the gtm engineer

Listen on SpotifyManthan Patel began working with YC founders two years ago, where he first started using AI agents and LLM workflows. When AI agents gained popularity, Manthan already had hands-on experience, so he started recording and sharing what he was building. In January 2025, he began posting content on LinkedIn and grew his following from zero to 100K in just six months. Today, Manthan runs both an AI automation agency, and a lead gen agency. His agencies offer low ticket courses that over 50,000 people have taken, while also offering white glove agent-building and implementation services.Subscribe for weekly updates on top GTM Engineering content, open roles & moreIn this podcast, we discuss:* How frequency of posting is one of the last remaining differentiators on LinkedIn* How to repurpose content across LinkedIn, Instagram, and TikTok in platform-specific formats to maximize distribution* Building effective AI voice agents for outbound and inbound calls* Automated inbound form enrichment workflows that qualify leads to prevent SDRs wasting time on unqualified calls* Building end-to-end prospecting workflows using Claude MCP* When to use Clay vs. n8n* Self-hosting LLM infrastructure to reduce API costs, maintain control of data, and meet compliance requirementsEpisode highlights:* Manthan grew his LinkedIn following from zero to 100K in six months by posting lead magnets that encouraged comments to amplify their reach. He also posts three to four times per day across different global time zones to maximize visibility and create faster feedback loops on what content resonates with his audience.* Manthan repurposes the same content across LinkedIn, Instagram, and TikTok by adapting it to each platform’s format. For instance, he’ll convert LinkedIn carousel posts into short-form videos for Instagram and TikTok. By being present on multiple platforms he’s able to reach his audiences where they actually consume content.* Manthan ran an automated cold-calling campaign for a vending machine company by scraping local business data, and personalizing AI calls with shop names and addresses. With this personalization along with multiple call attempts, and disclosing upfront that the call was coming from an AI agent, Manthan generated 80 demos across 10,000 leads in 1 month.* Manthan built an inbound form enrichment workflow where prospects submit only their name and email, then an AI agent enriches both personal and company data, feeds it to a second AI that evaluates ICP fit, and only books demos with qualified leads. This prevents SDRs from wasting time on calls with unqualified prospects who fill out forms.* Manthan uses Claude with MCP servers to do prompt prospecting, where he describes research tasks in natural language. Prompted with these research tasks, Claude connects to data APIs like Lusha to find prospect information, and once found, Manthan has Claude add these leads directly to HubSpot. This eliminates the need to manually build workflows or switch between multiple tools for prospecting or tracking.* An emerging trend Manthan sees is self-hosting LLM infrastructure. Compliance-focused clients run models locally on hardware like a Mac Mini to avoid sending their data to big models like OpenAI or Anthropic. This approach allows these orgs to take advantage of AI while preventing data exposure that could trigger compliance audits and license loss for regulated industries, while also reducing long-term API costs for high volume AI usage.Where to find Manthan:* LinkedIn* Lead Gen ManTranscript details:(00:00) Intro and background(02:27) Manthan’s personal branding and agency structure(03:44) Growing his LinkedIn from zero to 100K in six months(05:54) Manthan’s early client work(08:48) Learning LinkedIn strategy(12:30) Building social media presence across multiple platforms(15:12) AI voice agents at scale to drive 80 demos from 10,000 AI cold calls(18:59) Backtesting prompts and handling edge cases(20:10) Manthan’s AI agent tech stack(24:28) Prospects don’t care when disclosing AI upfront(28:12) Inbound AI agents for 24/7 support with conversation history(30:38) Nailing AI call agent prompting(31:58) When to use Clay versus n8n(33:44) Self-hosted n8n for compliance-driven enterprise clients(35:00) Manthan’s favorite workflows(37:48) What 50K people have learned from Manthan’s courses(40:16) Using Claude with MCP servers for prompt prospecting(42:35) Local LLM infrastructure for compliance and cost(44:39) How to get started with workflows, agents, and MCPs(45:33) Favorite underrated tool, growth hack, and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Sylvain Giuliani, growth and ops leader who built GTM motions at Census and now scales AI-native Augment Code, talks hiring technical generalists and building compounding systems. He covers centralizing data in the warehouse, automating account-level outreach, collapsing meeting cycles with AI-driven context, and treating tools as replaceable parts of a warehouse-first stack.

Listen on SpotifyGuillaume “G” Cabane’s career started in the 1990s as a teenager in France running a Mac gaming website that attracted 2,000 daily visitors. That led to an internship at Apple in the early 2000s where G worked in online SMB sales and learned to run experiments disguised as normal campaigns to avoid bureaucratic approval processes. After Apple, G spent time in IT security where he learned about the back corners of the internet and social engineering psychology. G was positioned at the intersection of marketing and technology when growth emerged as a discipline around 2010, allowing him to become an early expert on tools like Segment, when few others understood the space. He ran growth at Segment, before a string of successful stints as VP of Growth or CMO at Drift, Gorgias, and Ramp. Today, G runs HyperGrowth Partners, a collective of VPs and CMOs who advise companies like Reddit, Ashby and Zapier on growth.During this conversation, we talk how G’s background gave him the worldview he has today, his effective growth methodology, whether he believes in GTM Engineers, how AI is changing go to market and more.Subscribe for weekly updates on top GTM Engineering content, open roles & moreIn this podcast, we discuss:* How perfectionism and campaign quality during Apple’s Steve Jobs era shaped G’s marketing philosophy* G’s effective craziness growth framework that combines rapid experimentation and high risk tactics with scientific rigor* Why founders say they want Ramp-level growth but aren’t open to taking the risks needed to get there* G’s outbound gifting experiment that guaranteed replies in order to test whether outbound infrastructure or messaging was responsible for poor campaign performance* Why (and how) growth experiments should test one variable at a time to enable faster learning* G’s favorite AI use cases, what he thinks is just hype, and his predictions for the future impact of AI on GTMEpisode highlights:* During G’s time at Apple, Steve Jobs would demand screens be perfectly aligned so they looked like one line when viewed from the side. This attention to craft and quality became foundational to G’s approach to growth campaigns. He combines artistic perfectionism with rapid experimentation to create work that stands out in the market.* G’s effective craziness growth framework combines rapid ideation with scientific rigor to find outlier campaigns. Growth teams should fail 60 to 80% of the time because high failure rates signal they are chasing experiments that could massively outperform. The key is pairing this bias to action with documented hypotheses, baseline metrics, and thorough postmortems. This combination ensures each failure brings the team closer to finding what works by systematically extracting learnings and narrowing down winning strategies.* Most founders fail to build great growth teams because they cannot distinguish between growth and the rest of their marketing organization. They struggle with having teams that fail frequently because of the contrast to orgs like product marketing, who should be rightly fired if 3 straight product launches go poorly. Unless founders understand these differences and can hold teams accountable in different ways, growth tends to die out as companies scale.* G ran an experiment for a blue collar HR company to diagnose whether infrastructure or messaging was the problem behind poor outbound performance. Instead of a typical gifting campaign, they sent emails to leads asking them to confirm an incorrect address (a few doors down) with a gift arriving tomorrow. Since people love correcting mistakes, and picking up a package at the wrong address is painful, the campaign drove a 15% reply rate and proved that the outbound infrastructure was sound and the problem was messaging/targeting.* When a company wanted to create a gated content marketing asset, G challenged the team to test only the most critical unknown first - whether people wanted the asset in the first place. After shipping a banner and landing page promoting a non existent asset in a handful of hours, they received one tenth as many clicks as predicted, and learned people did not care about the gated content. By isolating to test the critical variable first, they cut the corner and avoided building the full asset they were planning on, saving weeks of work.* G points out that while AI enables smaller and more efficient growth teams by automating work that previously required multiple specialists, it’s still often not the right solution. For instance, when it comes to messaging enterprise leads and top ABM prospects, he hasn’t seen anyone using full AI to write copy, because the risk to reward is not there. Additionally, workflows through tools like Zapier remain critical because they deliver deterministic outcomes, not probabilistic AI results with a chance of failure.Where to find G:* LinkedIn* Hypergrowth PartnersTranscript details:(00:00) Intro(05:37) Learning experimentation and perfectionism at Apple in the early 2000s(09:23) Working in IT security, social engineering tactics, and an early understanding how the internet works(16:52) G’s effective craziness methodology(21:19) Why most companies don’t implement the growth methodology(23:44) A crazy growth experiment that worked and why(30:30) Whether automated marketing is easier or harder today, and ethics around automated outreach(33:27) Standing out in an increasingly crowded market(36:30) G’s failed growth experiments, from Apple bundles to Segment’s product team conflicts(41:49) Building an autonomous growth team that ships quickly by cutting corners on experiments(45:16) How AI enables smaller, faster growth teams(47:39) Why GTM Engineer as a job title signals forward-thinking company culture(49:52) AI in go-to-market: Its current limitations in outbound and the future displacement of sales roles(01:00:43) Favorite underrated software tool, G’s most memorable growth hack, and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyMahau Ma is an operating partner at Sapphire Ventures, a growth stage VC firm focused on B2B software. Previously, he spent 14 years at MuleSoft, including 7 years as VP of Marketing & CMO and several years as SVP of Corporate Strategy. Mahau joined MuleSoft in 2007 as employee number 20 when the company was still figuring out its business model. He helped build the marketing function from scratch and navigated the company through both its 2017 IPO at $300 million in revenue, and the subsequent $6.5 billion Salesforce acquisition in 2018. By the time Mahau left Salesforce in early 2022, MuleSoft was doing approximately $1.5 billion in revenue under the Salesforce umbrella. Currently at Sapphire Ventures, Mahau advises portfolio companies and other B2B software businesses on go-to-market strategy, helping them navigate transitions from product-led growth to sales-led motions, align marketing and sales organizations, and build executive teams.Subscribe for weekly updates on top GTM Engineering content, open roles & moreIn this podcast, we discuss:* ​​The five year up and down marketing journey as MuleSoft figured out its scalable business model* Why MuleSoft transitioned from a developer-led inbound motion to enterprise outbound sales* MuleSoft’s hiring philosophy and selecting for marketers who think about business objectives before marketing activities* Why stage two pipeline is the unifying metric that holds marketing, SDR, and sales teams accountable* How marketing fundamentals don’t change, even as technology advances* Why an organization’s messaging can be just as crucial to differentiate themselves as their product in the AI eraEpisode highlights:* When Mahau joined MuleSoft, the company was open source, and his first challenge was figuring out how to de-anonymize serious users. By gating advanced documentation and community forum access beyond, MuleSoft identified high-potential users while avoiding backlash from the developer community.* MuleSoft’s journey from inbound to outbound took ~five years of experimentation. When the journey began, the software was being used by developers for small tactical projects that still required lengthy sales cycles. After tactics like offering support services didn’t work, they had a breakthrough around 2013, when they began refusing small deals meant for tactical projects until they could open strategic conversations with decision makers with real budgets.* Mahau stayed through years of trial and error because CEO Greg Schott established a non-negotiable principle of building a team you would want to get the band back together with. Even when pulling their hair out, their cultural alignment, plus a shared conviction that they were solving a massive IT problem, kept everyone aligned and motivated to keep testing and help MuleSoft win.* Mahau shares how MuleSoft hired people who thought about business objectives before marketing activities. Using principles from the book Hire With Your Head, they would ask open questions and listen for 15 minutes to deeply understand how candidates approached problems. They looked for whether candidates jumped straight into executing tactics, or started by understanding what they were trying to achieve and why. By having a team aligned around an outcomes first mindset, they were able to create powerful cohesion as they experimented and worked towards accomplishing MuleSoft’s business objectives.* During Mahau’s time at MuleSoft, the pre-sales organization became marketing’s best friend for understanding customers. Because MuleSoft sold technical infrastructure software, they built a strong pre-sales team that covered everything from technical conversations to business outcomes. These teams helped marketing decode customer decision making processes, validate messaging, and understand the incentive structures that were key to whether organizations moved or stalled out on deals.* Category creation around the concept of an application network became a major growth driver for MuleSoft. Enterprises would automatically recognize the need for an application network, and naturally pull MuleSoft into their organizations. Today, grabbing category leadership creates a more critical distribution advantage and defensible moat when AI enables competitors to achieve product parity faster than ever.Where to find Mahau:* LinkedInTranscript details:(00:00) Introduction(04:11) Mahau’s background and the Mulesoft story overview(06:48) Mahau’s initial charter at Mulesoft(9:28) De-anonymizing users(11:19) Finding what users were willing to pay for and navigating complex buyer committees(20:52) What gave Mahau conviction to stay for 5+ years as Mulesoft figured out their growth(23:20) How Mulesoft iterated to find success(26:20) What Mulesoft taught Mahau about positioning and storytelling(29:15) How Mahau built a winning, committed team that could think from first principles(33:13) Growth wins including Dreamforce stunts like the Connect SaaS child actors video that landed on Marc Benioff’s desk(38:16) Current advisory work with Sapphire Ventures portfolio companies(40:12) Go-to-market fundamentals that never change despite technology shifts(41:30) Importance of a GTM strategy and telltale signs of companies without one(46:14) How companies over-rotate on AI tools without connecting to broader strategy(48:12) ABM as an example of what AI makes newly possible(51:45) In the AI era, messaging differentiation can matter as much as product differentiation(53:45) Stage two pipeline as the unifying metric across marketing, SDR, and sales(56:25) Favorite underrated tool and Third Eye Blind growth hackFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyDivyanshi Sharma runs Growth Exe, a GTM consultation and training agency serving 30 clients. She started her entrepreneurial journey selling perfumes and clothes as side hustles in India, where student businesses were uncommon at the time. During Covid, Divyanshi discovered freelancing through content writing, which led her into LinkedIn personal branding, lead generation, and eventually the GTM space. Divyanshi has built a following of 75,000 on LinkedIn by consistently sharing lead magnets, playbooks, and free resources that drive engagement. Growth Exe focuses on auditing client funnels, creating customized roadmaps and strategies, and training internal teams to execute GTM workflows.In this podcast, we discuss:* How Divyanshi uses Reddit marketing as a blue ocean strategy to find honest conversations and convert them into clients* How to reverse engineer viral posts using AI to create compelling content* Divyanshi’s framework for Reddit marketing, including warming up accounts and using F5Bot for social listening* Why Discord is an untapped channel for software companies who can build authentic relationshipsEpisode highlights:* Divyanshi creates lead generating content by finding the top creators in her niche, identifying their highest engaging posts, then feeding those into ChatGPT alongside her internal SOPs and successful client strategies. This process generates both the lead magnet content she shares, and the copy for LinkedIn posts that consistently attract hundreds of comments.* Divyanshi’s Reddit strategy involves finding niche subreddits, replicating viral posts with her own ideas, and moving conversations to DMs. One tactic she uses involves posting a question asking for software recommendations, letting the post gain traction and rank in search engines, then editing it later to feature your own product at the top.* The first four hours after posting on LinkedIn play a large role in determining its long term reach, so Divyanshi recommends having a group of five to fifteen creators in your niche who agree to engage with your posts. The early engagement from people in your ICP drives useful inbound leads.* Divyanshi knows founders who make $100K per month by finding clients exclusively through Discord groups tied to YouTube communities. They join group calls, candidly share their products as beta offerings at cheaper prices, and rely on word of mouth to drive sales rather than running traditional outbound campaigns.Where to find Divyanshi:* LinkedInTranscript details:(00:00) Introduction and Divyanshi’s background(05:36) Reddit marketing as a blue ocean strategy(06:44) LinkedIn lead magnet strategies and reverse engineering viral content(10:05) Why clients buy trending tools without implementation plans(11:54) Using MCP for content creation and social listening(13:38) Reddit marketing tactical frameworks(19:51) How Divyanshi would tackle Reddit if she worked at Rippling(20:59) Discord as an underrated channel for software companies(23:26) AI agents: hype versus reality(26:18) Small LinkedIn engagement groups and the importance of the first four hour engagement window(29:45) Building in public as a lasting digital asset(31:24) Learning resources and communities for GTM engineers(33:47) The traits of Divyanshi’s clients that are adapting to AI(35:49) Experimenting with emerging channels like Substack and Quora(36:54) Favorite underrated software tool and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyAlex Fine is the co-founder of Understory, an all-bound marketing agency serving B2B SaaS companies. Since going full time in October 2023, Alex has scaled Understory from $6K to multiple hundreds of thousands in monthly revenue. The company is a Clay Studio Partner and operates with a team of 26, delivering services across automated outbound, paid ads for LinkedIn/Google/Reddit, and revenue operations. During our conversation, we talk about how Understory finds arbitrage on paid social, how they have built out the team, how Alex automates every manual step of his sales process to close $1M+ ARR per month by himself, and much more.Subscribe for weekly updates on top GTM Engineering content, open roles & moreIn this podcast, we discuss:* How Understory uses Clay for list enrichment to improve their LinkedIn match rates* Why agencies develop GTM engineering expertise faster than internal teams, and how anyone can leverage their learnings* How a LinkedIn post with three likes helped Alex find Understory’s head of GTM engineering, Naufal, and what makes Naufal so effective* The complete automation stack Alex uses to close >$1M ARR per month as the solo rep* Why founders are the best clients to work with, and how to avoid action paralysis at larger companiesEpisode highlights:* Understory uses Clay to enrich ad audience lists before uploading to LinkedIn, improving match rates from 60% to 90%.* Alex discovered Naufal Nugroho, now Understory’s Head of GTM Engineering, from one of his LinkedIn posts describing a system he had built with complex enrichment workflows using 40 different APIs in Google Sheets.* Alex built a complete sales automation stack that handles pre-call research, post-call follow up, and CRM hygiene. Before calls, a Lovable app uses Perplexity APIs to send digestible research to Slack. After calls, an N8N automation reads the call transcripts to determine call type, then generates follow up emails in Alex’s writing style using Claude, creates next steps with timelines, and builds statements of work in PandaDoc. This saves roughly an hour per deal and enables him to handle seven sales calls per day.* Alex’s loves working with founders because they’re willing to break things and test rapidly, focusing on results over pristine brand image.* Understory has implemented MCP connections with Claude on platforms they manage like Instantly and LinkedIn ads. This allows their team members to query campaign performance, identify trends, and generate reports through natural language (even dictated, using Wispr Flow). These systems have proven so valuable, that clients have been asking for Understory to start offering their operating systems as a service.Where to find Alex:* LinkedIn: https://www.linkedin.com/in/theclayguy* UnderstoryTranscript details:(00:00) Intro(03:20) Alex’s background and path to co-founding Understory(06:06) Understory’s evolution from offering LinkedIn ads to full stack GTM services(09:51) Using Clay for paid media(12:40) Hiring philosophy for paid ads roles(15:47) Common GTM gaps at different company stages(19:17) Why companies use agencies(21:24) Why Alex loves working with founders(22:59) Incentivizing play, failure, and experimentation(26:36) How Understory found their Head of GTM Engineering(28:57) Characteristics of the best GTM engineers(32:11) Understory’s onboarding process & company knowledge sharing(34:05) Alex’s complete sales automation stack(37:47) What is currently exciting Alex the most about Understory’s future(43:55) Why not to put GTM engineering in a box(44:42) Favorite underrated software tool, growth hack, and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyBrian Balfour was the VP of Growth at HubSpot, where he helped expand the company from a single to multi-product company, created their CRM offering that became Sales Hub, and led their transition to product-led growth. After leaving Hubspot 10 years ago, Brian founded Reforge to solve the education gap for mid-career professionals, building it into the premier educational platform for product, marketing, and growth teams. Recently, Reforge evolved beyond expert-led training courses to launch an AI-native product suite with four tools: Insights to aggregate customer feedback, Research to run AI-powered interviews, Build to generate and validate product prototypes, and Launch to help teams safely and quickly deploy experiments.Brian interacts with some of the best operators across all of tech as part of his work at Reforge, giving him a unique vantage point into how different orgs are adopting (or failing to adopt) to AI. He has written thoughtful pieces including The Big Squeeze and The Next Great Distribution Shift.Subscribe for weekly updates on top GTM Engineering content, open roles & moreDuring our conversation, Brian shares why most companies are falling short in AI transformation by creating disconnected systems & not being ambitious enough. We talk about the big squeeze and why reaching escape velocity is more important than ever, how the acceleration of product development is fundamentally changing how product and GTM teams interact, and Brian shares some of his thoughts for early-career operators.In this podcast, we discuss:* Why replacing individual workflows one at a time with AI creates disconnected, hacky systems that lose critical context* What the top 5-10% of companies are doing differently to accelerate AI transformation* What Brian means by the Big Squeeze and how it’s forcing startups to achieve escape velocity faster than ever* Which sorts of roles (or parts of roles) are being automated across product & GTM* How AI enables a product velocity that outpaces go-to-market's ability to adopt and distribute* Brian’s early career advice to stand out from the sea of AI slop showing up in job applicationsEpisode highlights:* Brian's product team is shipping so fast that go-to-market can't keep up. This is becoming a common challenge as engineering acceleration moves bottlenecks to other parts of the system rather than necessarily improving overall output.* Brian segments people's AI adoption styles into three groups: leaders who experiment naturally, a middle group needing specific constraints and support who can adopt, and anchors who resist change. Companies taking AI transformation seriously design different strategies for each segment, with some establishing hard constraints like refusing to review proposals without at least 3 AI-generated prototypes.* Go-to-market teams are gravitating towards two distinct ends: systems & infrastructure people handling data + signals, and creative people designing messages and experiences. Most of the work in the middle is getting automated, reducing the number of bottlenecks for GTM or product teams* The Big Squeeze is speeding up an incumbent’s ability to copy, increasing competition, and making it more important than ever for ambitious software businesses to achieve escape velocity - a level of growth & distribution that allows them to build more sustainable moats.* Due to an overwhelming volume of AI-generated job applications, Brian no longer posts every job publicly. Instead, he scouts talent by scrolling social media to find people building and publishing their work. He advises early career professionals to build and share publicly in order to stand outWhere to find Brian:* LinkedIn: https://www.linkedin.com/in/bbalfour* Reforge.comTranscript details:(00:00) Introduction and Brian's background at HubSpot and Reforge(04:45) Reforge's evolution to AI-native product suite and velocity challenges(07:28) AI Frankenstein systems and what companies are getting wrong in AI transformation(14:28) How to get massive returns on AI(20:27) The three types of AI adopters & the discrepancies between C-Suite and ICs(23:08) How AI is driving role polarization(29:07) The recipe for hypergrowth and advantages to PLG(31:57) How Brian defines escape velocity and why it matters(39:42) How the big squeeze paradigm is changing hiring and GTM + product collaboration(44:36) The next great distribution shift(50:13) Career advice for early professionals in the AI era(58:15) Favorite underrated tool, growth hack, and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyAnshul Gupta is the co-founder of Actively, a GTM superintelligence platform for revenue teams. After diving into AI research at Stanford during the early OpenAI days, Anshul decided to take his work and apply it to helping sales and marketing teams be more effective. He argues that while tools like Cursor and Claude Code have materially increased coding efficiency, go to market is lagging behind because of the nuance and complexity behind each business.Actively serves as the brain and connective tissue that handles all of the underlying data that sales and marketing teams generate. They use first, second and third party data to create a single context window for each account that businesses can use to prioritize accounts, create relevant messaging and more.In this conversation, we talk about the Actively thesis, which has important implications on how businesses should think about integrating AI into their GTM motion. We cover everything from how to philosophically structure the “brain” behind your GTM engine to data hygiene and the right GTM structure for AI transformation.In this podcast, we discuss:* The concept of “GTM Super Intelligence” and why it matters* The “horseless carriage” problem: Why simply adding AI to legacy systems isn’t enough* Cognitive architecture: How and why to build systems that mirror the best sales reps’ processes* Why treating the account as a unit to track, store, and iterate on context creates a winning formula* Data hygiene and why “garbage in, garbage out” is a defeatist mentality* The evolving role of humans in go to market* The right org ownership & structure for AI transformationEpisode highlights:* Failure cases of adopting AI in GTM include trying to tack on AI to legacy systems (horseless carriage), using exclusively logic-based intent providers, and not investing in an iterative system.* First ask, “if we had one AE per account, how would the AE approach their role?” and then back into creating a system that gets as close as possible.* There’s no one-size fits all account prioritization or messaging framework. There are materially different, but equally viable ways to do sales and marketing into your top accounts (e.g. going bottoms up vs. tops down).* The garbage in, garbage out mentality is overly defeatist - if your reps have to deal with the data every day, then there’s no doubt layering in AI can improve your data’s impact.* The companies finding the most success with AI transformation in GTM are bringing the top internal representatives together across RevOps, AI, and SDR + marketing.* Anshul and Actively use poor cold outbound campaigns into their business as a signal and trigger to do their own outbound campaigns.Where to find Anshul* LinkedIn* Actively.aiTranscript details(00:00) Introduction(03:21) Anshul’s background and the founding of Actively(06:20) The Actively thesis & product(10:51) The failure cases that come from the smoke & mirrors AI GTM tools(17:52) Buy vs. build when thinking through account prioritization and custom messaging(23:44) Cognitive architecture(27:50) How Anshul thinks about the next best account and the perfect message to send them(33:00) Data hygiene & practical tips for improving data quality(38:26) Non obvious ways that AI will show up in GTM(40:59) Where humans will need to stay in the loop as AI continues to evolve(44:57) The ideal org structure & collaboration for AI transformation(50:07) Favorite underrated software tool, growth hack, & conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyFrank Sondors went from managing a fifty person sales team to building software that enables companies to scale their best salespeople. As CEO of Forge, he's built multiple interconnected products that cover the entire cold outbound stack, from email infrastructure and deliverability to lead generation and AI powered execution. Frank scaled Forge to $3 million ARR in under 12 months while hiring just three salespeople and using AI agents that book 500 meetings per month. In this conversation, Frank shares his contrarian approach to scaling revenue without scaling headcount, and we explore how AI agents are fundamentally changing the economics and strategies of outbound sales.In this podcast, we discuss:* The core problem with existing sales automation software tools and why Frank built Forge* How the modern tech stack can enable companies to scale revenue without scaling headcount* How Forge's AI agents achieve 2% reply rates on cold email* Which sorts of products and use cases work with AI agents, and which do not* How to to identify and automate repetitive work across your org* A number of Frank’s favorite growth hacks that have helped his team scale to $3M ARR* The other decisions and drivers behind Forge’s $0 to $3M ARR growthEpisode highlights:* Frank explains that Forge built N8N workflows to automatically WhatsApp message new signups within two minutes, achieving 10x higher reply rates than email.* Forge uses voice AI rather than SDRs to call leads who don't convert within 14 days in order to gather feedback on why they didn't purchase.* To scale without hiring, Frank's team religiously automates repetitive tasks through a Slack channel called "N8N Ideas", where anyone can request automation. They prioritize automation ideas based on revenue impact and ship new workflows weekly.* Frank's team runs campaigns where half the leads get AI written emails and half get human written emails to see which performs better. Forge then uses these results to inform future campaign copy.* Frank suggests scraping and targeting the LinkedIn followers of your competitors because they have already shown interest in your category. Campaigns targeting these prospects consistently deliver better results than traditional intent signals like funding events or new hires, which everyone else is already targeting.* Despite building outbound software, 80% of Forge's revenue comes from inbound. Frank attributes this to building in public by sharing daily updates about product and company progress, working US hours despite being based in Europe, and offering a website widget that lets prospects instantly call their sales team if they’re online.Where to find Frank:* LinkedIn: https://www.linkedin.com/in/franksondors/* ForgeTranscript details:(00:00) Intro(02:41) Frank's background and the Forge origin story(04:23) The early set of tactics that Forge automated and building an automation culture(11:47) Overview of Forge's product ecosystem(13:50) Using AI agents to book demos(18:05) Forge’s agent success rates and where agents are less effective(21:01) The role of real SDRs at an AI first company(27:10) Automating WhatsApp follow ups(28:47) How Frank thinks about agent prompting, context building, intent signals, and human-in-the-loop(35:05) How Frank would think about finding winning messaging against competitors(39:20) The best agents’ edge is their data layer(47:08) Scaling to $3 million ARR in under 12 months and $1 million ARR without making a hire(49:55) The impact of building in public(52:25) Frank’s advice on building out go-to-market side of an org(56:23) Favorite underrated software tool and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyMaximus Greenwald is the CEO and founder of Warmly, a $5M ARR GTM software tool. Warmly helps businesses identify, prioritize and follow up with their prospects by tracking and ranking intent signals ranging from website visits to third party competitor research.Maximus left his job as a product manager at Google and decided to start a company. After multiple failed iterations, including a tinder for cofounders, and five pivots, Maximus and his team learned sales and marketing from scratch, eventually building out Warmly. Warmly has grown from zero to $5 million ARR in the last three years, and Maximus built the company completely in public for the last year and a half, sharing revenue numbers and strategic insights on LinkedIn.In this podcast, we discuss:* The right number of marketing channels to test each quarter and to build up to over time* Why the team at Warmly structures their marketing teams horizontally, focused on different parts of the funnel, instead of vertically, focused on channels* The rise of signal-based orchestration and the number of days you have to engage with an actively looking buyer before they’re lost* Why Maximus chose to build Warmly in public and the benefits that have come with the decision* The GTM software tool landscape and different ways of building motes within micro verticals* Maximus's favorite growth hacks and tips for building a LinkedIn presenceEpisode highlights:* Maximus intentionally cut off all warm introductions to pressure test their cold outbound campaigns. This taught Warmly what sorts of LinkedIn messages cut through the noise so they could scale the team of SDRs.* Warmly obsessively focuses on 1 channel per quarter while testing the waters on 1-2 others. This allows them to build conviction on which channels to cut out, leave the lights on, and double down in.* Maximus believes AI will create a shift from channel-level specialists focused on things like SEO and paid ads to horizontal generalists who can context shift and hone in on different parts of the funnel (TOFU/MOFU/BOFU).* Maximum decided to build Warmly in public by sharing revenue numbers and strategic insights monthly on LinkedIn. This has been beneficial to drive pipeline, keep employees aligned on the company strategy and to have fun going up against competitors in the ring of LinkedIn.* Warmly has put together step by step guidance to evaluate data providers quality.* There is still significant alpha to be had in most industries by capturing intent signals that indicate a company is in market and quickly following up with the relevant messaging.Where to find Maximus:* LinkedIn: https://www.linkedin.com/in/max-greenwald* WarmlyTranscript details:(00:00) Intro(02:44) The origin story of Warmly's multiple pivots(07:22) The journey to $5M ARR and how Warmly obsesses on 1-2 channels per quarter(12:34) How Warmly made LinkedIn work(14:30) Horizontal vs vertical team structure within marketing(21:54) Signal-based orchestration and how intent signal commoditization is playing out(27:34) Making the most out of your warm lead follow up and targeting niche verticals(33:30) The role of GTM engineering at Warmly(35:30) Why Warmly builds in public and how it has impacted their business(38:01) Maximus’s advice for growing your LinkedIn following(39:12) How Maximus thinks about the GTM software space at large and evaluating different data vendors(44:04) How Warmly uses a tennis analogy to explain how humans should interact with AI(46:55) Underrated tools and the LinkedIn group chat growth hack This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com