The Distribution Era - GTMnow

This text is a extremely necessary one to us at GTMfund. It’s the results of weeks of writing and years of compounding market perspective. It’s the idea that we based GTMfund on, and it feels extra necessary in the present day than ever.

Hope you take pleasure in studying it as a lot as we loved writing it. The total factor is beneath, and you too can learn it on X.

The shifting moat of B2B software program and AI

The story of enterprise software program is the story of an evolving moat. Every period opens with one price because the binding constraint – capital, deployment, distribution, or constructing itself – and the businesses that personal that period acknowledge and weaponize that constraint for his or her profit. They deeply perceive the limiting variable of the day and learn how to unlock it. Then a brand new know-how collapses the binding price, the moat strikes one step farther from the product, and a brand new set of winners emerges.

Throughout all 4 eras, two core variables have solely moved in a single route: price to deploy goes down, and time to worth goes down. Each period opened with a decrease flooring and fewer friction than the one earlier than it. The moat strikes as a result of the earlier constraints collapse.

Within the Capital + Know-how Period, software program was a bodily factor. These had been the CD-ROM and server rack days, when deployments took 12-24 months and prices had been excessive. The moat was capital itself: solely the businesses that might afford the multi-year construct, promote, and deploy ever bought into the client’s fingers.

Within the Cloud Period / SaaS 1.0., software program now not required thousands and thousands of {dollars} upfront and a multi-year deployment to get began. As an alternative, same-day cloud-based deployment meant firms may enroll on-line and begin utilizing it that day. The moat moved from capital to constructing product and the gross sales movement: multi-tenant SaaS, the SDR/AE machine, and the accomplice ecosystem. Even with deployment prices collapsing, constructing software program stayed exceedingly costly. The sheer variety of engineers required to ship and scale a market-ready product meant SaaS 1.0 winners wanted critical capital to construct what their GTM machine was promoting.

Within the PLG Period / SaaS 2.0., the product turned the funnel, the onboarding turned frictionless, and product love may precede the eventual buy. Slack reached a $1B valuation earlier than hiring a gross sales rep. Figma adopted the identical playbook. The moat moved from gross sales to product mechanics: viral loops, collaboration-driven adoption, and evangelists. Throughout this similar interval, software program engineers turned extra productive and environment friendly, however it nonetheless took significant time and capital to construct an enterprise-ready product.

Now, we’re within the Distribution Period. AI collapsed the price of constructing software program to close zero. Extra importantly, it collapsed the price of copying it. A class chief that when had months to ship a moat-defining function now has days earlier than a fast-following AI-native staff matches it. Product loops nonetheless work, however they now not compound the way in which they did when nobody may copy you. So the moat moved one last step, off the product and onto the viewers.

Throughout all 4, two core variables have solely moved in a single route: price to deploy goes down, and time to worth goes down. Each period opened with a decrease flooring and fewer friction than the one earlier than it. The moat needed to transfer as a result of the earlier moat collapsed.

That is the place we are actually.

AI is the primary wave that meaningfully eroded the price of constructing the product itself.

Each different wave collapsed a price adjoining to the product. None of them collapsed the price of constructing the factor. Now, while you ask a CTO/CIO/CEO what % of code is written by AI, the reply is often “nearly 100%.”

The moat of in the present day is distribution. The sample is identical throughout each Distribution Period winner we’ve seen thus far: grow to be the default model within the class earlier than anybody else can. Construct the viewers, earn the belief, and ship the product right into a market that already believes in you.

Distribution beat product earlier than AI existed

Distribution beating product isn’t a brand new phenomenon. It’s been hiding within the case research of among the greatest firms of the final thirty years. Let’s check out a number of examples.

Salesforce vs. Siebel (1999–2005). Siebel had the higher CRM, the deeper enterprise relationships, and the larger gross sales engine. Salesforce had a thinner product and a wierd pitch: No Software program. Marc Benioff despatched actors in pink T-shirts to protest exterior Siebel’s consumer convention chanting “dying to software program,” drawing police, crowds, and free protection in Fortune and the Wall Avenue Journal. The pricing was key. Siebel required a five-million-dollar minimal to begin a dialog, Salesforce bought seats for 50 {dollars} a month. By 2005, Siebel bought to Oracle for $5.85 billion. Salesforce is value greater than $280 billion in the present day, roughly 50 instances Siebel’s exit.

HubSpot vs. Marketo (2006–2015). Based the identical yr. Marketo had the extra refined automation product. HubSpot had a e-book that named a class. They spent half their advertising and marketing vitality not on HubSpot however on the inbound motion itself: the convention, the certifications, the free CRM, the weblog that got here to personal each significant search time period in B2B advertising and marketing. By the mid-2010s, “inbound” was synonymous with HubSpot (which they just lately re-branded to “unbound”). Marketo, regardless of the higher product, was the specialist software the specialists already knew about.

Notion vs. Evernote (2016–2024). Evernote had a ten-year head begin and greater than 200 million customers. Notion had higher structure and a group. Their ambassador program started with a touchdown web page asking energy customers to work extra carefully with the staff – 400 purposes got here in for twenty spots. Ambassadors made templates, recorded YouTube tutorials, answered questions on r/Notion at 3am. At present, Notion has greater than 20 million customers, over 1,000,000 group members, and roughly 95% natural visitors.

This story isn’t essentially new. What’s new within the Distribution Period is that that is now not the distinctive path, it’s the one sturdy one. To be clear, none of this implies product doesn’t matter – fairly the other. The product nonetheless must be distinctive. The order merely modified, not the requirement.

Distribution issues from Day 0

A typical false impression is that product-market match comes earlier than go-to-market, however go-to-market is definitely the way you get product-market match. It’s additionally the way you preserve it.

Founders have been advised the identical sequence for twenty years: construct the product, discover PMF, then work out GTM. That has modified. It’s now not the way you construct and scale.

If the product may be copied at in the present day’s tempo of improvement and pricing is now not gated by buyer headcount, the sturdy benefit has to stay someplace that may’t be copied or capped. That’s the viewers and the distribution you construct to achieve that viewers.

Cursor turned the quickest B2B firm to a billion {dollars} in ARR with a loyal group of builders who couldn’t cease speaking in regards to the product – see their GTM deconstructed right here. Harvey turned the default model for authorized AI by establishing itself throughout greater than half of the AmLaw 100 earlier than its product may do all the things a purchaser wished, then backfilling the product behind the model. Something, Lovable, Replit, Bolt – similar playbook in vibe coding.

Your go-to-market is what builds your moat and what dictates your development trajectory.

What this period appears like

The following period of software program can be received by the businesses that constructed distribution first. Markets are being received earlier and extra convincingly than ever. First-to-scale rules have by no means been extra necessary in firm constructing than within the AI period, and the hole between first movers and everybody else will solely widen from right here.

AI retains opening new planes of chance, and the winners get topped sooner than ever. When markets are transferring this quick, that early model recognition compounds greater than ever. For instance, Harvey and Legora each surged to default-brand standing within the authorized area, Lovable and Replit are racing to personal vibe coding, and OpenEvidence locked in medical choice help earlier than a lot of the market knew the class existed. These are main examples. The following decade will produce dozens extra of them, and much more in rising classes.

We’ve by no means seen velocity to early income like this, and we’ve by no means seen the dimensions like this both. a16z just lately shared benchmarks round how the median enterprise AI startup hits $2.1M ARR by month 12 and raises a Sequence A at 9 months post-revenue. The highest quartile hits $5.3M ARR of their first yr. What was best-in-class for SaaS startups a decade in the past ($1M ARR at 12 months) now sits beneath the median for AI-native firms.

Corporations are attending to income sooner, scaling income sooner, right into a buyer base that decides which model to belief. The founders who win the subsequent decade received’t have the luxurious of determining GTM at Sequence A. For those who do, a competitor could have already locked within the viewers your product was going to want. GTM foundations must be in place at inception.

Founders are properly conscious of this. Excessive Alpha’s current benchmarks discovered that GTM execution is the highest problem maintaining founders up at night time. It’s ranked forward of product execution, fundraising, and even AI technique itself. Founders already know that it’s the Distribution Period, and it’s as much as the remainder of us within the ecosystem to reply the bell.

Effectively, what if I construct a world-class AI product? Received’t that promote itself? The market will let you know, “No, it’s not sufficient.” Take a look at the place Anthropic is hiring.

Gross sales is the one largest division on their job board. It’s larger than AI analysis, larger than product engineering, or some other perform. The fastest-growing software program firm in historical past is concentrating its hiring round learn how to promote, not simply learn how to construct. The businesses with the strongest product positions on the planet are additionally those doubling down on GTM. The sample Anthropic is setting is the one the subsequent era of category-defining firms will observe.

Pricing is present process a giant evolution beneath all of this. Seat-based pricing was the suitable mannequin for a world by which prospects scaled by hiring extra people, which is now not the world we stay in. Seat-based pricing caps scale. In yr one, a seat-based vendor and a consumption-based vendor look comparable inside a buyer of comparable dimension. By yr three, the consumption-based vendor is working with out a ceiling that its seat-based peer can’t escape. The pricing fashions that may win the subsequent decade are consumption, outcomes, and credit. Distribution strikes alongside pricing, and arguably forward of it. Each buyer motion turns into each a billable occasion and a advertising and marketing occasion. The audience-first firm isn’t promoting to its viewers a lot as monetizing the viewers’s habits.

Lastly, the alpha of early adoption has by no means been wider and can widen additional earlier than it narrows. Changing into AI-native in your personal GTM in the present day produces the widest disparity of efficiency we’ve ever seen between groups that undertake early and groups that don’t. The aperture for what’s doable in constructing a GTM engine may even proceed to widen. The limiting issue is now not the GTM know-how stack you utilize, however your creativeness and your staff’s operational execution. This phenomenon has at all times existed for early adopters of know-how, however it’s by no means been this acute. For those who’re not forward of it, you’re getting left behind sooner than ever. The alpha of early adoption is the distinction between making a class and being absorbed by one.

Welcome to the Distribution Period. We’re simply getting began.

I wrote this piece with Paul Irving. It’s the idea that we based GTMfund on, and it feels extra necessary in the present day than ever.

Tag @GTMnow so we will see your takeaways and assist amplify them.

Anthropic raised $65B at a $965B valuation, crossing a $47B income run charge earlier this month. Claude is now the most-requested mannequin on Amazon Bedrock, and enterprises have moved from experimentation to full manufacturing deployment.

Autodesk is buying MaintainX, the operations and upkeep platform on observe for $135M ARR in 2026 with 50%+ development. The wager: connecting design and construct workflows with day-to-day asset upkeep in a single steady lifecycle. Autodesk is increasing from serving to individuals create the bodily world to serving to them run it.

Polaris dropped their 2026 AI in GTM Research. AI is delivering ends in GTM, however most groups are navigating a fragmented vendor panorama with hidden prices they don’t absolutely perceive.

VC: Tomasz Tunguz on AI’s $575B Wager, the Fifth-Largest Infrastructure Challenge Ever

Pay attention by way of the hyperlinks within the web page above or by looking out wherever you get your podcasts “The GTMnow Podcast.”

Default – raised $20M Sequence A led by 8VC to launch a unified, real-time information layer for income brokers. Everyone seems to be making an attempt to deploy brokers — Default solves the bottleneck: siloed information and legacy techniques that aren’t prepared for them.

Noble – partnering with Profound to shut the loop on AI search visibility. Profound prospects can now determine quotation gaps and fill them with Noble’s offsite community, getting their model into the third-party sources AI platforms truly cite.

Polsia – raised $30M at a $250M valuation — one human, zero workers, approaching $10M ARR. Brokers run coding, analysis, outreach, adverts, and help. Polsia even dealt with its personal fundraise. The pitch: brokers take away the filter that’s at all times determined who will get to construct.

OpenRouter – raised $113M Sequence B led by CapitalG, with NVentures, Databricks, Snowflake, and MongoDB Ventures. Now dealing with 25T+ tokens per week throughout 400+ fashions by way of one API — constructing the Stripe for AI mannequin routing.

Shipper – raised $17M at a $164.4M valuation — two founders, zero workers, approaching $2M ARR in 10 months. The brokers ran the fundraise themselves. A military of brokers that runs an organization end-to-end. You simply construct prefer it’s a online game.