In the high-stakes arena of software-as-a-service, artificial intelligence is rewriting the rules for founders racing to capture market share. Jason Gilmore, CTO at no-code platform Adalo and a veteran with over 25 years building products at firms like Nomorobo and Treehouse, shared his blueprint in a recent O’Reilly Radar interview. As chair of O’Reilly’s Building SaaS Businesses with AI Superstream, Gilmore emphasized starting small but thinking big: “I always start by finding a name that I love, buying the domain, and then creating a logo. Once I’ve done this, I feel like the idea is becoming real.” AI tools like ChatGPT now streamline this, generating names and even logos via Cursor, slashing weeks to hours.

Yet ideation alone doesn’t guarantee traction. Gilmore skips formal market validation upfront, building if the itch is personal: “If the problem is sufficiently annoying that I eventually can’t resist building something to solve it, that’s enough for me.” Post-MVP, he hustles hands-on onboarding, like DNS setup for SecurityBot users, to reveal true needs. This scrappy approach echoes broader shifts, as Constellation Research predicts enterprises in 2026 will favor custom AI apps over bloated SaaS stacks amid rising costs.

From Prompt to Prototype: Accelerating Builds

Before code flies, Gilmore poses one litmus test: “Personally, the question I ask myself is whether I seriously believe I will use the product every day.” A hesitant “yes” means shelve it. His stack—Cursor with Opus 4.5, Claude Code for coding, Laravel Forge for deployment, Cloudflare for security—powers rapid iteration. GitHub issues map the MVP roadmap, pulled via MCP servers into AI coders. This mirrors Fungies.io’s 2026 forecast, where agentic AI and vertical tools dominate, projecting the SaaS market to $1.22 trillion by 2032.

Success metrics demand vigilance. Gilmore warns against launch haste sans monitoring: dashboards tracking trial sign-ups and milestones like Slack integrations for SecurityBot are non-negotiable. On build-vs-buy, he champions frameworks: “I think it’s a tremendous mistake to try to reinvent the wheel.” Laravel and Django handle scalability, freeing focus for acquisition. X discussions amplify this, with a16z noting AI startups hit $5M ARR in nine months versus traditional SaaS laggards.

Securing the AI Core: Prompts and Vigilance

AI introduces unique pitfalls. Gilmore stresses prompt engineering to curb “response drift,” testing against diverse data and favoring Hugging Face’s 2.2 million models over generic OpenAI APIs. Output monitoring, human-in-the-loop reviews—as at Nomorobo for robocalls or Adalo for app prompts—ensures reliability. Transparency on data use with third-party LLMs builds trust. Sobonix highlights cloud-native architectures like Kubernetes amplifying these needs in 2026.

AI shines augmenting non-AI tools too: generating scripts or schema reviews. Enterprises echo this caution, per Qrvey, where embedded analytics and gen AI boost outcomes but demand integration savvy. Forward-deployed engineers, as Constellation flags, become essential for bespoke fits.

Launch Tactics: SEO and Hustle Dominate

Launch hinges on two pillars: SEO supremacy and shameless promotion. Gilmore prioritizes Google organic traffic, tuning pages for AI crawlers amid rising GEO. Cursor automates feature landing pages, like SecurityBot’s Broken Link Checker, driving all early traffic. “Overcome founder shyness: Be vocal about your product,” he urges, sharing with networks first. BetterCloud notes platforms overtaking point solutions in 2026, with unified management key.

Scaling exposes the crux: balancing acquisition and retention. Feature creep tempts, but “max MRR” looms where churn offsets gains. X founder vas warns generic AI SaaS fails enterprises craving exact workflows, pushing custom agents like Varick’s Palantir-style builds. Monetization favors simple subscriptions over complex usage-based, easing onboarding.

2026 Horizons: Agents, Verticals, Moats

Trends point to agentic dominance. Gartner via Digital Applied sees 40% of enterprise apps agent-powered by year-end, though 40% of projects may flop on costs. Vertical SaaS surges, per MindInventory, with AIaaS democratizing access. Funding booms, Qubit Capital reports, fueled by gen AI personalization.

Build costs plummet—OpenAI’s o3 cuts by 80%—sparking indie floods, a16z observes. Moats shift to data, memory, context graphs. Klarna CEO Sebastian Siemiatkowski on X: “Separating your data and workflows across multiple apps is a thing of the past.” Acquisitions thrive, Acquire.com listings show AI SaaS fetching 5x profits.

Enterprise Realities: Custom Over Commodity

X threads reveal jadedness toward one-size-fits-all AI SDRs or bookkeepers. Enterprises demand forward-deployed tweaks, per vas. Amplitude’s Spenser Skates pushes rebuilding UIs on SaaS priors, not chat-only. Rohit Mittal distills vertical wisdom: domain trumps AI, compliance claims 40% roadmap, land-and-expand morphs.

Notion’s $600M ARR, half AI-driven, proves incumbents adapt. O’Reilly’s Superstream lineup—from Dynatrace to DBGorilla—promises phase-by-phase tactics. As Journeybee frames, efficiency via RevOps 2.0 and ecosystems define winners.

Founder Playbooks: Velocity and Resilience

Vatsal Sanghvi advises distribution-first: “Think distribution before features. ‘If I build this, will users talk about it on X?'” Ship fast, hoard data moats. freeCodeCamp tutorials even guide full-stack AI email SaaS with Next.js and Stripe. Yet, as Harry Stebbings praises Jason Lemkin’s pivot, mastery lies in wielding AI limits.

For insiders, 2026 demands agentic bets, vertical depth, ironclad security. Custom triumphs over generic, velocity over perfection. Gilmore’s ethos endures: solve your pain, hustle the rest.