp assistant for finding information in business files.
They are most useful for framing product and GTM decisions about AI, workflows, and handoffs.
Best for
This fits B2B SaaS content for product and GTM leads weighing AI roadmap changes.
Not for
This does not fit direct-response reach campaigns for WhatsApp commerce tools expecting audience conversion.
Key takeaways
AI makes handoffs faster, but teams should ask which handoffs should exist at all.
Product discovery should observe user workarounds and define the user journey before choosing AI.
Pitch what buyers cannot easily do themselves, not just the feature list.
Check first: Confirm audience demographics, engagement, and sponsored content results before contacting them.
Main ideas
AI makes handoffs faster — the harder question is which handoffs should exist at all · When anyone can build, the pitch is what buyers cannot easily do themselves · Publishing working notes from an early-stage GTM product
Gowtham Arivarasan is a product manager who writes about product discovery, AI, and go-to-market strategy.
They say they lead product at heddl, a GTM platform for early-stage founders, and have shared an early-access launch for Ask Avi, a WhatsApp assistant for finding information in business files.
Their posts offer useful perspectives on how product teams can understand real workflows before building, and how AI changes decisions about what work to automate—or remove.
In one of their clearest original arguments, they ask whether products should keep optimizing handoffs between people or reconsider whether some handoffs need to exist at all.
Other posts recap lessons from sessions by named speakers, presenting those ideas as takeaways rather than claims of original authorship.
Arivarasan connects practical product work to bigger questions about how work gets done.
In a handoff post, they use examples from lending, support, sales, and HR to explore what AI means for those processes.
In a product-discovery recap, they emphasize observing users’ workarounds and defining the user journey before choosing an AI capability.
Together, the posts show a recurring interest in finding the underlying job or friction rather than starting with a feature or technology.
Their work also spans GTM and enterprise selling.
A post recruiting early users for Ask Avi shows a hands-on, feedback-seeking approach.
An earlier post on product minimalism is more general and draws on familiar brand examples; it offers less detail than their first-person reflections on product decisions and work in progress.
Gowtham Arivarasan is a product manager who writes about product discovery, AI-era product decisions and go-to-market.
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Their posts come in two modes: first-person notes from their product work and practitioner sessions, and earlier explainer-style pieces.
One argument runs through them: when building gets cheap, the hard part shifts to choosing the right problem.
They keep asking who work should go to, what buyers cannot easily do themselves, and what teams should understand before building with AI.
AI makes handoffs faster — the harder question is which handoffs should exist at all
Across products in lending, CRM, lead management, collections, HR, hospitality and support, Arivarasan noticed one shared question: who is the next human this work should go to?
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Product managers got good at refining those handoffs with dashboards, workflows, notifications and service-level agreements.
Their argument: AI does not just speed handoffs up; it forces teams to ask whether some should exist at all, and which work deserves a human.
They call it a different, harder roadmap conversation and leave the question open to readers.
Arivarasan lists four chains: a loan application moving from sales to operations to risk, a support ticket moving from L1 to L2 to engineering, a lead moving from marketing to sales, and an appraisal moving from employee to manager to HR. The post asks whether others find the examples relatable or see them differently.
When anyone can build, the pitch is what buyers cannot easily do themselves
Across several posts, Arivarasan argues that “can be built” is no longer the bar.
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ChatGPT can scaffold an idea, a few APIs can stitch it together and an engineer can prototype it in a weekend.
What buyers still cannot easily handle is the compliance, regulation, manual effort, time and operational mess around it.
That gap, not the feature list, is the pitch.
The same logic shapes their approach to discovery: understand the problem before deciding what to build, define the user journey before deciding what the model should do, and explain both why something should exist and why it should not.
At a session with Manikantha S, head of product at Sarvam AI, Arivarasan heard how the team built for doctors.
Instead of starting with an app, they went into hospitals, observed workflows and spoke to users.
The useful insight was not “we need an app” but where the friction actually sat.
Arivarasan summarises the speaker’s lessons and adds their own takeaways.
Publishing working notes from an early-stage GTM product
Arivarasan says they lead product at heddl, a GTM platform built around how early-stage founders actually sell.
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They describe a messy problem: the ideal customer profile, messaging and channels change, and sometimes the whole go-to-market motion changes.
Most GTM software, they argue, was not designed for that.
They also say they have spent six years behind the product — researching, taking user calls, writing requirements, prototyping, shipping, breaking things and fixing them — across ecommerce, fintech, NBFC, SaaS and AI projects.
Rather than write “10 things every PM should know,” they plan to share notes from user calls, product decisions, failed experiments and arguments with the team.
In the same vein, they post about Ask Avi, a WhatsApp assistant that answers questions from files in Excel, PDF or Google Sheets.
It targets small business owners, D2C sellers, retailers and freelancers who repeatedly answer the same customer questions.
They are recruiting the first 25 early adopters, offering 30 days free and explicitly asking for criticism.
The Ask Avi post names the repeated questions it targets: “What's the price?”, “Is this available?” and “Can you send me the details?”
Arivarasan says it accepts Tanglish, Hinglish, Telugulish and Kannadlish.
They want people who already use a real spreadsheet so the product is built around actual workflows.
Turning practitioner talks into applied product lessons
Arivarasan attends practitioner sessions, writes up the takeaways, credits the speakers and hosts, and connects each lesson to their own work.
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The recaps mix direct quotations with their own framing.
Together, they show a stated priority: learn in public and put lessons into practice rather than collect generic advice.
The speakers supply the lessons; Arivarasan supplies the framing and application.
Arivarasan recaps Vineeta Hazra on selling to enterprise as an early-stage startup, Manikantha S on product at Sarvam AI, and Himanshu Agarwal on growing an Instagram account from zero to 50K.
In the enterprise post, they list the roles inside a deal — blocker, invisible influencer, economic buyer, champion, user and coach — and record an objection sequence of acknowledge, reframe, evidence, ask, noting that most people skip the listening.
They close by saying they are taking the enterprise lessons back to heddl's GTM motion.
From the Instagram growth session, Arivarasan takes away not the follower numbers but the number of format changes.
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Their argument: often the idea is fine and the presentation is the problem, so change the format and watch what people do.
They draw a direct parallel to product: teams spend time defending what they built instead of watching how people use it.
They close by favouring genuine content over automated AI-generated content as a way to build trust.
Arivarasan recaps the session's progression: a first static post got around 10 likes, a video with captions got 5,000 views, and a later video took the account from roughly 200 to 6,000 followers.
The speaker tried carousels, videos, different hooks and different ways of presenting the same kind of idea.
These figures come from Himanshu Agarwal's session.
It lays out four principles of minimalist product thinking — declutter the user experience, put features on a diet, pursue invisible design, and prioritise outcomes over outputs — and illustrates them with well-known companies.
It closes with a question to readers.
The same writer moves between instructive list-style posts and first-person notes from sessions and their own product work.
The illustrations are Google's homepage for simplicity, Dropbox starting as one folder syncing across devices, Apple's AirPods for invisible design, and Spotify's personalised playlists for outcomes over outputs.
Read alongside the session recaps and build-in-public posts, these pieces show a shift in style, not subject: from general principles illustrated by famous products to specific lessons from named practitioners and Arivarasan's own experiments.
This is Arivarasan’s argument, not a recap of someone else’s talk.
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It answers “what does this person actually think about product management?” Start here for an idea a campaign could build around, rather than a summary of their employer or tools.
Arivarasan lists experience across lending, CRM, lead management, collections, HR technology, hospitality and customer support.
Strip away the labels, they argue, and the products all answer one question: who should this work go to next?
Four examples make the point: a loan application moving from Sales to Operations to Risk, a support ticket from L1 to L2 to Engineering, a lead from Marketing to Sales, and an appraisal from Employee to Manager to HR.
Product managers became adept at refining those handoffs with dashboards, processes, notifications and service-level agreements.
But AI, Arivarasan argues, does more than speed them up: it prompts teams to ask whether some handoffs should exist at all.
The job is shifting toward deciding which work deserves a human.
They end by inviting others to weigh in, framing the idea as a question under discussion rather than a settled position.
For brands planning a campaign
For a product-led GTM brief, use this question as the opening hook: which human handoffs does the product automate today, and which could an AI-era roadmap remove entirely? Keep the invitation to debate; presenting it as a firm recommendation would overstate the post.
This answers “where does this person work and what problem are they building against?”
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It is the clearest statement of Arivarasan’s current role and product focus, and announces their plan to write about what they learn — a useful frame for the other posts.
Arivarasan describes six years behind the product — research, user calls, product requirement documents, prototypes, shipping, breaking things and fixing them — across ecommerce marketplaces, fintech, NBFC, SaaS, AI projects and now go-to-market.
They say they lead product at heddl, a GTM platform built specifically around how early-stage founders actually sell.
That problem is messy: the ideal customer profile, messaging and channels change, and sometimes the whole GTM motion does too.
Most GTM software, they argue, was not designed for that reality.
The post pairs a positioning statement with a publishing plan: Arivarasan will share notes from user calls, product decisions, failed experiments and arguments with the team, not “10 things every PM should know.”
For brands planning a campaign
Treat this as vendor-side context.
The post discloses Arivarasan’s tie to heddl, which matters when deciding whether an engagement is independent commentary or content tied to a product they sell.
Ask how much the resulting content should lean on heddl’s positioning.
This answers “how does Arivarasan process other people’s expertise, and what discovery method do they actually recommend?”
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It is the most specific piece on how to research a product problem before building, and it is a good sample of how carefully Arivarasan extracts and applies lessons from a talk.
A recap of a session with Manikantha S, head of product at Sarvam AI.
Arivarasan’s lessons are concrete: spend most of your time understanding the problem, because a well-built solution to the wrong problem is still the wrong product; talk to customers before deciding what to build.
One example follows a team building for doctors that visited hospitals and observed processes instead of starting with an app.
Watch what users do, not only what they say: workarounds, existing tools, WhatsApp groups and repeated manual steps can point to a stronger opportunity than a requested feature.
Define the user journey before building the AI, starting with what the user is trying to accomplish rather than what the model can do.
Then repeat a build, test, observe, reflect, build-again loop until product intuition develops.
The line that resonated most with Arivarasan: a product manager needs to know both why something should exist and why it should not.
The lessons are attributed to the speaker, with Arivarasan adding their own framing and takeaways.
For brands planning a campaign
A strong source for a campaign about discovery before building: user journey first, model capability second. For a campaign explaining AI product decisions, the post offers a sequence to walk readers through, with the doctor workflow as a worked example.
This answers “what does Arivarasan think actually wins enterprise deals?”
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It is their most detailed piece on selling and GTM mechanics, and shows how they translate a live session into planned changes at their own company.
A detailed recap of a session led by Vineeta Hazra on selling to enterprise as an early-stage startup.
Arivarasan records the framing question — everyone can build everything now, so what actually makes someone buy? — and the takeaway: pitch what the buyer cannot easily do themselves, including the compliance, regulation, manual effort, time and operational mess involved, rather than a feature list.
An enterprise purchase involves a blocker, an invisible influencer, an economic buyer, a champion, a user and a coach.
The person who loves the product often cannot sign, so the sharper question is who inside the ideal customer profile you are actually talking to.
On objections, Arivarasan records a four-step sequence: acknowledge, reframe, evidence, ask — and notes that most people skip the listening.
There is no single GTM playbook: Atlassian grew product-led, Palantir went deep with forward-deployed engineers, and Stripe made a hard problem simple for developers.
The closing point is knowing when to walk away: don’t be trapped by a logo, know your runway and your number, and sometimes the strongest move is a pause.
Arivarasan says they are taking the learnings back to heddl’s GTM motion.
For brands planning a campaign
For a B2B SaaS campaign about messaging, the useful contrast is “can be built” versus “can be operated”: compliance, manual effort and time are the real objections.
The buyer-role list and acknowledge–reframe–evidence–ask sequence could inform a practical resource.
Credit the session and speaker rather than presenting the framework as Arivarasan’s original work.
This answers “does this person build things, and what do they look like?”
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It is a working product artifact, not an essay, and shows both the audience Arivarasan is building for and how they ask for feedback.
Arivarasan is recruiting the first 25 early adopters of Ask Avi, a WhatsApp assistant that lets people use information they already have in Excel, PDF or Google Sheets.
The flow is simple: give Ask Avi your files, ask questions on WhatsApp, and find product or service information without digging through files.
It accepts Tanglish, Hinglish, Telugulish and Kannadlish — the way people actually write on WhatsApp, Arivarasan says.
They name small business owners, D2C and Instagram sellers, retailers and distributors, freelancers and service businesses, and anyone who repeatedly answers the same customer questions.
Examples include “What’s the price?”, “Is this available?” and “Can you send me the details?”.
The offer is 30 days free, with no payment now; the trial starts only once someone is onboarded.
Arivarasan especially wants people who already use a file today, so the product can be built around real processes.
They explicitly ask for criticism.
For brands planning a campaign
This is a launch and user-recruitment post: a strong signal of hands-on shipping and of the small-business audience Arivarasan understands, but less suited to a thought-leadership campaign. The product claims come from Arivarasan; ask for supporting evidence before using them as independently verified claims.
This answers “how does Arivarasan connect creator marketing to product work?”
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It is the shortest, most portable lesson in the set and shows their range beyond product management.
A recap of a session by Himanshu Agarwal titled “Lessons from Growing on Instagram: 0 to 50K.”
Arivarasan records the numbers — a first static post getting around 10 likes, a video with captions getting 5,000 views, and one later video taking the account from roughly 200 to 6,000 followers — but says what stood out was not the numbers, it was how many times the format changed: carousels, videos, different hooks, different ways of presenting the same kind of idea, with attention paid to what people responded to.
Arivarasan draws the product parallel directly: sometimes the problem is not the idea, it is that people are not getting it in the way it is presented, so change the format and watch what happens.
The line Arivarasan repeats is “Don’t fall in love with the format,” and Arivarasan adds that being genuine builds trust better than automated AI-generated content.
For brands planning a campaign
For a campaign involving creative or format testing, this is the anchor piece: it argues for rapidly iterating on presentation before assuming the underlying idea is wrong. The growth numbers come from the session speaker, not Arivarasan; attribute them if you reuse them.
This answers “what did Arivarasan’s earlier product writing sound like?”
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It is a useful contrast to the first-person, notes-from-a-session posts, and it shows the list format Arivarasan used before focusing on GTM and AI.
An essay-style post lays out four principles of minimalist product thinking.
Declutter the user experience: if users need a manual, you’ve already lost them, illustrated by Google’s homepage.
Feature dieting: every feature should fight for its place, as Dropbox did when it started as one folder syncing across devices.
Invisible design: the best products blend into users’ lives, with AirPods as the example because they have no buttons.
Prioritise outcomes over outputs: one feature that delivers value beats five half-finished ones, illustrated by Spotify’s personalised playlists.
The post closes by asking readers which feature they removed that made their product stronger.
The hashtags point to a personal product-writing identity.
For brands planning a campaign
This could work as an evergreen educational format, but the examples are familiar and need verification before a campaign presents them as evidence for a specific position. Pair it with Arivarasan’s later first-person posts to show how their writing evolves.
AI lowers build cost, raising the value of choosing the right problem3
Enterprise sales for AI-native startups1
Product discovery before AI solutioning1
Handoffs and human-in-the-loop in AI-era products1
Digital minimalism in product management1
Build-in-public notes from GTM and product work1
Content format iteration and authenticity1
Ask Avi and SMB conversational AI1
Enterprise sales for AI-native startups
Gowtham recaps a Vineeta Hazra session on selling to enterprise as an early-stage startup.
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The central argument: pitching what buyers cannot easily do themselves—such as compliance, regulation, manual work, and operational fixes—matters more than pitching product features.
The post breaks enterprise deals into roles: blocker, invisible influencer, economic buyer, champion, user, and coach.
It offers an objection sequence—acknowledge, reframe, evidence, ask—and argues there is no single GTM playbook, pointing to Atlassian's product-led growth, Palantir's forward-deployed engineers, and Stripe's developer simplicity.
Founders, it adds, should know when to walk away rather than get trapped by a logo.
Session recap organized as numbered takeaways with direct quotations and credited speakers.
For brands planning a campaign
Useful for B2B SaaS briefs on enterprise GTM, AI-native positioning, objection handling, and sales enablement. Brands could engage Gowtham for a practical teardown of buying-committee roles or a founder-led sales motion, especially when a product needs to be explained through the operational gap it removes rather than its feature list.
From a Manikantha S session at Sarvam AI, Gowtham highlights that good product managers learn fast, not just build fast.
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The lessons include spending 80% of time understanding the problem, talking to customers before deciding what to build, observing what users do rather than only what they say, and defining the user journey before building AI. The post advocates a build-test-observe-reflect prototyping loop and argues that product managers must explain both why something should exist and why it should not.
For brands planning a campaign
Relevant for AI product discovery, UX research, and product management education. A campaign could show a process that maps the user journey and task decisions before adding AI, using Gowtham's framing that prototype speed is not the same as product judgment.
Gowtham notes that products across lending, CRM, lead management, collections, HRTech, hospitality, and customer support all answer one question: who should handle this work next?
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Teams have optimized those handoffs with dashboards, workflows, notifications, and SLAs.
AI, they argue, now forces a harder question: whether some handoffs should exist at all, and where human-in-the-loop work should be eliminated.
The post frames this as a different and more difficult roadmap conversation.
For brands planning a campaign
Strong fit for campaigns on handoffs and AI-era product decisions. Brands could evaluate Gowtham for content about process automation, operations software, AI copilots, or roadmap tradeoffs between faster handoffs and eliminating them.
Gowtham argues that minimalism in product management is about clarity, efficiency, and impact rather than aesthetics.
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The recommendations: declutter UX, put features on a diet, pursue invisible design, and prioritize outcomes over outputs. The post uses Google, Dropbox, Apple AirPods, and Spotify as illustrations and closes by asking readers which feature they removed to make a product stronger.
The examples are illustrative, not original product research. Treat them as evidence of Gowtham's product philosophy, not as detailed proof of how those companies made decisions.
For brands planning a campaign
Relevant for UX simplification, feature prioritization, and design or product tooling brands.
A brand could commission a feature teardown or a 'what to remove' exercise.
Ask for a firsthand case study if detailed implementation experience is essential; this post offers list-style advice.
Gowtham describes spending six years behind the product across ecommerce marketplaces, fintech, NBFC, SaaS, AI projects, and now GTM.
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They lead product at heddl, a GTM platform built for how early-stage founders actually sell, and describe the work as messy because ICP, messaging, channels, and the whole GTM motion keep changing. The post says most GTM software was not designed for that reality and announces plans to share notes from user calls, product decisions, failed experiments, and team arguments.
Reflective build-in-public post that names the product context and invites ongoing discussion.
For brands planning a campaign
Useful for founder-led GTM, PLG and SDR workflows, early-stage SaaS, and product management content. Brands could assess whether Gowtham's build-in-public format suits an honest implementation story rather than polished thought leadership.
Gowtham recaps a Himanshu Agarwal session on growing on Instagram from zero to 50K.
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The post tracks format changes: a first static post got around 10 likes, a video with captions got 5,000 views, and one video took the account from around 200 followers to 6,000.
The takeaway: the problem is often not the idea but how it is presented, so teams should change the format and watch what people do.
The post favors being real and building trust organically over automated AI-based content.
For brands planning a campaign
Relevant for social and content strategy, creator marketing, and product discovery analogies. Brands could use the angle of testing formats and reading behavior signals instead of defending a single launch format.
Gowtham is building Ask Avi, a WhatsApp assistant that lets users ask questions about information already stored in Excel, PDF, or Google Sheets.
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The post targets small business owners, D2C and Instagram sellers, retailers and distributors, freelancers, and anyone who repeatedly answers the same customer questions.
It supports Tanglish, Hinglish, Telugulish, and Kannadlish, seeks 25 early adopters, and offers 30 days free with no payment required now.
The trial starts only after onboarding; Gowtham asks for people who already use real files so the product can be built around actual workflows.
This is a product launch and early-adopter request, not a general topic explainer. Treat it as evidence of current build focus, not as an endorsement or paid partnership.
For brands planning a campaign
Relevant for SMB SaaS, WhatsApp business, conversational AI, India-market products, and founder-led validation. Brands could evaluate a build-in-public angle about early-adopter recruitment or testing conversational AI against real customer-question workflows.
Across several posts, Gowtham Arivarasan attends sessions by other practitioners—Vineeta Hazra on enterprise selling, Himanshu Agarwal on Instagram growth, and Manikantha S on product at Sarvam AI—then translates them into product and GTM lessons.
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The pattern is consistent: they recap the session, extract numbered takeaways, connect them to discovery, GTM, or product judgment, and credit the speaker, hosts, and participants.
Session recap with quotations, numbered takeaways, hashtags, and explicit credit to speakers and hosts.
For brands planning a campaign
A brand can assess Gowtham for event-based or expert-session content. A campaign could give them access to a practitioner or event and ask them to translate product and GTM lessons rather than offer generic commentary.
AI lowers build cost, raising the value of choosing the right problem
A recurring argument across Gowtham's posts is that AI and modern prototyping make 'can be built' a weak bar.
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ChatGPT can scaffold an idea, APIs can stitch it together, and an engineer can prototype in a weekend.
They focus instead on compliance, regulation, manual effort, user journeys, and whether a step or handoff should exist at all.
The through-line is that problem selection, customer observation, and product judgment matter more when building becomes cheap.
For brands planning a campaign
Useful for AI product positioning, discovery, product-market fit, and build-versus-buy messaging. A campaign angle could contrast prototype speed with operational adoption and show where AI changes the product decision rather than just the delivery speed.
Gowtham Arivarasan says they lead product at heddl, a GTM platform for early-stage founders, and are building Ask Avi, a WhatsApp assistant for querying business files.
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Their posts do not document sponsor, affiliate, client or investment relationships; ask for disclosure.
heddl
Employment
Gowtham Arivarasan says they lead product at heddl, a GTM platform built around how early-stage founders sell.
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In another post, they refer to applying session learnings to heddl's GTM motion.
Gowtham Arivarasan writes short field notes about product management, go-to-market and how AI changes product decisions.
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Their session recaps record takeaways from practitioner talks, with speakers named, and explain what they will take back to their own work.
First-person posts offer the clearest view of their thinking: an essay asking whether the handoffs product managers spent years perfecting should exist at all, and a reflection on leading product at an early-stage GTM platform where the ideal customer, messaging and channels keep moving.
Their build-in-public work applies the same thinking on a smaller scale: a WhatsApp assistant for small businesses that answers questions from files they already keep, alongside a public request for early adopters and criticism.
An earlier, separate mode appears in a principle-style piece on minimalist product management, illustrated by Google, Dropbox, AirPods and Spotify.
Read the handoffs essay alongside the practitioner recaps to see their recurring argument: cheap building shifts the hard work to choosing the right problem and understanding what buyers cannot easily do themselves.
A product management writer tackling the same AI-era question from a different direction: what remains for a product manager when building gets cheap.
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Both argue that problem selection, customer pain and judgment are what AI cannot supply, and frame this as a challenge to current industry practice rather than a tooling tutorial.
Singh writes long, statistics-heavy analytical essays on project failure rates, investment figures and the history of the product manager role, and comments on a mentor cohort teaching PRD review. Arivarasan writes shorter field notes drawn from named practitioner sessions and their own product work, and rarely builds an argument from market data.
Both are founder-builders writing in public about conversational AI taking over work that used to happen through a website or a person, and explain the idea through an origin story rather than a product announcement.
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Warwick argues conversation beats navigation for discovery, support and returns; Arivarasan makes a similar case for a WhatsApp assistant answering the questions small businesses repeat all day.
Warwick's subject is narrow and commercial: WhatsApp as the next default channel for UK retail brands, with the in-store sales associate as the model to replicate. Arivarasan ranges across product discovery, enterprise selling and GTM strategy, with conversational AI as one current build rather than their organising theme.
A product manager in fintech who builds AI prototypes and writes up the product lesson, much like Arivarasan, who posts notes from their own experiments.
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Both use natural language as an input that a system must turn into something actionable: Romero turns speech into validated form data, while Arivarasan turns spoken questions into answers from a spreadsheet.
Romero writes implementation walkthroughs that name the specific stack, plus job posts for a lending product team; the writing centres on the mechanics of the demo. Arivarasan focuses on discovery, positioning and GTM implications, and writes session recaps alongside their own product notes.
Before committing to a B2B SaaS campaign aimed at product-led GTM leaders, request stronger audience evidence.
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The only measured audience figure is 2,998 LinkedIn followers, observed on Gowtham Arivarasan's LinkedIn profile on 9 October 2026.
Their posts cover product discovery, GTM motions, workflow handoffs, AI-era product decisions, and build-in-public updates, making the topic match plausible.
Audience composition is unverified.
Ask for recent audience survey results or platform demographic data covering country, age, gender, employer or industry, and job seniority, plus results from sponsored content on comparable product-management and GTM topics.
Gowtham Arivarasan’s posts focus on first-person learning notes about product management and go-to-market: session write-ups, a product-thinking reflection, and an early-access call for Ask Avi, a WhatsApp assistant for retrieving information from business files.
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The Ask Avi post invites small-business owners, D2C and Instagram sellers, retailers, freelancers and others to apply for one of 25 early-access spots, with a 30-day free trial and no payment required at that stage.
That is a product trial for users, not a paid creator partnership.
The posts provide no sponsor policy, rate card, media kit, audience specification or partnership inquiry route.
For a B2B SaaS campaign, ask the creator directly about sponsored formats, pricing, production timelines, disclosure practices and approval processes.
Need: content for a B2B SaaS company's product and GTM leads to decide where AI changes the roadmap as they sharpen their product-led go-to-market strategy.
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Decision: yes on subject authority, with two things to confirm before contracting.
Gowtham Arivarasan writes about the decision layer this content need depends on, not just the vocabulary.
The clearest example is their handoff argument: across lending, CRM, lead management, collections, HR technology, hospitality and customer support, the products all answered one question — who should this piece of work go to next?
Four chains carry the idea: a loan application moving Sales to Operations to Risk, a support ticket moving L1 to L2 to Engineering, a lead moving Marketing to Sales, and an appraisal moving Employee to Manager to HR.
The post argues that AI does not only make handoffs faster; it forces teams to ask whether some handoffs should exist at all.
That is a roadmap question product and GTM leads need someone to frame.
The same discipline shows up in the Sarvam AI recap: understand the problem before building, define the user journey before the AI, watch what users do rather than only what they say, and be able to explain why something should not exist.
The enterprise-selling recap supplies the commercial half: pitch what a buyer cannot easily do themselves — compliance, regulation, manual effort, operational mess — rather than the feature list, and know who inside the buying committee you are addressing.
Before contracting, settle two points.
First, agree on commercial terms: the posts provide no sponsorship format, rate card, production timeline, disclosure practice or inquiry route, so confirm pricing and deliverables directly.
Second, verify audience delivery.
The only measured figure is 2,998 LinkedIn followers observed on 9 October 2026; there are no demographic details, engagement rate or sponsored-content results.
Ask for audience data and sponsored-post performance.
Also address the independence question: they state they lead product at heddl, a GTM platform for early-stage founders, and the enterprise-selling recap says they are taking those learnings back to heddl's GTM motion.
For a competing or adjacent GTM product, frame the work as a practitioner's view and disclose that tie, rather than presenting it as neutral analyst commentary.
When to choose a specialist
Need: a direct-response reach campaign for a WhatsApp-based commerce tool aimed at Indian small retailers and D2C sellers, bought for volume — impressions, clicks and sign-ups — with an expectation that the creator's audience converts.
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This is the wrong campaign to buy on reach, for three reasons.
First, audience delivery is unverified: the profile shows 2,998 LinkedIn followers, but provides no platform demographic details, engagement rate or past sponsored-content performance.
Ask for audience composition and sponsored results before pricing a reach buy.
Second, the topic adjacency is real, but the evidence is about product-building, not media reach.
The Ask Avi post shows they understand this buyer — small business owners, D2C and Instagram sellers, retailers and distributors, freelancers, and anyone repeatedly answering what is the price, is this available, can you send me the details — and that the product accepts Tanglish, Hinglish, Telugulish and Kannadlish.
But the post is a recruitment call for 25 early adopters routed to their own network, not evidence that the account reaches SMB decision-makers at volume.
Third, someone buying impressions normally wants a short, punchy sponsored format, while these posts are long session recaps, numbered takeaways and first-person reflection.
That format suits a considered GTM campaign, not a volume play.
This campaign would work better as a validation or feedback request than a reach buy: ask for candid critique of a product aimed at small sellers, in the style of the Ask Avi post, which explicitly invites criticism.
Reframed that way, the fit improves; as an impressions purchase, it belongs with someone who can evidence audience composition and sponsored results.
A worked campaign example
Angle: Which handoffs should stop existing?
Step 1 — open with their own question, not a rewritten one.
Use the framing that products across lending, CRM, collections, HR and support were all answering who the next human is, illustrated with the four chains: loan application Sales to Operations to Risk; support ticket L1 to L2 to Engineering; lead Marketing to Sales; appraisal Employee to Manager to HR.
Note that product teams got very good at perfecting those handoffs with dashboards, workflows, notifications and service-level agreements.
Present it as a question they tested in public — the post ends by asking for other perspectives — rather than a settled thesis.
Step 2 — turn the screw.
AI does not just make a handoff faster; it raises the question of whether the handoff should exist.
Frame the buyer's real problem using the enterprise-selling lesson: everyone can build now, so the pitch is what the buyer cannot easily do themselves — the compliance, the manual effort, the time, the operational mess.
Step 3 — give the reader a worked exercise on their own product.
Pick one handoff in the workflow.
List what it actually protects: a risk check, ownership of an error, cost approval, a relationship moment.
Separate the parts that genuinely need a person from the parts that exist only because the software could not do them.
Then decide per step: keep, automate, or delete.
The product-discovery lessons from the Sarvam AI recap give the rule for the AI step — start from what the user is trying to accomplish, break it into tasks and decisions, and only then decide where the model adds value.
Watch what users do with the current workaround, not only what they say they want.
Step 4 — close with the roadmap tradeoff. Frame the harder conversation: faster handoffs versus fewer handoffs, and who inside the buying committee — the blocker, the invisible influencer, the economic buyer, the champion, the user, the coach — has to agree before a deleted step ships.
Deliverable shape: one long-form piece plus a short companion worksheet a product lead can fill in with their own workflow, in the style of the practical, credited session recaps this creator already writes. Keep the credit lines for the named speakers whose frameworks appear along the way.
Before commissioning, agree fees, format, review and disclosure in writing; ask for audience details covering country, industry and seniority, plus sponsored-post results on product-management or GTM topics; and choose the reader action — a booked call, a worksheet download, or a reply thread. The posts currently end in discussion, not a hard conversion ask.
Choose Alok Kumar Singh when the campaign focuses on the AI-era product decision and the buyer wants an argued case for problem-first discipline.
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One post assembles failure statistics — roughly 80% of AI projects not delivering business value and 95% of generative-AI pilots with no profit-and-loss impact — and argues that leadership and problem clarity, not the technology, are to blame.
It quotes an IBM researcher who observed that companies said, 'Step 1: use LLMs.
Step 2: what should we use them for?' The other post shows how prompts, checklists and scoring rubrics applied to PRDs and prototypes can scale one mentor's thinking to more than 100 product managers.
If the campaign needs someone to argue that an AI-heavy roadmap should start from the problem and show a concrete review-and-feedback process, this is the stronger match.
The tradeoff is firsthand go-to-market experience.
The posts make arguments and teach through statistics, a historical analogy to the 1931 origin of the product-manager role, and a cohort feedback process.
They do not describe selling into early-stage founders or redesigning a live go-to-market motion.
Gowtham Arivarasan's handoff essay and note that they lead product for a GTM platform provide that practitioner context and the specific framing for a campaign grounding the handoff question in someone's product and selling work.
Choose Alok when the argument about AI product decisions matters more than the GTM-platform detail; choose Gowtham when the buyer wants the handoff question grounded in someone's product and selling work.
Choose Josh Warwick when the opportunity is the interface itself: conversation replacing website navigation for discovery, support and purchase.
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The two posts argue that large language models make conversation a more efficient interface than a website for a wide class of tasks; that WhatsApp is where those conversations already happen at scale; and that an agent can handle discovery, support, cart recovery and returns without labour costs scaling linearly with each conversation.
The second post grounds the idea in the origin of their product: a real assistant in a Prada store messaging on WhatsApp, walking the store to photograph products, recommending a gift and sending a payment link.
That approach worked only at luxury order values until language models could handle the variation.
For a product-led GTM campaign focused on the channel or buying interface, this is the closer match.
The tradeoff is scope.
The posts cover consumer and commerce messaging — beauty, fashion and lifestyle brands, and WhatsApp commerce in Brazil, India, Mexico and Singapore — as well as the origin story of one startup.
They do not cover internal workflow handoffs or enterprise buying-committee decisions.
Gowtham Arivarasan's posts do, including named buying roles such as the blocker, champion and economic buyer.
If the campaign angle is that conversation is the new interface for buying, choose Josh; if it is which steps in a workflow still need a human, choose Gowtham.
Choose Jose Romero when the campaign needs a demonstrated build that turns messy human input into structured product value.
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The posts show a Voice-to-Form demo built with Google Genkit and Firebase.
It listens to natural speech, extracts and normalises data such as weekly versus annual income, and validates it against schemas so downstream systems can trust it.
The demo targets friction in FinTech and HealthTech forms.
The second post is a hiring call for a technical product manager for data products, specifying skills in data-analytics roadmaps, compliance with data regulations, and tools such as SQL, Power BI and Tableau.
For a campaign where the seller wants to show a working prototype of conversational input replacing a form, this is the more concrete choice.
The tradeoff is breadth and recency.
The posts comprise one prototype write-up and one hiring post, focused on form and input experience in FinTech and HealthTech; the hiring post dates to 2024.
They offer a demo and role description, rather than a product lead's account of a go-to-market motion or a roadmap decision about which work should stay human.
Gowtham Arivarasan's posts provide that decision-level framing and GTM-platform context.
Choose Jose when a demonstrated prototype is the proof the campaign needs; choose Gowtham when it needs someone to reason about handoffs and AI-era roadmap choices.
Gowtham Arivarasan's LinkedIn posts include recaps of sessions led by named speakers on enterprise selling, AI product management and Instagram growth, first-person essays on handoffs, digital minimalism and product work at an early-stage GTM platform, and an early-access post for a WhatsApp assistant.
A profile snapshot records 2,998 followers observed on 9 October 2026.
The posts do not give individual publication dates, so their order is approximate and change over time cannot be confirmed; the profile sections place the material between March 2025 and September 2026.
Session lessons and quoted growth figures belong to the speakers credited in the posts, not to Arivarasan.
The posts here do not include session recordings or transcripts, audience demographics, engagement rates, sponsorship terms, inquiry routes, or any channel beyond this LinkedIn account.
Those gaps are noted where they could change a campaign decision.
