1 to 100

1 to 100

Conversations with founders and operators at businesses between 1 and 100 million

  1. 10. Okt.

    The New Playbook with Alex Beller

    In this episode of 1 to 100, Alex Beller from Postscript joins us to talk about rebuilding a scaled SaaS company from scratch with AI coding agents, moving from SMS into a broader marketing platform, and managing the organizational change that follows. Postscript has about 20,000 customers, an application shaped by eight years of work, and roughly 50 engineers. Alex explains why Adam started rebuilding the product alone as an experiment, then turned it into the company’s new direction after seeing what AI-native development could do. The new product, first called Acorn, competed with Oak, the existing Postscript platform. The company eventually decided to move all customers to Acorn and call it Postscript 2.0. Alex says the new codebase created a 10x to 50x improvement in speed and capacity compared with the old system. That rebuild also changed the product strategy. Email is no longer an add-on to an SMS platform. Email, SMS, and other channels are part of an AI-native CRM. Alex explains why starting from scratch made this multi-channel product easier to build. The internal transition was difficult. Some employees were excited by the new direction. Others were skeptical because the original system contained years of their work, including mistakes made by the founding team. Postscript gave people time to adjust, but some employees left and the company made proactive leadership changes. We also talk about the difference between adding AI to an old workflow and building around AI from the start. Alex argues that old CDPs are built around passive, structured data. The new system uses brand context, customer behavior, reviews, surveys, and results from ongoing experiments to create more proactive and personalized messages. Alex expects this to create revenue gains as well as cost savings. He describes a possible future where a small retention team can manage more work without an agency, while the software handles more of the campaign creation and orchestration. Adoption will take time. The product still needs to connect with familiar tools such as a brand’s marketing calendar and help teams move toward this new operating model. The lesson for SaaS founders is that some AI transformations may require a founder-led rebuild from scratch. One high-agency founder can build the new system while the rest of the company runs the existing business. It is painful and demanding, but it may be the fastest way to escape the constraints of an old product and old operating model. Chapters00:00 Cold open: tech at home, staying offline01:53 The co-CEO experiment and Adam going quiet04:10 Rebuilding Postscript from scratch08:03 Migration, the email launch, and multi-channel08:59 Telling the team: skeptics and change management12:30 Managing believers and leavers14:47 A customer churns for email and SMS17:35 Identity, dilution, and the advisory board19:54 Investors, category outcomes, business health23:02 Bolt-on AI versus AI-native26:00 Retention teams, agencies of one, revenue alpha28:48 Adoption stories: FERMAT and Foreplay34:16 Advice for founders: the rebuild playbook39:34 Hiring after the rebuild

  2. 30. Sept.

    From 0 to $100M... Three times with Kyle Hency

    In this episode of 1 to 100, Kyle Hency joins Jack, Jeremiah, and Rishabh to talk about high-agency teams, company structure, AI adoption, and how the nature of work is changing.Kyle describes Chubbies’ early advantage: four highly invested co-founders producing the output of a much larger team. As the company grew to about 125 employees, leadership, structure, and team design became more important.He compares that experience with GoodDay, which operated in a decentralized way for almost three years. With 24 people, nearly 100 customers, and several functions working at once, the company started centralizing decisions. The challenge is adding process without reducing creativity.The group talks about how AI changes management. Tools can provide a faster view of performance, coordination, and even the complexity or usefulness of software features. Managers still need human conversations, but they spend less time sorting through competing versions of events.They discuss the difference between people who need a task list and people who see a problem, take ownership, and finish it. Kyle argues that companies should hire for deep expertise and high agency. Rishabh argues that the number of people who can independently own major initiatives may matter more than company size.The conversation also covers earned equity, outcome-based incentives, and employees who run side projects. The group agrees that side work is not automatically a problem. The real question is whether the employee brings their best work to the company and whether the company has created work worth committing to.They close by discussing software interfaces. Rishabh expects more work to move through agents, CLIs, and MCPs. Jack and Jeremiah argue that visual interfaces will still matter, especially for creative work, while Kyle questions how quickly people can absorb constant UI changes.The lesson for founders is hire people who can own outcomes, use AI to expose reality faster, and add structure without blocking initiative.Chapters00:00 The benefits of being CEO03:59 Scaling Chubbies and building the team05:06 Centralizing decisions at GoodDay06:14 Company size and high-agency people11:29 Founder roles and company structure13:10 AI and team performance25:59 Earned equity and incentives28:23 Side projects and employee commitment36:19 Software interfaces and agent workflows

  3. 22. Sept.

    E67: How to create an AI team with August

    In this episode of 1 to 100, Jack, Jeremiah, and Rishabh discuss AI adoption, token budgets, and how they organize work for agents. The discussion opens with AI-generated documents and the time they take to review. Jack argues that people should check their own work before sharing it. The group talks about setting expectations for quality as teams adopt faster tools. Rishabh describes a conversation with an apparel company’s CFO about AI token costs. He recommends starting with the strongest five percent of the team, learning how they use the tools, and using those results to guide wider adoption. Jeremiah describes giving everyone a basic plan before approving higher tiers. Jack discusses how agents affect hiring for routine work. He values people who take ownership, exercise judgment, and find solutions outside their assigned tasks. He also explains how he organizes AI work into tasks with a clear finish, projects that combine tasks, and systems that repeat work over time. Rishabh compares managing agents to managing people. Agents need clear job descriptions, and those descriptions need updates as their responsibilities change. Jack wants a dashboard that shows the work in progress and whether each automation is running. The group discusses FERMAT’s AI twins, which colleagues can reach through Slack. Rishabh describes asking his twin for feedback on a customer presentation. The twin redirected the work toward customer discovery. Jeremiah connects the quality of that feedback to having a specific point of view. Chapters 00:00 Reviewing AI-generated work 02:55 Token budgets and team adoption 08:35 Hiring as agents improve 10:21 FERMAT’s AI twins 19:52 Tasks, projects, and recurring systems 23:35 Updating agent job descriptions 24:58 Giving agents a point of view 27:33 Choosing tools around the work

  4. 15. Sept.

    E66: Revenue Solves Every Problem with Andrew J Faris

    In this episode of 1 to 100, Andrew Faris joins us to talk about scaling an agency when the details matter, what should be automated, and what still needs a strong operator in the loop.Andrew explains why agency work gets harder as the business grows. There are too many small decisions across media buying, creative, forecasting, client work, and operations for a simple SOP to capture everything. AI can help, but only if the tools preserve the decisions that actually drive performance.We talk about the limit of automation. Something that looks like basic Ads Manager work can include dozens of small choices that change CAC materially. Andrew argues that some of the boring, click-heavy work may still be where a good operator creates value.That leads into a broader conversation about executives doing the work themselves. Rishabh argues that if you are exceptional at something, your job is still to do it. The best founders and executives often remain directly involved in sales, product, creative, or whatever part of the business they are uniquely good at.We also talk about hiring. The bar is not finding people who can follow your Loom videos and SOPs. The bar is finding people who know something you do not, can teach you something, and can improve the way the company works. Andrew adds that great people still need a clear point of view from the business so they know what they are trying to replicate and improve.Andrew describes an agency as operationalized knowledge. The agency needs a real point of view on what works, then systems, people, and software that can apply that knowledge across clients without losing the details that make it valuable.We finish with speed and volume. Rishabh argues that most growth comes from taking more good swings. Andrew agrees that agencies need more creative volume, while still improving quality and diversity at the same time. AJF Growth is already producing hundreds of ads per month and expects that number to keep increasing.The lesson for operators is automate what can be standardized, keep great people close to the work, and increase the number of good decisions your team can make without losing the details that drive the result.

  5. 10. Sept.

    E65: Building an Ad Platform

    In this episode of 1 to 100, formerly the SAAS Operators podcast, Gabriel and Jordi from Publicit join us to talk about making connected TV, digital out of home, and other ad inventory accessible to smaller brands. Publicit started from a simple problem: smaller companies can buy Meta and Google ads in minutes, while most other ad inventory is expensive, difficult to access, or built for large campaigns. Publicit is trying to make those channels self-serve and measurable for SMBs and mid-market brands. Gabriel explains how Publicit evolved from a marketplace idea into its own bidder and DSP infrastructure. The company connects directly to SSPs and publishers, processes hundreds of thousands of bid opportunities per second, and runs an inference on each bid in real time.We talk about what happens as CAC rises on Meta and brands start looking for the next place to spend. Audience moves, and advertisers increasingly need to test CTV, digital out of home, and other channels instead of relying on one platform. Publicit can also take audiences from Meta, CRM lists, and other sources and use them for retargeting on connected TV. That starts to make CTV look more like a performance channel, with targeting and measurement that smaller advertisers can actually use. We also talk about fundraising in Europe, why Gabriel cared about investor distribution as much as capital, and how Publicit used business angels and ad-tech operators to get access to customers before raising from VCs. The lesson for advertisers is Meta does not have to get every next dollar. As CAC rises, more channels become worth testing, especially when they can be bought, targeted, and measured more like Meta.

  6. 3. Sept.

    E64: Profitable Marketing with Bryan Bumgardner

    In this episode of 1 to 100, formerly The SAAS Operators Podcast, Bryan Bumgardner from Northbeam joins us to talk about AI adoption, measurement, software moats, and how Northbeam is growing. We start with dashboards. AI can give you the answer directly, but most companies still want to see the numbers, understand how the answer was reached, and keep the workflows their teams already know. Bryan explains why that makes AI adoption as much a change management problem as a technology problem. We talk about why AI-native startups can change faster than larger companies. New companies can build their workflows around AI from day one. Larger companies have existing teams, systems, incentives, and processes that make the same change much harder. Bryan explains why Northbeam sees part of its job as helping customers move into a more AI-enabled way of working. Northbeam already has the data, measurement systems, and experience of thousands of brands. AI can make that collective knowledge easier for customers to access and use. We also talk about what creates a software moat when product development gets cheaper. Bryan argues that Northbeam's main asset is its data. The company has built identity graphs, device graphs, and petabytes of data that cannot be recreated by simply building a similar interface with AI. Rishabh makes the case that AI creates more value when it helps companies grow than when it only cuts costs. Cost savings have a fixed ceiling. Growth does not. For a company like Northbeam, the opportunity is using its data and intelligence to help customers make better decisions about where to spend the next dollar. Bryan also explains what is working for Northbeam's growth. The company focuses on education, authority, relationships, small dinners, and its Media Buyer newsletter. The newsletter grew from about 2,000 subscribers to more than 100,000 by publishing aggregate performance data and giving marketers a way to compare their results with the wider market. We finish with measurement. Bryan explains why there is rarely one perfectly correct answer to whether a campaign worked. Northbeam combines incrementality testing, MMM, MTA, and experienced media strategists to help brands make decisions with the highest probability of being right. The lesson for software companies is AI makes product development easier, so the value moves toward assets that are harder to copy: proprietary data, institutional knowledge, trusted relationships, and people who know how to turn those assets into better decisions.

  7. 26. Aug.

    E63: The AI War Machine with Rabah Rahil

    In this episode of 1 to 100, formerly The SAAS Operators, Rabah Rahil joins us to talk about what software businesses look like when AI makes development cheaper, increases leverage, and changes how companies buy. We start with the idea that SaaS is done. Rabah argues that this is overblown. Rishabh points out that software consumption is still growing quickly, while companies like Anthropic can produce huge amounts of revenue with far fewer employees than older software companies. The bigger change is the amount of leverage software now creates. We talk about the rise of AI-powered services. Rabah thinks many businesses would rather buy an outcome than another software tool. AI lets agencies and service companies deliver that outcome with much less headcount, while keeping the domain expertise and flexibility that software alone can miss. The go-to-market model also changes by market. Some companies need PLG with very fast time to value. Others need founder-led sales, CEO-to-CEO selling, and a services layer that helps the customer implement the product. Rishabh argues that the middle ground is becoming a difficult place to build. Rabah also explains what he has learned building Doctrine, internally called War Machine, while using it inside a real marketing team. His main lesson is get the product into the hands of real customers as early as possible. A product can feel productive to build, but founders can use product work to avoid the harder job of building the company and selling. We also talk about why distribution matters more as development gets cheaper. A better product can still lose to an incumbent with stronger sales, relationships, and market access. Rabah argues that building software is becoming easier while building distribution remains difficult. The lesson for founders is build for real demand, sell the product that exists, and choose a go-to-market model that matches how your market buys. AI makes it easier to build. It does not make distribution, trust, or customer adoption automatic.

  8. 19. Aug.

    Building David with Brandon Doyle

    In this episode of 1 to 100, formerly The SaaS Operators , Brandon from DAVID AI joins us to talk about building an AI transformation company across e-commerce, construction, law, and other industries. Brandon explains why DAVID AI moved from AI-powered marketing into a broader service model. One client was paying about $48K per month across four different software products. DAVID AI replaced them with one custom system that costs about $9K per month.We talk about why e-commerce can be a harder market to serve. The operators are often early adopters, willing to build internally, and surrounded by platforms like Shopify, Meta, and Google that are already adding AI to their products. In other industries, the gap between what AI can do and what companies are using today is much larger.Brandon also explains why he thinks AI services can serve almost every business. DAVID AI can work with masonry companies, law firms, HVAC companies, roofers, software companies, and e-commerce brands because the core job is finding where AI can remove cost, simplify systems, or improve how the business works. We also talk about what happens as Shopify, Meta, Google, and other large platforms automate more of the work agencies and software companies do today. The role of the service company may move toward strategy, system design, and making sure the automation reflects how the business actually wants to operate. Brandon explains how DAVID AI hires engineers through one-day hackathons. The harder hire is someone who can enter a company, understand the business, identify what should be built, and explain the solution well enough to get the company to act. We also talk about the gap between companies at the front of AI adoption and companies that are still several steps behind. There is a large opportunity for people who understand the tools today to help the rest of the market catch up. The lesson for founders is don't let your network decide which market you serve. The best market may be the one where your product or service creates the most value.

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Conversations with founders and operators at businesses between 1 and 100 million

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