Marketers of Technology

The Front Lines

Conversations with the marketers building the narratives, brands, and categories that shape technology adoption.

  1. 8h ago

    The Excel Add-In That Won't Call Itself an AI Company | Kieran McQuilkin

    In this episode of Marketers of Technology, Andres talks with Kieran McQuilkin, Director of Product Marketing at Macabacus, the Excel, PowerPoint, and Word add-in used by investment banks, private equity firms, venture capital groups, and other financial services teams to keep deal documents accurate and on-brand. Kieran explains why a company built a decade before generative AI existed refuses to brand itself as an "AI company," how its deterministic, non-AI functions build trust where a single wrong number can mean real financial and legal exposure, and why its AI features run on each client's own LLM connection instead of a proprietary model. He also shares how his small marketing team mines its own sales calls for content research, and why customer webinars and third-party review sites now matter as much for AI-driven search as they do for human buyers. Topics Discussed: Kieran's path from business journalism to product marketing, and how he became Macabacus's first dedicated product marketing hireWhat Macabacus does inside Excel, PowerPoint, and Word for investment banks, private equity, venture capital, and other financial services teamsThe shift from a largely self-serve, product-led business to a blended enterprise sales motion, and what that meant for his roleWhy Macabacus, built as an Excel plugin a decade before generative AI existed, doesn't position itself as an "AI company"The case for deterministic functions over AI guesswork when a single wrong number in a deal document carries real financial and legal riskDeckCheck AI: how it catches contextual errors across a presentation and points directly to the slide and number at faultWhy Macabacus runs its AI features on a client's own LLM connection — Gemini, Claude, or OpenAI — rather than building and hosting its own modelHow the internal marketing team uses AI to connect CRM, call-recording, and ABM data into fast dashboards and segment recommendationsUsing an MCP-connected tool to mine sales-call transcripts for first-draft blog research, while keeping final writing human-ledWhy Kieran is doubling down on customer webinars and third-party review sites now that AI search tools lean heavily on outside validation rather than owned content GTM & Technology Adoption Lessons: Don't let "AI company" become your whole positioning. Macabacus leans on ten years of deterministic, non-AI functionality as its credibility anchor, using AI to complement the product rather than define it.The riskier the output, the more verification matters. As AI makes first drafts faster and more confident-looking, Kieran argues the "last mile" of human and deterministic review becomes more valuable, not less, especially in financial services.Build on your customers' existing AI stack, not your own. Letting clients plug in their own LLM connection avoids the cost, privacy, and trust issues of building a proprietary model.Third-party validation now feeds AI search directly. With LLM-based search citing review platforms and outside voices, customer webinars and sites like G2 and Capterra matter as much for AI visibility as for human buyers.A product-led motion and an enterprise motion can coexist. Macabacus kept its self-serve funnel healthy while building a sales-assisted path for larger, more customized enterprise deals.Use AI to mine your own first-party data for content ideas. Pulling insights from recorded sales calls gives marketing a faster, more specific starting point than a blank prompt. // Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

    The Excel Add-In That Won't Call Itself an AI Company | Kieran McQuilkin

  2. 9h ago

    Why He Walked Back AI-Automated Outbound | Julian Meinke

    In this episode of Marketers of Technology, Andres talks with Julian Meinke, founder and CEO of Postelix, the full LinkedIn engine for B2B teams. After six years leading marketing at two European hypergrowth startups, Prewave and Sereact, taking both from zero to a combined $20 million in ARR and helping raise over $200 million in funding, Julian built Postelix around a single obsession: turning an anonymous target market into a named list of actual buyers. He explains why he deliberately walked back from AI-automated outbound messaging in favor of human-written outreach, how Postelix is evolving into a Slack-based "coworker" that flags buying signals in real time, and why he still has Claude and ChatGPT check each other's work rather than trusting either one blindly. Topics Discussed: Julian's path through fifteen years in marketing, including six years leading marketing at the hypergrowth startups Prewave and Sereact, before founding Postelix roughly six months agoWhat Postelix does: turning an anonymous target market into a named list of real buyers, so companies know exactly who to advertise to and which events or influencers to trackWhy Julian walked back Postelix's original AI-automated outbound messaging feature in favor of human-written outreachHow a named buyer list feeds everything downstream: ad targeting, event selection, influencer partnerships, and sales follow-upPostelix's upcoming shift toward a Slack-based "coworker" that proactively flags buying signals, like a target account's employee viewing a LinkedIn profile after a campaignRunning a two-person company supplemented by "a bunch of virtual" AI agents, including using Claude and ChatGPT to check each other's workWhy Postelix deliberately limits its own go-to-market to two channels, founder-led outbound and LinkedIn ads, rather than spreading across manyHow Postelix prices around the size of a company's target account list rather than tiered feature plansOnboarding new customers by mapping the problem that actually motivates a buyer, rather than relying on static demographic ICP dataJulian's advice for a first marketing hire at a B2B startup: talk to customers directly rather than relying on secondhand input from sales GTM & Technology Adoption Lessons: A named list beats an anonymous audience. Postelix's entire value proposition rests on converting a vague target market into specific identified buyers, which then sharpens every other marketing motion built on top of it.Don't let AI automate your way into the spam folder. Julian deliberately pulled back from automated AI outbound messaging because he believes inboxes are already flooded with it, choosing human-written outreach as a differentiator instead.Buying signals belong where the team already works. Postelix's next iteration moves intelligence out of a standalone dashboard and into Slack, so account signals reach the right marketer or sales rep in the moment rather than waiting to be checked.Pick two channels early, not ten. Julian's rule for an early-stage company: one higher-reach channel and one one-to-one channel, rather than spreading thin across every available tactic.Verify AI output by cross-checking tools against each other. Julian's habit of having Claude and ChatGPT review each other's work is a simple, repeatable way to catch mistakes neither tool would flag on its own.Talk to buyers firsthand, not through secondhand filters. His strongest advice to a new marketing hire: get direct, first-person interviews with customers rather than relying on what the sales team reports back. // Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

    Why He Walked Back AI-Automated Outbound | Julian Meinke

  3. 9h ago

    Predicting a Presidential Speech Almost Word for Word | Cristina Restrepo

    In this episode of Marketers of Technology, Andres talks with Cristina Restrepo, Head of Marketing at Statt, an end-to-end AI workflow platform built for public policy, government affairs, and regulatory teams. As Statt's first and only marketing hire, Cristina built the brand's entire foundation from scratch, then repositioned the company as a purpose-built AI company, borrowing cues from category leaders like Claude, Harvey, and Rogo, to win over a historically tech-averse industry. She shares how predicting a State of the Union address almost word for word became Statt's biggest brand moment to date, how an ongoing midterm election forecast report turned into the company's single largest source of qualified leads, and how a 25-person team uses its own product to build internal workflows that keep marketing on top of the news cycle. Topics Discussed: Cristina's path through fifteen years in marketing and communications at pre-seed and Series A B2B and AI startups, and how she became Statt's first and only marketing hireBuilding Statt's entire marketing foundation from zero: logo, brand, website, SEO, and lead generationWhy the public policy, government affairs, and regulatory audience has historically been underserved by technology, and the apprehension that comes with itRepositioning Statt as a purpose-built AI company by studying how category leaders like Claude, Harvey, and Rogo present themselvesMoving to a consultative model that frames Statt as a customized system woven into a customer's existing workflow and templates, rather than another tool to log intoAutomating "hearing intelligence," a task that used to mean sending an intern or analyst to physically sit in on hearings and take notesHow Statt predicted a State of the Union address almost exactly, using its own policy dataset, and the brand attention that followedTurning an ongoing midterm election forecast report into Statt's single biggest source of marketing-qualified leadsStatt's global data footprint, from deep US legislative coverage to EU, UK, South American, and Asian regulatory dataBuilding internal, Statt-powered workflows, including one that tracks relevant DC-area policy events and another that monitors upcoming elections GTM & Technology Adoption Lessons: Borrow positioning cues from outside your category. Cristina modeled Statt's "purpose-built AI company" positioning on recognizable AI brands like Claude, Harvey, and Rogo, rather than on legacy research and monitoring tools in her own space.Overcome adoption apprehension by reframing the tool as a system, not software. Presenting Statt as something built alongside the customer and integrated into their existing templates opened doors with both technical buyers and more cautious, leaner teams.Predictive content can outperform explanatory content. Statt's State of the Union prediction and ongoing midterm forecast report drove more traffic and leads than content that directly explains the product, by demonstrating data quality instead of describing it.A proof-of-concept report doesn't need to mention your product to generate pipeline. Roughly a fifth of Statt's marketing-generated deals trace back to a report that never explains what the company does.Keep AI output on-brand with a living internal tool. Statt built its own branded, memory-updated internal AI tool so that every AI-assisted asset reflects the company's latest messaging as it evolves.Use your own product internally before asking customers to trust it. Statt's marketing team runs on workflows built from its own platform, both to stay informed and to prove out the product's value firsthand. // Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

    Predicting a Presidential Speech Almost Word for Word | Cristina Restrepo

  4. 1d ago

    Marketing AI, Not Using AI for Marketing | Karolina Kelleher

    In this episode of Marketers of Technology, Andres sits down with Karolina Kelleher, Head of Marketing at Generis, the 25-year-old company behind CARA — an enterprise content and data management platform built for the world's most heavily regulated industries. Karolina walks through how a team of four supports customers across life sciences, government, and financial services, why CARA is engineered to never hallucinate an answer, and the trust-based process behind bringing in an outside ad agency that helped drive a 150% increase in inbound leads. She also shares a clear-eyed view of where AI belongs on a lean marketing team — as an assistant, not a teammate — and why her goal for the rest of the year is to start marketing AI itself, not just using it. Topics Discussed: Karolina's path into marketing: from an English literature degree and a near-miss sales role to becoming Generis's first marketing hireWhat CARA is and how it evolved from a Documentum front-end into an independent, AI-enabled content and data management platformHow CARA's AI framework is built for regulated industries: permission-aware, fully audited, and designed to never hallucinate an answerThe mix of customers relying on CARA: primarily life sciences, plus government, financial services, and other highly regulated organizationsRunning marketing with a team of four: what Generis keeps in-house versus what it outsourcesThe internal audit that led Generis to bring in an outside ad agency, and the trust-first vetting process behind that decisionThe 150% year-over-year increase in inbound leads that followed a broader push to reassess underperforming campaignsKarolina's rules for using AI as a marketing assistant: template-first prompting, tone-of-voice guardrails, and never skipping human reviewWhy she avoids feeding finished, long-form content straight into AI for repurposing, and what she does insteadHer goal for the rest of the year: moving from "using AI for marketing" to "marketing AI" itself, with campaigns built to run for years GTM & Technology Adoption Lessons: Audit before you scale, not after. When ad spend crept up without matching results, Generis's team paused to ask a blunt question — are the ads bad, or are we bad at ads — before bringing in outside help.Vet partners on trust, not just capability. Generis interviewed multiple ad agencies and chose based on which one it actually trusted with a specialized, compliance-heavy industry — a decision that preceded the 150% jump in inbound leads.Treat AI as an assistant, never a team member. Karolina draws a hard line: AI can execute tasks, but it can't be handed ownership of a channel or a decision.Prompt with your own material, not a blank ask. Feeding AI a previously written, on-brand example — not just bullet notes — is what keeps its output from reading like generic AI copy.Use AI to compress research time, not to skip verification. Fast-tracking an unfamiliar sub-vertical is a legitimate use case, as long as a human on the team signs off before anything goes out.Don't repurpose finished assets through AI. Dumping a completed ebook into a prompt for "LinkedIn slides" just regurgitates the same ideas; better to ask AI for new angles and build from there.The next opportunity is marketing AI itself, not just using it. For unglamorous, regulated categories like content and data management, the differentiator is making the AI story itself compelling and durable. // Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

    Marketing AI, Not Using AI for Marketing | Karolina Kelleher

  5. 2d ago

    Turning a personal consulting offer into a free trial | Ryan Pollock

    FrontierGTM helps AI infrastructure, developer platform, and agent companies turn deeply technical products into clear market narratives, stronger customer traction, and sustainable growth. In a recent episode of Marketers of Technology, we sat down with Ryan Pollock, VP Marketing of FrontierGTM, to learn why he thinks building an AI product is no longer the hard part, and how he applies the software free-trial playbook to his own consulting time. Topics Discussed: How a career through Oracle, Google Cloud, DigitalOcean, and Together AI led Ryan to found his own go-to-market consultancyWhy Ryan believes building has become relatively trivial while go-to-market has become the harder problemWho Ryan considers his ideal customer: companies building AI infrastructure, developer platforms, and autonomous or specialized agentsThe "FrontierGTM Rush" offer that gives selected companies free hours of consulting and AI credit before any paid engagementWhy Ryan built a deliberately bold "gold rush" brand identity for a highly technical consultancyWhy Ryan currently spends nothing on paid advertising and is limited by his own capacity, not demandRyan's argument that AI productivity gains should push labor markets toward far more fractional employment GTM & Technology Adoption Lessons: Treat a free trial of yourself as the product, not just the service: Ryan's core funnel mechanic is "FrontierGTM Rush," an application where selected companies get up to three hours of his time and five hundred dollars in AI credit at no cost, explicitly borrowing the software free-trial model and applying it to a fractional consulting engagement. Bet that go-to-market, not building, is now the harder problem: Ryan said pre-trained models have made building relatively trivial for AI infrastructure and agent companies, which is why he positions FrontierGTM specifically around the go-to-market side rather than product development. Prove a technical category doesn't have to look boring: Ryan built a deliberately bold, visually distinctive "gold rush" brand identity for a company serving deeply technical AI infrastructure clients, arguing that even the most technical categories have room for creative, memorable marketing, and cites Apple as proof that technology and creativity aren't in tension. Grow through network and appearances before spending on any paid channel: Ryan said he currently spends nothing on advertising, relying instead on his professional network, direct inbound interest, and appearances like this podcast, because his own capacity, not demand, is the current constraint on how many clients he can take on. Advocate for the business model you're personally betting on: Ryan argues that AI-driven productivity gains should be pushing labor markets toward far more fractional and flexible engagements, and positions his own willingness to work fractionally, ahead of how most tech job postings are still structured, as part of what he's selling. Package pricing around outcome-sized engagements, not fixed retainers: After the free trial period, Ryan structures paid engagements flexibly, from as little as ten hours to as long as ten weeks, matching the scope of the engagement to what a client actually needs rather than a standard retainer. // Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

    Turning a personal consulting offer into a free trial | Ryan Pollock

  6. 2d ago

    Diagnosing what a customer actually wants to build with stablecoins | Debbie Soon

    Privy, a Stripe company, provides wallet infrastructure that makes it easy for any business to let its users hold, move, and grow digital assets like stablecoins and tokens. In a recent episode of Marketers of Technology, we sat down with Debbie Soon, Head of Marketing at Privy, to learn why most of Privy's biggest customers never let their own users know Privy exists, and how the company diagnoses what a customer actually wants to build before recommending a solution. Topics Discussed: How a career through equity portfolio management, live sports, and a crypto startup led Debbie to PrivyWhat wallet infrastructure actually means, and why Privy is completely B2B even though its wallets reach millions of end consumersWhy Privy stays invisible under the hood for most of its largest customersHow Privy diagnoses what a customer is really trying to solve before recommending an integrationWhy product-led growth and developer experience drove Privy's earliest adoptionHow Privy's forward-deployed engineering model turns one customer's request into a roadmap signalWhy a single security incident anywhere in crypto becomes a trust problem for the whole industryHow being acquired by Stripe turned into a concrete product advantage, not just a funding event GTM & Technology Adoption Lessons: Win developers first, then layer on outbound for bigger accounts: Debbie said Privy's early growth was almost entirely product-led, driven by strong documentation and developer experience that created organic word of mouth among builders. Only once the company had scale did it add a proactive outbound motion aimed at larger enterprise accounts. Diagnose the actual pain point instead of assuming the prospect arrives with a spec: Debbie said companies like Ramp, Deel, or Robinhood often reach out to Privy with only a rough sense of what they want to build with stablecoins, so the team's job is to uncover whether the real driver is transaction speed and cost, one non-custodial integration to expand globally, or new revenue from DeFi yield, and recommend the integration shape accordingly. Let white-labeling be a feature, not a limitation: Debbie said Privy is designed to be completely invisible under the hood for most large customers, who want their own branding front and center, while still offering an out-of-the-box UI kit for earlier-stage companies that want to move faster. Treat one customer's custom feature request as a signal for the whole market: Privy's forward-deployed engineering model embeds an engineer directly with a customer to scope and build features that do not yet exist in the product. Debbie said building something for one customer usually means other customers will want it too, effectively turning customers into design partners in a fast-moving category. Absorb industry-wide reputational damage as your own problem to solve: Debbie said any security compromise anywhere in crypto, even one completely unrelated to Privy, creates a stain on the whole industry's trust. Because Privy is a wallet company, the team treats "all engineering is security engineering" as a core operating philosophy rather than a marketing line. Turn an acquisition into a distributed integration advantage, not just a funding event: Debbie said a major priority this year has been leaning into Privy's position inside Stripe, concretely shipping a tighter integration with Stripe's card-issuing business so customers can launch a stablecoin-backed card program in as little as thirty days instead of stitching together multiple vendors themselves. // Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

    Diagnosing what a customer actually wants to build with stablecoins | Debbie Soon

  7. 3d ago

    Why Collective disqualifies forty percent of its own applicants | Nick Knight

    Collective is the first all-in-one financial platform designed to give self-employed entrepreneurs the technology and team they need so they can stop focusing on the paperwork behind their business. In a recent episode of Marketers of Technology, we sat down with Nick Knight, VP of Sales and Marketing at Collective, to learn why member referrals still beat every paid channel, and why Collective turns away forty percent of the people who apply. Topics Discussed: How a growth marketing career at an agency built on quantitative, hypothesis-driven testing led Nick to Collective's earliest daysWhy Collective serves exclusively "businesses of one," single founder, service-based companies across 256 different professionsThe three different onboarding paths a solopreneur takes into Collective's back-office platformWhy member referral is still Collective's most important channel for both acquisition and retentionWhy referral moves inside professional micro-communities rather than across the market at largeWhy Collective deliberately disqualifies forty percent of the people who apply for membershipHow AI lets Collective give thousands of daily prospects a Fortune 500-level personalized experienceWhy Nick's team keeps tearing down and rebuilding its internal AI workflows as models improve GTM & Technology Adoption Lessons: Treat referral as a compounding growth loop, not a one-time channel: Nick said member referrals remain Collective's most important channel for both acquisition and retention, because members who refer others are already satisfied, and the people they refer are themselves more likely to refer again, creating a self-reinforcing loop. Recognize that referral clusters inside professional micro-communities: Nick said referral doesn't move randomly across the market, it travels inside vertical networks: real estate agents refer other real estate agents, and wedding photographers, planners, and florists cross-refer each other. Collective's growth motion leans into those existing professional relationships instead of treating its market as one undifferentiated audience. Disqualify the wrong-fit leads on purpose, even at real cost: Collective turns away roughly forty percent of the people who apply for membership because the platform isn't the right fit for their business. Nick treats this as equally important as scaling the go-to-market motion, since reaching the right people matters as much as reaching more people. Use AI to give every prospect a Fortune 500-level personalized experience at scale: Nick's stated philosophy is that a solopreneur entering Collective's funnel should feel treated the way a Fortune 500 CEO would entering a funnel like Salesforce's, with immediate, highly personalized attention rather than a generic automated email. Keep rebuilding AI workflows rather than treating any version as finished: Nick said Collective's team builds internal AI agents and then regularly tears them down and rebuilds them as underlying models improve, testing continuously and only keeping what the data proves works better than the previous version. Set a single hard number and let the roadmap be wrong along the way: Nick's concrete 2026 marketing goal is a forty percent improvement in conversion rate by December, without a fixed plan for how to get there. He described the process as testing quickly, getting proven wrong often, and iterating based on what customers actually do. // Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

    Why Collective disqualifies forty percent of its own applicants | Nick Knight

  8. 4d ago

    Why In-Person Trust Wins When AI Floods Every Channel

    In this episode of Marketers of Technology, we speak with Annabel Maw, Founding Marketer at Finnovant, about what happened when a playbook that worked for years in the US failed to land in South Africa. Finnovant launched the Phoenix X, a roughly $200 blockchain smartphone that pays owners a monthly share of decentralized processing revenue, into a market where many buyers earn around $500 a month. Six to eight months of influencer campaigns, paid social, and PR produced momentum but no breakthrough. The turning point came from a 30-day in-store activation at Pick n Pay in Cape Town's V&A Waterfront. A weekly lunchtime giveaway, run on paper slips because the store had no cell service, turned a standard booth into a crowd event and showed the team that buyers needed to meet the brand before they would trust it. Annabel explains how that changed her messaging, ad strategy, influencer briefs, and plan for expanding into new countries. She also makes the case for why in-person trust-building matters even more for B2B marketers as AI-generated content floods every channel. Topics Discussed: Why a proven US marketing playbook stalled in an emerging marketUsing a retail partner activation as a live market-validation testTurning a flyer-and-pitch booth into a weekly community eventBuilding trust with buyers who are skeptical of new brands and possible scamsRemoving loaded category language like "blockchain" from customer-facing messagingSimplifying paid social creative and cutting ad spend in an unproven marketSourcing and briefing TikTok influencers through a multilingual in-house hireScaling into new countries through local affiliate networksUsing AI for copy and newsletters while keeping real people in visual content// Sponsors:Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io

    Why In-Person Trust Wins When AI Floods Every Channel
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About

Conversations with the marketers building the narratives, brands, and categories that shape technology adoption.

Information

  • Creator
    The Front Lines
  • Years Active
    2024 - 2026
  • Episodes
    236
  • Rating
    Explicit
  • Copyright
    © The Front Lines
  • Show Website
    Marketers of Technology