Sahil Aggarwal works on Von, an AI platform for revenue teams. His posts describe building software that gives AI context about a company’s data, processes, customers, and deals, then uses that context to answer questions and support team workflows.
Sahil Aggarwal is most useful for explaining AI trust and business context to revenue operations teams.
Best for
AI or revenue operations software vendors seeking a practitioner voice and co-marketing.
Not for
Brands needing a bookable sponsored placement with published deliverables, rates, and verified buyer segments.
Key takeaways
AI should show its work so revenue teams can trust it.
Business context is the foundation, and applications get built on top of it.
A sales leader facing a six-week deadline used Von to build enterprise outbound.
Check first: Confirm whether they offer sponsorship terms, deliverables, pricing, inquiry route, and audience demographics.
Main ideas
AI should show its work so revenue teams can trust it · Business context is the foundation, and applications get built on top of it · The case for adopting AI early: it can learn the business faster than you can
Sahil Aggarwal works on Von, an AI platform for revenue teams.
In the supplied posts, he is best known for describing how Von builds a working model of a company’s business context—its metrics, processes, customers, accounts, and relationships—and uses it to support analysis and workflows.
His posts give readers a practical view of the product idea and how a customer might apply it, rather than only announcing features or milestones.
Aggarwal connects product decisions to the problem revenue teams face: AI can be hard to trust when people cannot see what it knows or how its knowledge is connected.
In one post, he explains why Von made its “GTM Brain” visible and describes a direction from asking AI questions toward building workflows on shared business context.
A customer story adds a concrete use case: a sales leader facing a six-week deadline used Von to absorb years of company knowledge and begin building an enterprise outbound motion.
Other posts show a complementary focus on product progress and community, including a usage milestone and an informal gathering around Dreamforce.
These examples make his work worth following for readers interested in AI product development for revenue operations and the practical adoption of business context in sales workflows.
The supplied material does not establish whether books, shows, earlier work, or other signature projects are also central to why people search his name; targeted identity and search-intent research is needed before treating this as a complete account of his public profile.
Sahil Aggarwal writes mostly about Von, an AI platform for revenue teams, and the arguments in these posts point in one direction: AI is only as useful as the business context behind it, and that context is worth showing to the people who use it.
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He argues that revenue operations teams adopt AI more readily when they can see what the system knows, that building an accurate model of a company is the foundation and applications come on top of it, and that the practical win for a new sales leader is learning a business faster than a person can alone.
A separate priority in the material is meeting people in person away from a conference floor.
The one post that steps outside this is a public endorsement of another martech company's funding round — the only place he states his own background, more than a decade in marketing technology.
AI should show its work so revenue teams can trust it
Aggarwal argues that for revenue teams, an AI product's value depends on people being able to see what the system knows and where that knowledge came from, not just on the answers it produces.
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His reasoning is that AI software otherwise reads as a black box, and that making the underlying model visible turns it into something a team can reason about. He treats this as increasingly central to where the product is heading, not a one-off feature.
He describes shipping a view of Von's "GTM Brain" — a graph of a company's definitions, metrics, processes, systems, customers, deals, accounts and the relationships between them.
He says he expected almost nobody to care and was surprised by how strongly customers and prospects reacted once it was exposed.
He closes by asking RevOps readers directly whether seeing what an AI system actually knows makes them trust it more, or whether they only care about the output.
Business context is the foundation, and applications get built on top of it
The recurring priority is that the hard, unglamorous work is giving AI an accurate model of how a company works — its metrics, deals and customer history — and making that model useful enough for people to get real work done.
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Everything else is described as built on that base. His stated aim is to stop teams rebuilding the data model and business context for every new question, so they move from asking whether AI can answer something to building the workflow themselves.
In a milestone post he says Von crossed one trillion tokens processed for customers, setting it against earlier points where ten billion tokens felt large, then became a weekly figure, then five hundred billion cumulative.
Most of that post is about what comes next: the first half of the year spent making Von's context about a business useful, and the team's most ambitious work now being built on top of it, with some pieces already in beta with selected customers.
The other post gives the customer-facing version of the same argument — over the next three months, customer graphs and custom data applications so revenue teams build on the context Von already has.
The two posts describe complementary halves of one position: one treats context-building as groundwork largely done, the other describes handing that context to customers as applications they build themselves.
The case for adopting AI early: it can learn the business faster than you can
Aggarwal's argument in his customer story is about pace rather than features.
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A new leader arriving at a company has weeks to absorb years of accounts, objections, pricing habits and institutional memory, and the tool is worth adopting immediately because it can take on that learning at a speed a person cannot match. Once the system knows the business, the leader can hand it work and get to building the motion and reporting they were hired for.
He recounts a first-week call from Angelo Quiambao, who had used Von at a previous company.
Quiambao, per Aggarwal's account, had six weeks to build an enterprise outbound motion and put agents live by September 1 for a company that had spent twenty years selling to small and midsize businesses.
Aggarwal's framing is that Quiambao did not spend his first month working through the revenue technology stack; he added Von immediately so he could learn the business at Von's pace instead of his own.
The post ends with a thank-you to Quiambao for trusting Von.
A priority visible in his posts is in-person presence that is deliberately not a booth.
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He frames a rented space near a large industry conference as an antidote to the show floor — somewhere people can sit down, have a decent coffee, meet each other and step out of the noise — and programs it with concrete activities rather than product demos. The programming also shows a willingness to share the room with other vendors and communities rather than keep it purely promotional.
For Dreamforce, he describes renting a space a few blocks away and running it as VonSpace for three days.
The schedule includes Counter Culture coffee and pastries, puppies from 11 to 1 on the Tuesday and Wednesday, lunch on both days, a vinyl DJ and drinks evening, a CRO roundtable co-hosted with Fullcast on the Wednesday afternoon, a RevOps happy hour that evening, and time to book one-to-one conversations with the Von team.
He gives the dates, September 15–17, and the SOMA street address.
This is the clearest statement of what Sahil Aggarwal builds and why.
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If you only open one post, open this one: it explains the product idea, the problem it addresses, and the direction of travel in a single read.
Sahil describes shipping a feature that exposes Von's "GTM Brain" in the product.
The GTM Brain is described as a living graph of how a company works: its definitions, metrics, processes, systems, customers, deals, accounts, and the relationships between them.
The feature had existed underneath the product for a while but was invisible to users; once it was exposed, customers and prospects reacted more strongly than expected.
Sahil explains the reaction: AI software feels like a black box, and revenue operations teams want to know what the system knows, where that knowledge came from, and how it connects.
Existing customers already use the GTM Brain to ask questions and build analyses across their business.
Sahil says the next three months of work will add customer graphs and custom data applications so revenue teams can build on the same business context rather than rebuild it each time, moving from "Can AI answer this question?" to "Can we build this workflow ourselves?"
The post ends with a direct question to RevOps readers about whether visibility into what an AI knows increases trust.
Answers the question a reader usually has after reading a product description: what does adoption actually look like?
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It is a short, specific customer story rather than a testimonial slogan.
Sahil recounts a call from Angelo Quiambao during Quiambao's first week at Intellistack.
Quiambao had used Von at a previous company, so he already knew its capabilities.
He described a six-week window to build an enterprise outbound motion and have agents live by September 1, at a company that had spent twenty years selling to small and midsize businesses.
Sahil's detail is that years of accounts, objections, pricing habits, and institutional memory existed that Quiambao had not been present for, with only weeks to absorb them.
Quiambao's response, as Sahil tells it, was to add Von immediately because he could not learn the business at the pace Von could; once Von understood the business, he could hand it work and start building the motion and reporting he needed.
Shows how Sahil talks about progress: a number, its trajectory, and what it means for the product roadmap.
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Useful if you want to see how this person communicates scale without overclaiming.
A milestone post: Von crossed one trillion tokens processed on behalf of customers.
Sahil sets the scale against earlier points — ten billion tokens felt like a milestone at the start of the year, then ten billion became a weekly figure, then five hundred billion cumulative two months before this post, and now one trillion with usage still growing.
The larger part of the post is about what comes next.
Sahil says much of the first half of the year went into building the foundation: giving Von context about a business and making that context useful enough for people to get work done.
On top of that foundation the team is now building its most ambitious work so far, with some pieces already in beta with select customers and more reaching customers over the following months.
It ends with the line that one trillion is a meaningful milestone, but this is still early.
Short, but it answers two questions at once: how Sahil engages with the wider software market, and what he says about his own background.
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Read it after the product posts to see the person behind the roadmap.
A brief congratulation to Ajeet and the team at Different on their launch and a 48 million dollar seed announcement.
Sahil writes that having spent more than a decade in marketing technology, the idea makes a lot of sense, and invites readers who have been thinking about giving customers different and unique experiences to look at the company.
The post contains a self-stated career detail — over a decade in marketing technology — and nothing beyond a public endorsement.
Answers what Sahil's team does around industry events, which is a different question from the product posts.
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Read it if you care about field marketing and how a vendor creates a room away from a trade show floor.
Sahil explains that instead of working the Dreamforce floor, the team rented a space a few blocks away and turned it into VonSpace for three days.
The post lists what the space offered: Counter Culture coffee and pastries, puppies from 11 to 1 on Tuesday and Wednesday, lunch on both days, the Von Soirée on Tuesday night with a vinyl DJ and drinks, a CRO roundtable co-hosted with Fullcast on Wednesday afternoon, a RevOps happy hour on Wednesday evening, and time to book one-to-one meetings with the Von team.
It gives the dates and street address and links to the full schedule.
The tone is deliberately informal — the aim stated is somewhere people can sit down, have a good coffee, meet people, and get away from conference chaos.
Aggarwal describes shipping a feature that exposes Von's "GTM Brain" — a graph of a company's definitions, metrics, processes, systems, customers, deals, and accounts, and the relationships between them.
Read more
He says he expected almost nobody to care, and was surprised by how strongly customers and prospects reacted once the underlying model became visible in the product.
His explanation is that AI software otherwise reads as a black box, and RevOps teams want to know what the system actually knows, where that knowledge came from, and how it connects.
He sets out the next step as letting revenue teams build their own analyses, graphs, and data applications on top of the same context instead of rebuilding the data model each time.
He walks through a product decision as a sequence of expectation, surprise, and his own interpretation of why the reaction happened, then closes by asking readers directly whether seeing what an AI system knows makes them trust it more or whether they only care about the output.
For brands planning a campaign
Useful for a brief aimed at AI-skeptical revenue operations buyers. The argument being made is that visible context, not just answer quality, is what moves adoption, so a campaign could pair a product explainer on model transparency with a practitioner poll rather than a generic capability claim.
Aggarwal marks Von crossing one trillion tokens processed on behalf of customers.
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He gives the growth in his own terms: ten billion tokens felt like a milestone at the start of the year, then the platform was doing ten billion in a week, then five hundred billion cumulative two months earlier. He treats the number as a checkpoint rather than a finish, describing the first half of the year as foundation work — giving Von context about a business and making that context useful — and says more ambitious work built on that foundation is moving into customer hands over the following months, with some already in beta with select customers.
The post is short and laddered: a single headline figure, then a personal-scale comparison to make the number legible, then a forward statement about what has not shipped yet.
For brands planning a campaign
Gives a partner a ready-made milestone hook for co-marketing or launch timing, and shows a willingness to publish the intermediate numbers behind a claim. A brief about a platform launch or a beta programme could anchor to this kind of checkpoint post.
Customer adoption told through one person's decision
Aggarwal tells the story of Angelo Quiambao calling him in his first week at Intellistack.
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Quiambao had used Von at a previous company, had six weeks to build an enterprise outbound motion and put agents live by September 1st, and was joining a company that had spent twenty years selling to SMB — meaning years of accounts, objections, pricing habits, and institutional memory that he had not been present for. Aggarwal's framing is that Quiambao added Von immediately rather than spending the first month working through the revtech stack, because he could not learn the business at the pace Von could.
A named customer story built around a constraint and a deadline rather than a feature list, ending with a direct thank-you to the person.
For brands planning a campaign
Shows a reference-style format a brand could reuse: the named practitioner, the time pressure, and the specific organisational gap. Relevant to a brief about onboarding speed, enterprise outbound motions, or selling into a business with a long sales history.
Running off-site programming around a major conference
For Dreamforce, Aggarwal describes renting a space a few blocks from the venue and turning it into VonSpace for three days — a place to sit down, have coffee, meet people, and step away from the conference floor.
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The programme listed includes Counter Culture coffee and pastries, puppies for two hours on the Tuesday and Wednesday, lunch on both days, a vinyl DJ and drinks evening, a CRO Roundtable with Fullcast on the Wednesday afternoon, a RevOps Happy Hour that evening, and time to book one-to-one conversations with the Von team. He gives the dates and the SOMA street address and links to a full schedule.
An event announcement written as an itinerary of concrete named activities and times, with the venue framed as an antidote to the main conference rather than a booth alternative.
For brands planning a campaign
Fits an experiential or field-marketing brief where a brand is considering presence around a large industry conference, including co-hosted roundtables and happy hours. The named partner slot in the roundtable is a concrete example of how a third party can be woven into event programming.
Aggarwal congratulates Ajeet and the Different team on their launch and a $48M seed announcement, saying the idea makes a lot of sense to him after more than a decade in martech, and suggesting readers look at the company if they have been thinking about giving customers differentiated and unique experiences.
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This post does not mention Von or its product at all. It is the one item in the reviewed material where he speaks as an industry observer rather than as a builder describing his own platform, and it is the only place his own stated background appears.
For brands planning a campaign
Shows he will endorse and amplify other companies in the martech and customer-experience space, which matters when a brand is weighing a peer endorsement or an ecosystem mention rather than a product-led collaboration.
Sahil Aggarwal's saved posts point mainly to one business: Von, the AI platform for revenue teams he writes about in the first person as the product he ships.
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The remaining brand connections are narrower — a customer story built around Intellistack, a co-hosted event session with Fullcast, and a public congratulation to Different. The reviewed material shows no paid sponsorship, affiliate arrangement, or investment relationship.
Von
Employment
Sahil Aggarwal writes about Von as his own product: exposing Von's "GTM Brain," a graph of a company's definitions, metrics, processes, systems, customers, accounts, and deals, and announcing that Von crossed 1 trillion tokens processed for customers.
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The posts describe product direction, including customer graphs and custom data applications planned for the following three months, and VonSpace, a space Von rented near Dreamforce with coffee, food, and sessions. First-person "we" language throughout places Sahil Aggarwal on the Von team; no separate commercial terms are stated.
A post tells how Angelo Quiambao called in his first week at Intellistack after using Von at a previous company, and added Von immediately to build an enterprise outbound motion and put agents live by September 1 at a business that had sold to SMB for twenty years.
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The post presents Intellistack as a Von customer and thanks Quiambao for trusting Von; it does not state Sahil Aggarwal's own role in that account.
VonSpace, the space Von rented a few blocks from Dreamforce for September 15–17, hosted a CRO Roundtable with Fullcast on the Wednesday afternoon alongside other sessions.
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Fullcast is named as the collaborator on that one event; the posts state no terms, duration, or payment.
Sahil Aggarwal congratulated Ajeet and the Different team on their launch and $48M seed round, adding that after more than a decade in MarTech the idea makes sense and recommending readers look at it.
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The post is a public endorsement with no stated commercial relationship.
Sahil Aggarwal's posts are mainly about making AI useful to revenue teams by giving it an accurate model of how a company works and then showing that model to the people who use it.
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Aggarwal writes about Von's GTM Brain as a visible graph of a company's definitions, metrics, processes, systems, customers, deals, and accounts, and argues that AI software otherwise reads as a black box.
A new reader should start with the GTM Brain post to understand the central claim, then move to the trillion-token milestone and the Angelo Quiambao customer story to see how context-building is framed as the foundation for adoption.
The Dreamforce off-site post shows a different side: event programming built around coffee, puppies, a CRO roundtable, and a RevOps happy hour rather than a product booth.
For readers tracking AI in revenue operations, product transparency, or field events, this is a short, recent sample of first-person posts rather than a full archive.
Seshan's posts also explain how AI agents are being wired into sales work: capturing context from calls and emails, researching accounts, personalizing outreach, and surfacing CRM actions where sellers already work.
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That is close to Aggarwal's argument that AI becomes useful when it holds and exposes business context for revenue teams.
Seshan writes about Salesforce Sales Cloud's announced products, including Momentum, Hunter, and Salesforce in Claude, as a product and keynote narrative. Aggarwal writes about Von as a platform builder and focuses on what customers do with visible context.
Skinner writes about practical AI and GTM systems, including signal-based outreach, cheap research filters before expensive model reasoning, and the infrastructure debt that builds up under agent work.
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The emphasis on making AI operational rather than merely capable overlaps with Aggarwal's context-and-applications argument.
Skinner's posts are oriented around founder-led GTM sprints and consulting offers, with event-floor observations and a two-hour sprint method. Aggarwal stays closer to product design, adoption stories, and RevOps use cases.
Stepanchuk works on B2B go-to-market, signals-based plays, and in-person industry events, including a cybersecurity marketing conference and Black Hat.
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That mix of demand generation, field presence, and signals overlaps with Aggarwal's revenue-team and event programming themes.
Stepanchuk's material is grounded in cybersecurity marketing and community building through Cusp GTM and CyberMarketingCon. Aggarwal's is grounded in an AI revenue platform and enterprise sales adoption.
The evidence supports a channel-and-topic match before it supports an audience-composition claim.
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Sahil Aggarwal publishes on LinkedIn about Von, an AI platform for revenue teams, and the posts are written for the people who run revenue operations.
One post asks "the RevOps folks" directly whether seeing what an AI system actually knows changes their trust in it; another describes an enterprise outbound leader who put Von live within six weeks at a company that had sold to SMB for twenty years.
That tells a buyer the material is aimed at RevOps and go-to-market software buyers, but it does not measure who actually follows or reads the account.
The only measured audience figure in the reviewed material is a LinkedIn profile snapshot showing 20,429 followers, observed 8 October 2026.
Nothing in the reviewed material reports geography, age, gender, job function, seniority, company size, or buying authority, so this section cannot support a claim that the following is made up of, for example, VP-level RevOps decision-makers.
Follower count here is a scale signal, not evidence that the followers match a target buyer profile.
Next step for a buyer: if the brief depends on verified access to senior RevOps or revenue-leadership buyers, ask whether Sahil Aggarwal can supply audience survey results or a LinkedIn follower-demographic export covering geography, job function, seniority, and company size, and check that against the brief before committing.
If the brief only needs a LinkedIn channel with relevant revenue-operations subject matter, the posts and the follower scale are enough to proceed.
LinkedIn followers
20,429
LinkedIn profile snapshot for linkedin.com/in/saggarwal2 · 2026-10-08
The reviewed material documents no sponsorship offering, rate card, or paid-partnership format associated with Sahil Aggarwal, and it publishes no inquiry route for one.
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What the posts do show is marketing activity run through Von rather than a personal creator inventory: a three-day VonSpace pop-up held a few blocks from Dreamforce on September 15–17 (257 3rd St, SOMA) with coffee, pastries, lunches, a vinyl-and-drinks evening, a CRO roundtable co-hosted with Fullcast, and a RevOps happy hour; posts promoting the GTM Brain and a 1-trillion-token usage milestone; a customer story about an enterprise outbound motion built on Von; and a congratulatory note on Different's $48M seed launch.
For a buyer, the practical consequence is that there is nothing published to book — no disclosed formats, deliverables, pricing, or intake point — so any collaboration would have to be scoped and negotiated directly.
The Dreamforce programming is evidence of how the team runs partner-adjacent events, not a purchasable placement.
This is a documented absence of partnership terms in the reviewed material; it is not a claim that Sahil Aggarwal never accepts sponsorships.
A vendor selling AI or revenue-operations software wants a practitioner voice to carry an argument about trust in AI, and to support it with co-marketing around a milestone or an industry event.
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Why
The reviewed material shows Sahil Aggarwal making exactly this argument in public. In the GTM Brain post, the point is not that the model answers well but that making the system's knowledge visible — the company's definitions, metrics, processes, systems, customers, deals, accounts and the links between them — is what moves adoption, because revenue operations teams otherwise read AI software as a black box and cannot see what it knows, where that knowledge came from, or how it connects. The post closes by asking RevOps readers directly whether visibility increases their trust or whether they only care about the output, which is a ready-made poll or comment hook for a campaign. Two other formats reuse cleanly: the one-trillion-token post ladders a metric from ten billion tokens, to ten billion a week, to five hundred billion cumulative, to one trillion, which is a template for publishing intermediate numbers behind a claim; and the VonSpace post shows how a third party was woven into event programming through a CRO roundtable co-hosted with Fullcast alongside a happy hour and a one-to-one meeting slot. The Angelo Quiambao post shows the adoption narrative a brand would want told — the named practitioner, the six-week deadline, and the twenty-year SMB sales history he had to absorb — rather than a feature list. A brief that needs a point of view, a milestone hook, a customer-story shape, or conference-adjacent programming is served by this material. A brief that also asks for verified reach into senior revenue buyers is not, on its own, supported: the only measured audience figure is a LinkedIn profile snapshot showing 20,429 followers observed 8 October 2026, with no geography, function, seniority or company-size data attached.
When to choose a specialist
A brand wants a bookable sponsored placement with published deliverables, a rate card, an intake process and verified access to specific buyer segments — for example a defined number of posts reaching VP-level revenue operations decision-makers in named regions.
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Why
The reviewed material documents no sponsorship offering, no deliverables, no pricing and no inquiry route, so there is nothing published to book and any collaboration would have to be scoped and negotiated from scratch. The evidence rule here matters in both directions: this is an absence of partnership terms in the reviewed material, not a finding that Sahil Aggarwal never accepts sponsorships. Audience targeting fails the same test. The 20,429-follower snapshot gives scale but says nothing about who the followers are, so a brief that depends on reaching a defined seniority, function, geography or company size cannot be judged from this evidence; a buyer would need a follower-demographic export or survey results before committing. A third limit is scope of evidence. The five posts are a small, recent sample with no publication dates in the reviewed material, no video, no transcripts and no article links, so a brief that needs demonstrated reach across a long period, a proven body of work before the Von material, or performance on formats outside short LinkedIn posts would be resting on more than the reviewed evidence supports. A brief aimed at consumer or non-revenue audiences would also be a mismatch: four of the five posts are written for the people who run revenue operations, and the fifth is a brief endorsement of another software company's launch.
A worked campaign example
Angle
A 90-day "show your work" trust campaign with a revenue-operations AI vendor, built on the argument Sahil Aggarwal already makes rather than a fresh creative concept. The campaign runs three linked pieces: a short LinkedIn series in which each post opens one element of what an AI system knows about a customer's business — a definition, a metric, a deal stage — and explains what changes once that element is visible to the team; a poll or comment prompt drawn directly from the closing question in the GTM Brain post, asking revenue operations readers whether seeing what an AI knows makes them trust it more or whether they only care about the output; and a live roundtable at an industry conference, run the way the VonSpace CRO roundtable was run with Fullcast, where the vendor's customer and a named partner sit on the same panel.
Deliverables
Three to four short LinkedIn posts from Sahil Aggarwal's account, each opening one piece of the system's business context, with one post carrying the trust question as a poll or comment prompt
One customer or practitioner story built to the Quiambao shape: the named person, the time constraint, the organisational history they did not have, and the decision they took
One co-hosted roundtable at a conference, with a named third-party partner and a one-to-one meeting slot for attendees, following the VonSpace format
One milestone checkpoint post, using the laddered-numbers approach from the trillion-token post, to mark a defined point in the campaign
Measurement
Agree up front on what the campaign is being judged on. Comment and poll responses, roundtable sign-ups and one-to-one meetings booked are observable from the campaign itself. Reach into a defined buyer segment is not established by the reviewed material and should not be assumed from follower count.
Negotiate
Because no formats, rates or inquiry route are published, the scope, deliverables and timing all have to be agreed directly, and the brand should confirm in writing what Sahil Aggarwal will and will not front as a vendor-side voice. If the brief needs proof that the audience matches a target buyer list, ask for a follower-demographic export or survey results before signing, and treat the 20,429-follower figure as a scale signal only. Confirm the conference date and partner availability early, since the roundtable depends on them.
Why it works
The evidence shows this style is already how the account communicates: an argument about visibility, a laddered metric, a named adoption story and event programming with a partner slot. The campaign asks for no format outside that set, so nothing has to be invented, and each piece gives the vendor a concrete asset — a poll with real responses, a reference story, a panel recording, a checkpoint — rather than an impression count.
When the campaign needs an insider product-launch voice for an agentic CRM or sales-AI release rather than a customer-side trust argument.
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The reviewed posts are built around Salesforce Sales Cloud keynote announcements: Momentum turning conversations into context, Hunter as an outbound sales agent, and Salesforce in Claude with 37 sales skills in beta.
One post explains Hunter's capabilities in a structured list.
A vendor launching a comparable agent, or a partner wanting a launch-day explainer, gets a named product narrative and concrete feature categories from these posts.
Choose Arvind Seshan over Sahil Aggarwal when the campaign decision is who can explain a new sales-AI product's mechanics and promised workflow, rather than who can argue from the buyer's side that visible AI knowledge earns trust.
The tradeoff is proximity and tone.
Arvind Seshan's material is official-launch framing: it describes what the product will do and why the shift matters, but it does not show an external buyer's adoption decision, a named customer deadline, or a trust objection from a revenue operations team.
It also does not show a milestone ladder or an event-hosting format.
A campaign that needs the vendor challenged, or that needs a co-marketing event with a third party, gets less from these posts than from Sahil Aggarwal's reviewed material.
The reviewed posts carry no audience demographics or sponsorship terms, so reach and availability cannot be assumed.
When the campaign needs a hands-on GTM operator who can teach a signal-based outbound sprint and name the infrastructure failures underneath agent builds.
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The reviewed posts describe a hackathon build where a working AI campaign engine shipped before lunch and the jury still faulted the team for not explaining why it mattered; one post lays out a four-layer GTM system of signal, research, outreach, and RevOps, and links to a full breakdown.
The other post reviews a week in AI, contrasts a Dreamforce mainstage announcement with a Thursday-night builder meetup, names seven builders and their live demos, and says the risk was not the agents but infrastructure debt stacking underneath them.
A brief that wants a practical, build-and-diagnose voice for an AI or RevOps campaign is better served by this than by a product milestone post.
Choose Doug Skinner over Sahil Aggarwal when the decision is who can make the campaign useful to founders and GTM engineers, rather than who can make a vendor's AI trust story tangible to revenue operations buyers.
The tradeoff is focus and evidence shape.
Doug Skinner's posts are wide-ranging: one is a GTM system argument, the other is a week-in-AI roundup that ranges from frontier-lab safety news to live demos to security Q&A.
That breadth can suit a thought-leadership campaign, but it gives less of a single buyer's adoption story than Sahil Aggarwal's Quiambao post, and it does not show a conference-hosted partner event or a milestone ladder tied to one product.
The reviewed posts also do not establish audience demographics, sponsorship formats, or rates, so a campaign that depends on verified reach into senior RevOps buyers would need more evidence.
When the campaign needs a cybersecurity marketing practitioner who can build signals-based GTM content and event community for a security audience.
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The reviewed posts are explicitly aimed at cybersecurity marketers: one invites demand generation marketers to attend Black Hat, explains why showing up matters, and announces co-founding Cusp GTM with Chas Larios to work with cybersecurity startups and founders; the other returns to CyberMarketingCon with a signals-based GTM playbook and argues the next layer is architecting first-party signals rather than only capturing third-party ones.
A vendor selling into cybersecurity, or an event looking for a named community voice, gets a clear buyer audience and a repeatable playbook from these posts.
Choose Jeanette Stepanchuk over Sahil Aggarwal when the campaign decision is who can speak to cybersecurity demand generation and event-led community, rather than who can speak to revenue operations leaders about trusting an AI system's business context.
The tradeoff is audience and offer.
The reviewed material is written for cybersecurity marketers and event communities; it does not show revenue operations AI adoption, product milestone numbers, or a customer story with a named deadline.
The posts also contain no sponsorship terms, rates, deliverables, or inquiry route, and no follower demographics, so a brand that needs a bookable security-audience placement or verified access to a named buyer segment cannot judge that from these posts alone.
The evidence shows subject-matter fit, not commercial availability.
This profile draws on five LinkedIn posts about Von’s product direction, customer use, usage milestone, Dreamforce event and a martech company’s launch.
Two posts are dated September 2026; the other three have no dates in the reviewed material.
The sample does not establish whether books, shows, earlier work or other projects are important to searches for Sahil Aggarwal’s name.
Research into those areas is needed before treating this as a complete account of his public work.
Recent engagement
Across their last 6 LinkedIn posts, the median post got 48 reactions and 3 comments. Checked 2026-11-02.