How software companies market and sell in B2C, B2P, B2B bottom-up and B2B top-down, and how AI search changes each one.
For a long time, if you were an engineer or a technical founder, you could safely treat Go-To-Market (GTM) as an annoying wrapper around the actual work. You built the software - every clean abstraction, a fast database, the model weights - and then some other department bolted on a layer of Google AdWords, "What is X?" SEO spam, and an army of twenty-two-year-old SDRs blasting cold emails. It was a very noisy, very lossy, human-to-human message-passing protocol.
Over the last twelve months, that protocol broke, and it’s being replaced by something much closer to systems engineering.
The core phase shift heading into 2027 is simple: the primary consumer of your marketing is no longer a human browsing ten blue links; it’s an LLM running a retrieval pass inside a context window.
When 51% of B2B buyers start their research inside ChatGPT, Claude, or Google’s AI Mode, and when developers type "add auth and a Postgres database" into Cursor or Claude Code and let the coding agent pick the vendor, your website is no longer a brochure. It has to become an API endpoint for an inference engine.
If you look at how an LLM evaluates a software vendor during a deep research pass or an agentic tool-use loop, the shortfalls of 2022-era marketing become obvious. If you hide your pricing behind a "Contact Sales" button, the model isn’t going to book a demo. It queries its training data and the live web, pulls an outdated number from a competitor’s comparison blog or an AWS Marketplace listing, and hallucinates your unit economics - usually unfavorably. If you lock your best architecture benchmarks inside a gated PDF to farm MQLs, the crawler hits the form, returns a 403, and cites your competitor’s open webpage instead. If your documentation has bundled, opaque pricing tiers, a coding agent literally cannot reason about the cost function and defaults to a vendor like Neon that exposes clean, per-unit primitives and provisions in a single API call.
At the same time, the human side of the funnel underwent a massive adversarial collapse. Everyone got access to the same frontier models at the exact same time and immediately used them to flood every low-cost channel with synthetic slop. Autonomous AI SDRs spammed inboxes until reply rates flatlined at 3.4% and mailbox providers nuked the domains. SEO farms generated millions of "ultimate guides" just in time for AI Overviews to cut organic click-through rates in half. And in February 2026, Wall Street finally did the math on per-seat pricing: if one human driving an agent does the work of ten seats, you need nine fewer licenses.
Because synthetic text is now free, human attention only responds to high-entropy signals that are expensive to fake: high-volume, messy short-form video from real practitioners in B2C and B2P (Business to Prosumer), and verifiable, un-gated proprietary data in enterprise B2B. Meanwhile, agents only respond to structured, machine-readable ground truth.
What follows is the SELF map of how software is actually discovered, evaluated, and bought heading into 2027 across four distinct markets - B2C, B2P, B2B bottom-up, and B2B top-down. Think of this guide as the new system architecture for distribution: how to write your public state for the models, how to build high-bandwidth proof for the humans, and how to re-architect your pricing and GTM stack for a world where agents sit on both sides of the transaction.
Write your website for a model first. In G2's survey, 51% of B2B software buyers start research in a chatbot more often than in a search engine; in 6sense's, B2B buyers first contact a seller 61% of the way through the journey. Most buyers who act on an AI answer visit later by typing your URL or searching your brand, so analytics tools don't credit AI for them. Teams should write every product, pricing, security and comparison page so that a model can answer questions about the company from it without guessing, then edit it for the human reader. The case against: AI referrals still account for about 1% of sessions on most sites; some categories still win most of their buyers on Google.
Publish your prices. Wynter asked 50 senior leaders at B2B SaaS companies in April 2026; 62% kept every price behind "contact sales." Buyers get a number anyway. Omnius researchers asked ChatGPT about ten customer service vendors that hide their prices and got a figure back for every one, pulled from cloud marketplace listings, rival blogs, pricing aggregators and the model's own estimates. In a Growth Unhinged and Profound study of about 7,600 AI answers to pricing questions, researchers found negative pricing language in 67 of 76 answers about a vertical AI company with no public pricing page. Sellers who can't publish a list price should publish a range, the unit and what drives the cost. The case against: the Omnius test covered one engine, one category and one country; enterprise sellers also worry, with reason, that a public price becomes the starting point in every negotiation.
Stop writing "what is" blog posts. Google answers informational queries on its own page; Ahrefs measured 58% fewer clicks on the top result when an AI Overview appears. Content teams should move that budget into reference pages (pricing, comparisons, integrations, security, implementation) and into original data. Ramp built two pages on accounts payable software for small and large businesses; Profound reports that Ramp earned more than 300 AI citations from them within weeks (see the Ramp teardown). The case against: a few categories still earn informational clicks; educational writing still builds an audience when it carries a real opinion or new data.
Don't build an SDR team in 2027. Buyers cancelled autonomous AI SDRs in large numbers; human cold email averages a 3.43% reply rate. Bottom-up companies find better accounts in their own product usage than in purchased lists. Top-down sellers need a few senior people sending fewer, better messages. Founders should hire a GTM engineer to build a system that finds accounts showing buying signals, and let account executives write and send. The case against: companies selling into markets with no inbound demand and no product usage still need people prospecting full time; the SDR role has also long been where companies train their future account executives.
Gate nothing but tools. Buyers who ask a model about your category never see what sits behind a form; many also resist forms for generic content. Publish the findings, the method and the charts on an open page. Ask for an email only in exchange for something the buyer can't get elsewhere: the full dataset, an editable model or a personalized report. The case against: gated content still brings in many companies' leads; teams measured on MQLs will see that number fall before brand search and win rates rise.
Start in B2P if you're starting from zero. Professionals buy in a day on their own card, find tools through video and creators and bring them to work. Cursor and Lovable both started there and went on to win enterprise contracts. A new B2C app competes with apps launched before 2020, which still earn 69% of subscription revenue; a new top-down vendor needs years of trust it doesn't have yet. The case against: model providers keep shipping features that overlap with prosumer tools; RevenueCat found that AI apps lose subscribers faster than other apps.
List on the cloud marketplaces before you hire your next enterprise rep. Omdia expects software sales through the AWS, Microsoft and Google Cloud marketplaces to grow from $30 billion in 2024 to $163 billion by 2030. Companies have committed about $470 billion upfront to those three clouds and can spend part of it on marketplace purchases, so buyers can pay for a listed product with money they've already budgeted. In the Omnius test, 15 of the 38 first price figures ChatGPT users received came from marketplace listings. The case against: listings take engineering and legal time; the marketplaces charge fees; vendors rarely sell much through a listing without co-sell work.
Marketers got even less in 2026 from most of the channels that declined in 2025. Searchers click fewer links. Cold email recipients reply less. LinkedIn shows company page posts to very few followers. Founders who launch on Product Hunt get about a day of attention.
Three groups also changed how they behave.
Buyers now research in AI models. Software buyers now use ChatGPT, Gemini, Claude, Perplexity and Google's AI Mode to build shortlists, compare vendors and draft business cases before they speak to anyone. In G2's survey, 69% of buyers said they chose a different vendor than they first planned because of what a chatbot surfaced. Forrester's survey of nearly 18,000 buyers found 94% use AI somewhere in the purchase.
Investors turned against seat-based pricing. Anthropic launched Claude Cowork in January. On February 3, Goldman Sachs' basket of US software stocks fell 6% in a single session, and the SaaSpocalypse selloff erased hundreds of billions in software market value over the following weeks. Investors had worked out that if one agent does the work of ten people, a customer needs fewer licenses. Software companies responded by rebuilding their pricing around usage, credits and outcomes.
Developers let agents pick vendors; AI companies built ways for agents to buy. Developers let coding agents pick their database, email provider and auth library. OpenAI put ads inside ChatGPT. Google and OpenAI each shipped a protocol for agents to buy things. Vendors now compete for a recommendation inside an answer, a default inside a code editor and a slot inside an agent's tool list, as well as for a human's attention.
Founders also learned that consumers and professionals find software on TikTok, Reels and Shorts, while enterprise buyers need evidence before they buy.
Founders can no longer treat software marketing as one market with several motions. They should pick a motion based on who pays, how much they pay and how many people have to agree.
| B2C | B2P | B2B bottom-up | B2B top-down | |
|---|---|---|---|---|
| Who pays | A consumer, for themselves | A professional, on a personal or expense card | A team lead or developer, on a company card or small invoice | A company, through procurement |
| Typical price | $5 to $20 a month | $20 to $200 a month | $10 to $50 a seat, or usage, up to roughly $25K a year | $50K to seven figures a year |
| Who decides | One person, in minutes | One person, in a day or a week | One person adopts; a team spreads it | A buying group of a dozen or more |
| Time to buy | Same session | Days | Weeks | Six to twelve months |
| Primary engine | Short-form video and UGC | Short-form video, creators, launches, product virality | Product, docs, agent defaults, community | Shortlist presence: content, research, proof, sales enablement |
| AI search role | Product discovery and comparisons | "Best tool for X" answers, reviews | Comparisons, docs retrieval, coding agents choosing vendors | Shortlist building, vendor comparison, business cases |
| Examples | Duolingo, Headspace, calorie and fitness apps | Granola, Superhuman, Raycast, Lovable, Cursor in its early years | Notion, Linear, Supabase, Resend, Calendly | Salesforce, ServiceNow, Workday, security platforms |
The founders behind the fastest-growing software companies of 2026 started in one market and moved into the next. Cursor sold $20-a-month plans to individual developers, watched those developers spread it through engineering teams and then signed enterprise contracts; by its reported $2 billion run rate in early 2026, enterprise customers made up most of its revenue. Lovable started with non-technical founders paying personally and moved into large companies. Founders who move up tend to follow the same path. They win users in B2P, win accounts as those users spread the product through teams, and win contracts with a top-down motion.
Buyers in each market respond to different marketing; founders who keep the old market's marketing after they move up stop growing.
Buyers in every market now also find products through AI search.
Searchers now read the answer on Google's page and leave. Ahrefs compared 300,000 keywords between December 2023 and December 2025 and found that searchers clicked the top-ranking page 58% less often when Google showed an AI Overview. Seer Interactive measured organic click-through on informational queries with an AI Overview falling from 1.76% to 0.61% between mid-2024 and late 2025. Pew's panel found that people clicked an organic result 8% of the time when an AI summary appeared and 15% of the time when none did. SparkToro estimated that 68% of US Google searches in early 2026 ended without a click.
Google reaches more people with AI answers than any chatbot company does. At I/O in May 2026, Google said AI Overviews reach more than 2.5 billion monthly users, and that AI Mode passed one billion monthly users a year after launch with query volume more than doubling each quarter. People type AI Mode queries about three times longer than classic searches and ask more follow-ups.
Some sites lost more traffic than others. Across 16,661 sites, SEOcrawl found that sites lost 15% of their Google organic clicks year on year; the median site lost 20%; 69% of sites got fewer clicks than a year earlier. Searchers still click on branded, transactional, pricing and local queries. They stopped clicking on "what is" and "how to" queries. Publishers who built businesses on informational traffic lost the most: by Growtika's Ahrefs-based estimates, Digital Trends fell from 8.6 million monthly organic visits to about 269,000 between March 2024 and January 2026.
Marketers shouldn't assume a straight-line decline. Seer saw click-through on AI Overview queries recover from 1.3% in December 2025 to 2.4% in February 2026 as Google adjusted the feature.
Companies that earned pipeline from top-of-funnel SEO lost much of it.
G2 surveyed 1,076 B2B software buyers in March 2026:
Other studies found the same. Forrester's 2026 survey of nearly 18,000 business buyers found that 94% use AI somewhere in the purchase. 6sense's 2025 Buyer Experience Report, built on roughly 4,000 buyers, found:
Gartner's surveys show that buyers still want a person to check with. In a survey of 646 buyers published in March 2026, 67% said they prefer a rep-free buying experience and 45% had used AI in a recent purchase. In May, Gartner reported that 69% of buyers prefer to check AI-generated insights with a sales rep; 51% said they are more likely to run into misleading information from generative AI. Forrester puts the typical buying decision at 13 internal stakeholders plus 9 external influencers.
Put together, buyers want to do the research alone, with a model, and then check the result with a human they trust.
Instantly's 2026 benchmark, built on billions of cold emails sent during 2025, puts the average reply rate at 3.43%. Senders in the top quartile clear 5.5% and the top 10% clear 10.7%. Recipients sent 58% of all replies to the first email in a sequence. Belkins measured averages of 6.8% in 2023 and 5.8% in 2024 using one method.
Vendors spent 2025 selling autonomous AI SDRs: agents that find prospects, write emails, send them, read replies and book meetings with no human involved. Many buyers cancelled within a year. Most teams failed the same way. They pushed volume, got their sending domains flagged as spam within months and collected replies nobody followed up properly; prospects learned to spot AI-written email quickly. Reporters questioned the revenue claims of 11x, the best-known vendor in the category. Most teams that stayed with AI moved to a hybrid model in which AI researches and drafts and a human reviews and sends.
Jason Lemkin replaced most of SaaStr's sales team with about 20 AI agents run by roughly one and a quarter people and reported better results than the human team. SaaStr also has one of the largest inbound audiences in B2B software, so its agents mostly email people who already know SaaStr.
Mailbox providers also tightened their rules. Gmail began rejecting non-compliant bulk mail outright in late 2025; Microsoft enforces the same SPF, DKIM and DMARC requirements for high-volume senders to Outlook addresses.
LinkedIn shows organic company page posts to a small share of followers; practitioner estimates put it under 2%. Members watched 36% more video on LinkedIn year on year; LinkedIn said video uploads rose 20% in the last quarter of 2025.
LinkedIn now lets advertisers pay to promote posts by individuals. Advertisers can promote employees' posts as Thought Leader Ads, and in March 2026 LinkedIn opened sponsored creator content to advertisers through Campaign Manager and began paying creators through Stripe.
TikTok's US business changed owners in January 2026. The TikTok USDS Joint Venture, with Oracle, Silver Lake and MGX each holding 15% and ByteDance keeping 19.9%, took over US operations and began retraining the recommendation algorithm on US user data. ByteDance kept the advertising and e-commerce business. Marketers expected reach to shift while the retraining ran; most kept spending.
Meta and Google spent 2026 automating choices that media buyers used to make.
When the platform picks the audience, advertisers can compete on only two things: creative and conversion data. Teams that give the platform dozens of distinct creative concepts a month and send it clean, deduplicated conversion data get better results.
Investors sold off software stocks in February 2026 because they expected customers to buy fewer seats as agents took over work. Vendors changed their pricing. Salesforce offered flat-fee agentic license agreements. Zendesk had already started pricing by resolved ticket. HubSpot, ServiceNow and Docusign rebuilt billing around credits.
Cruxy surveyed 300 SaaS CEOs in April 2026: 97% planned to retire seat-based pricing within two years; 94% said seat pricing matches the value of their product today. A separate survey of more than 230 SaaS companies found that hybrid pricing, a seat or platform fee plus usage or credits, made up the largest group at 37%. PwC and m3ter found that 52% of software companies run usage-based pricing in production; 72% of those combine it with a subscription. On the buyer side, SaaSRise estimates that usage-based contracts still account for only 4% to 6% of software spend.
Buyers still budget in predictable units. Vendors who sell outcomes still need a number the CFO can put in a spreadsheet. As a result, vendors have to explain a unit of value (a credit, a resolved ticket, an agent run) on the pricing page clearly enough that a buyer, and a buyer's AI model, can estimate next year's bill.
A top-three finish on Product Hunt still drives signups, backlinks and newsletter mentions.
Founders now launch with a professionally produced video posted on X from their own accounts. Flowjam, which makes launch videos, tracked 1,172 startup launches on X over twelve months: the median launch drew 28,258 views, 30.6% passed 100,000 and 7.9% passed a million. Founders now pay agency rates for these videos; Cluely's widely copied launch video reportedly cost about $140,000. Founders with AI products learned to show a real input and a real output within the first fifteen seconds, because viewers scroll past abstract promises.
Founders can now build software cheaply. RevenueCat counted about 2,000 new subscription apps launched each month in January 2022 and 14,700 in January 2026. Apps launched before 2020 still earn 69% of subscription revenue; apps launched in 2025 or later earn 3%. Founders of top-quartile subscription apps grew monthly recurring revenue by 80% or more year on year, while founders in the bottom quartile lost more than a third of theirs.
B2B buyers also build their own tools. In Retool's 2026 Build vs. Buy report, 35% of enterprises said they had already replaced some SaaS tools with custom software.
| Channel | 2026 state | 2027 state |
|---|---|---|
| Google organic, informational | Flooded with AI content | Answered on Google's page; clicks roughly halve where an AI Overview appears |
| Google organic, commercial and branded | Competitive | Still earns clicks, especially comparison, pricing and "alternatives" queries |
| AI answers | Emerging | Where half of software buyers start research; small volume, high intent |
| Cold email | 3.43% average reply rate | Same average, wider spread; human-sent, signal-led outbound works and autonomous outbound doesn't |
| LinkedIn company pages | 1% to 2% reach | Mainly for running ads and as a credibility check; individuals and creators get the reach |
| Short-form video | Growing | The main channel for B2C and B2P |
| Paid social and search | CPCs rising | Automated by the platforms; creative volume and conversion data decide results |
| ChatGPT ads | Rumored | Live, expensive, little measurement data |
| Product Hunt | Gamed | One stop in a launch; a launch video on X gets more reach |
| Review sites | A destination for buyers | A source for AI answers more than a destination |
| Seat-based pricing | Default | Under pressure; hybrid seat-plus-usage is the norm |
OpenAI reported 900 million weekly ChatGPT users in February 2026. Google reported more than 2.5 billion monthly users of AI Overviews and one billion of AI Mode, and said Gemini passed 900 million monthly users. Perplexity sits around 45 million monthly users. Buyers in every one of the four markets now use at least one of these products to decide what to try.
Most visitors who click through from an AI answer come from ChatGPT. Estimates run from about 75% to more than 90% depending on who measures and which sites they sample. Users of Claude and Gemini sent the fastest-growing share during 2026, from small bases.
Few visitors come from AI answers. SEOcrawl's study of 16,661 sites found 1.25 AI-referred sessions for every 100 Google organic sessions; AI referrals reached 0.42% of all sessions in August 2026. B2B SaaS companies see more: audits in early 2026 put AI referrals at 2% to 8% of their organic visits.
The visitors who do come convert well. Ahrefs reported that AI search sent 0.5% of its visitors and 12.1% of its signups. Vercel's CEO has said ChatGPT refers around 10% of new Vercel signups. Several 2026 studies found AI-referred visitors converting at higher rates than non-branded organic visitors, though results vary by industry and by engine. One analysis of 53 B2B SaaS brands found AI-referred cybersecurity leads converting to sales-qualified at 20% against 81% for organic leads, so teams should check their own lead quality rather than trust a headline average.
Most buyers who act on an AI answer never show up as a referral. A buyer reads an answer that names you, then types your URL or searches your brand a day later. You'll see that visit as direct traffic or branded search. Teams that want to see it should track brand search volume, add "an AI assistant (ChatGPT, Gemini, Claude...)" to the "how did you hear about us" field, and compare both against how often models mention them.
Other people wrote most of what models cite about you.
Software companies now write for two readers at once.
Agents and models. Buyers send them to your public pages to answer a question, build a shortlist, fill in a comparison table or decide which API to call. Give them complete, current, consistent facts to quote: what the product does and for whom, pricing or at least pricing ranges and units, limits, integrations, security and compliance posture, implementation time, how it compares to named alternatives, and what changed recently. Put those facts in text, tables and transcripts. You can't reach this reader through a demo call, a gated ebook or a sales deck.
Humans. Buyers read your sales material to decide whether to trust you, defend the purchase internally and imagine the result. They want proof, story, specifics about companies like theirs and someone to call.
A content team in 2027 writes public reference material so complete that a model can answer any reasonable question about the product without guessing, and writes sales material so specific that a champion can get the purchase approved without the vendor's help.
Developers let coding agents choose vendors. Developers now type "add email" or "add a database" and let Claude Code, Codex or Cursor pick the provider. Armature ran 16,893 coding-agent sessions across those three tools and found that developers got the same vendor for the same task only 42% of the time. Developers who let the agents choose got Neon in 66% of database picks, ahead of Supabase; Supabase lost some picks because the agents found its bundled pricing hard to reason about. Mailgun lost picks to Postmark once the agents read its free tier's one-day log retention. Developers using Claude Code got a hand-built solution instead of a vendor in 19% of cases, against about 10% with the other two. Developers who gave the same email prompt in different programming languages got different providers. (Armature sells growth services to developer-tool companies, so read the numbers with that in mind.) Bottom-up vendors now write their docs, pricing pages and starter templates for an agent's judgment as well as a developer's.
Vendors now use MCP servers to reach users inside AI tools. Every major model provider and coding tool now supports the Model Context Protocol. Public registries now list thousands of servers. SaaS vendors stopped waiting for third parties and shipped their own servers, so that a user inside Claude or ChatGPT can query, update and act in their product without opening it. OpenAI opened an app directory inside ChatGPT and in July 2026 folded it into a unified plugins directory. G2 connected its verified reviews to Claude through MCP so that the model can cite them.
Platforms started letting agents buy, slowly. OpenAI and Stripe launched the Agentic Commerce Protocol and Instant Checkout in ChatGPT in September 2025 and opened it to all US users in February 2026. A month later OpenAI deprioritized in-chat checkout and moved toward product discovery with checkout on the merchant's own site. Google launched the Universal Commerce Protocol with Shopify, Etsy, Wayfair, Target and Walmart in January 2026, turned on checkout inside AI Mode in February and announced a cross-merchant cart at I/O in May. Gartner predicts that AI agents will mediate 90% of B2B buying by 2028; Forrester expects a fifth of B2B sellers to face agent-led quote negotiation during 2026. Treat both as forecasts. Sellers can prepare cheaply: publish machine-readable pricing and plan limits, let agents create accounts and start trials through an API, and answer standard security questions on a public page.
Teams in every market should run these steps.
RevenueCat's 2026 State of Subscription Apps report covers more than 115,000 apps and $16 billion in revenue. Its findings show that users decide quickly whether to pay.
Founders have to promise in the ad only what the first session delivers.
Consumers discover apps in TikTok, Instagram Reels and YouTube Shorts. YouTube says Shorts averages more than 200 billion daily views. Founders can't explain an app in a static ad or a blog post as well as a 20-second video of a person using it.
Founders who do well in B2C post short-form video every day rather than in campaigns. They post across all three platforms, because TikTok's US ownership change in January 2026 showed how quickly a single platform can change.
Practitioner case studies from 2026 describe the same setup at many apps, from small indie apps to studios running dozens of titles.
Brands now pay clippers per view. A brand puts up a budget, sets a rate per 1,000 verified views and supplies source footage; anyone can cut clips, post them on their own accounts and get paid on views the platform's API confirms. Public campaign boards in mid-2026 listed rates from about $0.30 to $6 per 1,000 views, with most between $1 and $5. Whop's marketing director told Digiday the company had passed 3.5 billion views through clipping and was averaging more than 100 million a day. Platforms cap payouts per clip, so brands get views past the cap free.
Founders get results from clipping when they have footage worth cutting: a founder who talks well, a podcast, a product with a visual result. Clippers post whatever earns a payout, so brands need clear creative rules. Brands also have to watch for view botting and make sure clippers disclose the sponsorship.
Tools such as Arcads, Creatify and HeyGen generate UGC-style ads with AI actors and voices. Performance teams use them for testing: they generate dozens of hooks and angles with AI, find the two or three that work and then pay human creators to remake those concepts. Audiences got tired of synthetic content in 2026; advertisers report demand for messier, obviously human video. TikTok and Meta both require or apply labels on realistic AI-generated content, so teams should disclose.
US iOS apps can now link users to web checkout after the Epic v. Apple ruling. Founders hoped to save Apple's 15% to 30% commission. They got mixed results. In December 2025 the Ninth Circuit let Apple charge a "reasonable" commission on linked-out purchases; the ruling only covers the US storefront. Adapty reported three apps testing app-to-web paywalls with three different outcomes; one saved about 10 net percentage points after payment fees, not 25, and lost more than that in lower revenue per payer. RevenueCat's test with Dipsea showed a clear drop in conversion when users left the app to pay.
Founders running paid social get better results from web-to-app funnels. Users click an ad, take a quiz-style onboarding on the web, pay on the web and then install. Founders should test both against native in-app purchase and judge on paywall visitor to activated subscriber, not on margin per payer.
Consumers ask ChatGPT and Google AI Mode for "the best app for" a goal; the answers draw on listicles, Reddit threads, YouTube reviews and app store pages. Founders should make sure their website, App Store listing and press coverage describe the app the same way, earn spots on the "best apps for X" lists that models cite, and encourage honest Reddit and YouTube coverage. Founders in health and money should expect models to favor established brands and cite medical or financial sources first.
Zach Yadegari launched Cal AI, an app that estimates calories from a photo of a meal, in 2024. MyFitnessPal bought it in March 2026 for an undisclosed sum. Yadegari told a TechCrunch Disrupt audience that the app had made more than $30 million in about two years. Co-founder and CMO Jake Castillo told the AIM Conference that it had passed $50 million in annual recurring revenue and reached first place in Health & Fitness on the App Store.
The team built the company on creators. One early influencer post brought about 1,000 users in two days. Castillo says the team then signed more than 300 creators in 18 months, onboarding up to 20 a week at the peak. What to copy:
Castillo put Cal AI's paid ads at $1.20 to $1.50 back per dollar spent and health and fitness influencer CPMs at $0.50 to $3. He compared UGC at about 30 videos for $1,000 with influencers at about $1,000 for one video. The revenue figures come from the founders; MyFitnessPal hasn't published them.
B2P buyers are developers, designers, writers, marketers, consultants, salespeople and founders who pay for software to do their jobs better. They pay $20 to $200 a month, on a personal card or an expense card, usually without asking IT. Forerunner sizes the prosumer software market at $90 to $120 billion and found the newest wave of prosumer companies charging a median $78 against $25 for earlier waves.
These buyers behave like consumers when they discover a product and like businesses when they judge it. They find tools through video, creators and peers; they keep tools that save them hours or make them money.
Cursor reached a $1 billion run rate in November 2025 and $2 billion by February 2026, according to Bloomberg, with later reports putting it near $3 billion by April. Lovable passed $400 million in annual recurring revenue in March 2026 with 146 employees. Both companies started by selling to individuals who paid themselves and then sold to teams and enterprises. Consumer AI spending also skews prosumer: rankings of consumer AI apps by spend are dominated by tools that developers, creators, designers and operators use to build, create or earn.
Prosumers buy when they watch someone like them get a result. Companies that do well in this market show that result wherever their buyers look.
Prosumer AI pricing settled into a pattern: a free tier or short trial, an entry plan around $20, and power tiers at $100 to $200 with more usage or better models. Teams meter usage in credits and show users their balance. Unlike earlier freemium founders, AI founders pay inference costs for every free user. ChartMogul's 2026 conversion report found AI-native companies less likely than classic SaaS to lead with a free trial (43% against 61%), and found freemium products bring in about twice as many signups per visitor as trials.
Prosumer companies grow into B2B when users bring the product to work. Teams that do this well watch for clusters: three or more paying users on the same company domain trigger a team plan offer, shared billing and admin controls. When a cluster reaches a few dozen users, a salesperson calls the account with usage data. Cursor reportedly drew a majority of its revenue from enterprise contracts by early 2026, built on accounts that individual developers opened.
Prosumers ask models "what's the best AI notetaker," "Cursor vs Claude Code" and "is X worth it." Answers mostly cite YouTube reviews, Reddit threads and listicles. YouTube mentions correlate with AI visibility more strongly than any other signal Ahrefs measured, so B2P companies should treat reviews and tutorials by independent YouTubers as an AI search program as well as a video program.
Anton Osika built gpt-engineer, an open-source coding agent that passed 50,000 GitHub stars, in 2023. His team renamed the product Lovable in November 2024 and launched it at the top of Product Hunt and Hacker News. Lovable passed $10 million in annual recurring revenue about two months later with roughly 15 people. In March 2026 TechCrunch reported that it had passed $400 million with 146 employees, after adding $100 million in a single month.
Elena Verna, who runs growth, has said that giving the product away has become Lovable's strongest growth strategy, and that 60% to 70% of traditional growth tactics no longer apply to AI companies. What to copy:
Revenue figures come from TechCrunch. The social tactics come from a teardown by Postbeam, which sells LinkedIn tools, and from Verna's interviews.
Bottom-up buyers are team leads, developers, operators and managers who adopt a tool themselves, on a company card or a small invoice, and then pull their teammates in. Prices run $10 to $50 a seat or a usage meter, up to roughly $25,000 a year before procurement gets involved. Notion, Linear, Supabase, Resend, Vercel, Calendly and Loom grew this way.
ChartMogul, Growth Unhinged and ProductLed studied 200 B2B products in January 2026 and found a median free-to-paid conversion of 8% within six months. Few products sit near that median. A fifth of free-trial products convert below 2.5%; about a quarter convert above 25%. Sixty-one percent of classic SaaS products lead with a free trial. Seventy percent of freemium products add a human touchpoint once a user from a large company signs up. Kyle Poyar's 2026 data shows freemium products earning about 90 signups per 1,000 visitors against 45 for free trials.
The fundamentals haven't changed. Users have to reach value in minutes. A salesperson still has to show up once an account grows past what a credit card covers.
Bottom-up companies now market to the agent as well as the developer.
Vercel's CEO has said ChatGPT refers about 10% of new Vercel signups.
Bottom-up companies turn usage into contracts with product-led sales: they watch for accounts where usage, seat count or company size signals a bigger deal, and send a human to talk to them. In 2026, GTM engineers increasingly ran that system. Clay coined "GTM engineer" in 2023; job postings for the title grew 205% between 2024 and 2025; 69% of postings mention Clay. GTM engineers connect product data, enrichment, intent signals and AI research into workflows that find the right accounts and draft the first message. Salespeople take the conversation from there.
Let software find accounts and draft messages; let a person decide who to contact, send and close.
Bottom-up companies lose seats to agents first. When one person with an agent does the work of three, the team buys one seat. Vendors responded by charging for usage, credits or agent runs alongside a smaller seat fee, and by pricing "agent seats" for automated users. Founders should pick a unit that grows with the value the customer gets and that a team lead can forecast without a spreadsheet.
Developers and operators still trust peers over vendors. Bottom-up companies invest in community spaces, developer relations, open-source components and honest participation in Reddit threads. Model makers cite Reddit more than any other domain, so a developer who writes a good answer in r/devops reaches both other developers and the models that cite Reddit.
Bottom-up content teams should spend most of their effort on four kinds of page:
Neon sells serverless Postgres. When Databricks announced in May 2025 that it would acquire the company, it said that AI agents rather than people had created more than 80% of the databases provisioned on Neon. In Armature's 2026 tests, developers who let Claude Code, Codex or Cursor choose a database got Neon in 66% of picks. What to copy:
Neon's buyers include platform companies that decide what their agents provision for every user. Sources: Databricks' announcement, Neon's Agent Plan page and Armature's study, which Armature, a growth firm for developer-tool companies, ran and published.
Top-down buyers are buying groups inside mid-sized and large companies, spending $50,000 to millions a year. Forrester puts the average group at 13 internal stakeholders plus 9 external influencers. Procurement often joins from the start. They buy like this:
Top-down sellers get onto the shortlist through four kinds of work: public content for agents, classic content marketing rebuilt for AI, shortlist presence in AI answers, and sales content for humans. They support all four with brand work, events, creators, outbound and forward deployed engineers.
Buyers ask models to compare vendors, estimate cost, check security and draft a business case. Buyers get answers built from whatever the model can read. Sellers who publish the facts get represented accurately; sellers who hide them get represented by a competitor's comparison page, an old review or a guess.
Publish:
Keep it consistent. Buyers who ask a model about you get a vaguer answer when your pages contradict each other. Refresh every page on a calendar and date it.
The PDF guide, the research report, the benchmark and the calculator still work in top-down B2B. Buyers in a ten-month cycle need material to bring to the committee. Marketers have to change how they release it.
Top-down sellers should run the AI search playbook with four additions:
Buyers who prefer to buy without a rep still need to get the purchase approved internally. Top-down sellers now write sales content for the champion, not for the rep.
Gartner advises sales enablement leaders to move from static content to AI-powered buyer support. In practice that means letting buyers ask questions of your material, and of a human, whenever they need to.
Buyers put vendors they already know on the shortlist. Sellers have to become known before the buying cycle starts.
Sellers still book top-down meetings with outbound when they send fewer, better messages to accounts showing real signals: a new executive, a funding round, a job posting for the role your product supports, a competitor's renewal date, a visit to your pricing page. Sellers let AI research the account and draft; a human with judgment edits and sends. Sellers write to several people in the buying group, not one. Teams keep campaigns small, warm up new sending domains and authenticate everything.
Enterprise AI sellers adopted Palantir's forward deployed engineer model in 2026. FDEs are engineers who work inside the customer to get the product into production; they now run much of the pre-sale proof as well as post-sale delivery. OpenAI launched a separate deployment company in May 2026 with $4 billion from TPG, Advent and others. Google Cloud set up an AI organization inside its go-to-market team to hire FDEs. LinkedIn reported that forward deployed engineering roles grew 42-fold between 2023 and 2025. Former Snowflake CRO Chris Degnan warned that the best engineers often avoid the role, so companies building around FDEs may struggle to hire them.
Committees buying complex AI software say yes faster to a working deployment inside their own data than to any deck. Sellers should budget for it as a sales cost and price the pilot so the customer commits.
Committees want predictable costs. Sellers moving to usage or outcome pricing win more deals when they offer committed credit pools, spending caps, true-ups at renewal and a clear unit the CFO can forecast. Buyers accept pure outcome pricing where the outcome is countable and the vendor controls most of it, such as resolved support tickets. Elsewhere, buyers still prefer a platform fee plus usage.
Gartner predicts AI agents will mediate 90% of B2B buying by 2028. Forrester expected a fifth of B2B sellers to face agent-led quote negotiations in 2026. Treat both as forecasts; prepare cheaply now:
Ramp sells corporate cards and spend management to finance teams, from small businesses to large enterprises. The company used its transaction data to publish research, then built pages aimed at AI answers. What to copy:
Profound sells the tracking tool and wrote the case study; its own dates span about ten weeks, not the one month it claims. An outside teardown of Ramp's data marketing found none of the data pages among Ramp's ten biggest organic traffic pages and noted that Ramp still gated a quarterly report.
Wynter's April 2026 survey of 50 senior leaders at B2B SaaS companies found that 62% keep every price behind "contact sales"; 28% show smaller plans and gate enterprise pricing; 10% publish everything. Buyers ask models about price either way. When Omnius researchers added a moderate budget to shortlist prompts, ChatGPT users saw vendors that hid their prices about 10 points less often than vendors that published them; a retest showed a smaller gap, so treat the result as early.
Teams designing a pricing page in 2027 should:
SaaS Capital's 2026 survey of more than 1,000 private B2B SaaS companies put median net revenue retention at 103% and gross revenue retention at 91% for bootstrapped companies with $3 million to $20 million in ARR; the top tenth reached 118% and 100%. When customers need fewer seats, vendors have to grow accounts through usage, new products and new teams.
Expansion looks different in each market. B2C teams sell annual plans and run win-back offers; B2P and bottom-up teams sell team plans to clusters of users; top-down account teams sell into new departments and new products.
Buyers trust the tools and advisers they already use. Integration partners, agencies, consultancies and systems integrators pass some of that trust to the vendors they recommend. Pigment, which sells planning software to finance teams, added partner data to its inbound and outbound motions and reported the results in a case study published by Crossbeam, which sells the partner-data software it used:
Pigment's process: pull intent signals from G2 and website visits, check which partners already work with those accounts, alert the partner so it can register the opportunity and track the result. Reps request introductions from partner managers in Slack. Field marketers plan co-hosted events where partner overlap is highest.
By market:
Omdia expects enterprise software sales through the AWS, Microsoft and Google Cloud marketplaces to grow from $30 billion in 2024 to $163 billion by 2030, a 29.1% compound annual rate between 2025 and 2030. Companies have committed an estimated $470 billion upfront to those three clouds; buyers increasingly negotiate those commitments to include budget for third-party products. Omdia expects partners to handle nearly 60% of marketplace transactions by 2030 through partner private offers; Canalys made a similar call in 2024.
Top-down sellers and larger bottom-up vendors, especially in infrastructure, DevOps, security, data and AI, should:
Bessemer's January 2026 report on vertical AI, as summarized in secondary coverage, found AI-native vertical companies founded since 2019 growing about 400% a year, at about 80% of the contract value of incumbent vertical SaaS and with gross margins around 65%. Bessemer argues these companies sell against labor budgets (claims adjusters, medical coders, underwriters) rather than software budgets, so buyers measure return in full-time-equivalent work rather than per-seat cost. The operating executive who owns the workflow can often approve the purchase without the IT budget cycle. One example in the coverage: a $400,000 contract that displaced $1.2 million of contractor spend.
Core means build the motion around it. Strong means a reliable second channel. Supporting means useful in a mix. Weak means test only with spare budget. Skip means don't bother.
| Channel | B2C | B2P | B2B bottom-up | B2B top-down |
|---|---|---|---|---|
| Organic short-form video (TikTok, Reels, Shorts) | Core | Core | Supporting | Weak |
| Creator and UGC programs | Core | Core | Supporting | Supporting (operator-creators on LinkedIn) |
| Clipping campaigns | Strong | Supporting | Weak | Skip |
| AI UGC ads | Supporting (for testing) | Supporting | Weak | Skip |
| Paid social (Meta, TikTok) | Strong | Strong | Weak | Weak |
| Google search ads and AI Max | Supporting | Supporting | Strong | Strong |
| ChatGPT ads | Weak | Weak | Weak | Weak (test if budget allows) |
| AI search and reference content | Supporting | Strong | Core | Core |
| SEO for informational queries | Weak | Weak | Weak | Weak |
| SEO for comparison, pricing and "alternatives" queries | Supporting | Strong | Strong | Strong |
| Reddit, organic and paid | Supporting | Strong | Strong | Supporting |
| YouTube long-form and video podcasts | Supporting | Strong | Strong | Strong |
| Founder and executive posting on LinkedIn and X | Weak | Strong | Strong | Core |
| LinkedIn Thought Leader Ads and sponsored creators | Skip | Supporting | Supporting | Strong |
| Launch videos on X | Supporting | Core | Strong | Weak |
| Product Hunt | Weak | Supporting | Supporting | Skip |
| Product virality (shared outputs, invites, badges) | Strong | Core | Core | Weak |
| Coding-agent defaults, docs and MCP servers | Skip | Supporting | Core | Supporting |
| Review sites | Supporting (app stores) | Supporting | Strong | Strong |
| Research reports, benchmarks and PDF guides | Skip | Weak | Supporting | Core |
| Calculators, assessments and free tools | Weak | Supporting | Strong | Core |
| Signal-led, human-sent outbound | Skip | Skip | Strong (product-led sales) | Strong |
| Autonomous AI SDRs | Skip | Skip | Skip | Skip |
| Owned events (dinners, roundtables) | Skip | Weak | Supporting | Strong |
| Community | Supporting | Strong | Strong | Supporting |
| Forward deployed engineers | Skip | Skip | Weak | Strong (complex AI products) |
These splits are starting allocations for a team's first year in each market, not industry benchmarks. Teams should move money toward whatever their own data shows after 90 days.
| Spend area | B2C | B2P | B2B bottom-up | B2B top-down |
|---|---|---|---|---|
| Creators, UGC and clipping | 40% | 25% | 5% | 5% |
| Paid amplification of proven creative | 30% | 20% | 10% | 5% |
| Founder, executive and launch content | 5% | 20% | 10% | 15% |
| Reference content, AI search and comparisons | 5% | 10% | 15% | 15% |
| Docs, agent readiness, MCP and DevRel | 0% | 5% | 20% | 5% |
| Research, guides, calculators and free tools | 0% | 5% | 10% | 20% |
| Product virality, referral credits and lifecycle | 15% | 10% | 5% | 0% |
| GTM engineering and signal-led outreach | 0% | 0% | 20% | 10% |
| Sales enablement content | 0% | 0% | 0% | 10% |
| Events and community | 5% | 5% | 5% | 15% |
In every market: Put most of the money behind the one primary motion. Fund the testing of new channels from a fixed slice, around 10%, and stop tests that show no results within a quarter. Spend paid media on ads and messages that already worked as organic posts.
| Market | Leading indicators | Lagging indicators |
|---|---|---|
| B2C | Installs and trial starts per creator and per format; cost per verified view; day-zero trial start rate; day-zero cancellation rate | Month-one annual cancellations; 12-month revenue per install |
| B2P | Week-four active users per launch cohort; outputs shared per user; referral credits redeemed; creator-coded signups; company domains with three or more users | Expansion from individual to team plans; net revenue retention |
| B2B bottom-up | Agent pick rate across your developer prompts; signups through CLI, API and MCP; activation rate; product-qualified accounts | Free-to-paid conversion; expansion revenue; deals sourced from product usage |
| B2B top-down | Day-one shortlist presence (ask in every discovery call who else they considered and how they found you); share of AI answers by committee role; calculator completions; sales room views by buying-group members | Win rate; sales cycle length; share of deals where you were the first vendor contacted |
Founders should still hire only after they've proven a motion. The roles changed.
Small teams with good judgment and AI tools now produce what much larger teams produced a few years ago. Founders should hire for judgment and for knowledge of the buyer; the agents handle much of the production.
| ARR | B2C and B2P | B2B bottom-up | B2B top-down |
|---|---|---|---|
| $0 to $1M | Founder plus contract creators | Founder plus one developer advocate | Founder-led sales and content |
| $1M to $5M | Creator program lead, short-form producer | GTM engineer, content lead | AI search and reference lead, first AEs, events |
| $5M to $15M | Paid lead, lifecycle lead | Product-led sales team, community lead | Enablement lead, analyst relations, forward deployed engineers |
Buyers ask AI models first. Software buyers in every market now ask a model before they ask a person; developers let agents pick their tools. Marketers write for that reader by publishing complete, current, consistent facts on open pages and by earning mentions on the sites models cite.
Consumer and enterprise marketing split further. Founders selling to consumers and prosumers compete on how much video they post. They run creator programs that post hundreds of times a week, scale the winners with paid media and convert inside the first session. Sellers to enterprise committees compete on proof and on being known. They publish research and tools, build reference pages for agents, equip champions and show up in person.
Everyone has the same AI tools. Everyone can generate UGC, outbound email, blog posts and ads with AI. Buyers and platforms learned to discount all of it within months, as they did with personalized cold email the year before. Buyers still respond to things AI can't produce cheaply: original data, real customers on camera, executives with specific opinions, products that deliver in the first session and relationships built over years.
Owned assets still count. Platforms keep cutting organic reach and keep automating the ad auction. Companies still control their email list, their community, a product users share and a public body of facts about the company.
One motion per market. Founders should pick the engine that fits their market and run it for eighteen months, checking every month what the models say about them and fixing what they get wrong.
Keep the people with taste. Give them the tools. Rebuild the marketing department around judgment.
The engineering response and the communications response are different jobs. Railway’s October 21 incident shows what happens when nobody owns the words.
I took a $2M holding-company retainer apart line by line and rebuilt the same scope with one senior operator and four agents. It costs 6.8% of the fee.
Ibn Khaldun wrote the best account we have of why groups cohere and why they come apart. It was never about loyalty.
The old playbook now works against you. A diagnosis of the channels that broke, then the seven motions that still compound.
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