This week, we welcomed early beta users to Blaze Autopilot, the world's first fully autonomous AI marketer. So I want to share why exactly we chose to launch now—not next week or next month.

Great founders have been shipping fast and iterating even faster for years—it's not a new idea. But agentic products are ushering in a new competitive dynamic that's entirely different from every previous startup wave. To win, we had to be first to market.

Until now, even the most agile startups had to process learning into product improvements pretty manually.

Only 6 years ago, when we launched Almanac, each learning cycle—from feedback to shipping an improvement—took at least two weeks. At the time, shipping new features that quickly month after month put us in rarefied territory.

We're launching Blaze Autopilot into a very different world. Building something agentic means AI—not a user—handles a complex, cyclical workflow from beginning to end. Autopilot creates a marketing strategy for you, generates content, publishes it, collects performance data, and trains its models to make the next week's content better. Rinse and repeat.

Handing the whole workflow to AI unlocks a totally new way to learn what works and improve our product in real time. No one has ever been able to attribute marketing metrics to individual design or copy decisions, especially across hundreds of thousands of companies at once. Now, with Autopilot, we can confidently understand what content elements translate to higher conversion rates, and immediately train our generation models to take that insight into account.

Because Autopilot is fully agentic, we can finally connect all the dots without waiting on human analysis or action. Improving the product happens in seconds in the background, not weeks. That breakneck pace of learning is exactly why it's so important that we're first.

Why First Mover Advantage Matters

Imagine AI agents don't exist yet, and two similar startups launch. Each has the ingredients to succeed. One company launches first, but its culture of learning and iteration is a bit slow. The other launches 6 months later, but executes brutally fast. It rises to the top because its pace is so differentially better that it can aggressively overtake its earlier rival.

If those two companies launched today, agentic AI would let them each build learning loops of relatively similar speed and quality, assuming each had the human talent to architect and prompt them effectively. So whoever launches first and jumpstarts the flywheel gets better faster and wins. It's that simple.

If we launch Blaze Autopilot with 1,000 beta customers this month, within 30 days, we'd accumulate hundreds of thousands of data points from publishing and analyzing customer content. Just that first month of data will dramatically improve Autopilot's output quality, and by month six, the learning advantage will have compounded exponentially. More data leads to better quality content, which leads to more market awareness and customers, who generate even more data for us. A fresh competitor would need to catch up to the six months of learning and improvement we'd have baked in.

So choosing to launch now gives us the chance to carve out the most durable advantage in our new agentic reality. Waiting just a week or two could mean ceding that edge to someone else. Here's how we're getting into market first.

Defining "Good" Without Engagement

Building lightning-speed learning via an AI agent isn't a given. Your product's AI might handle complex workflows on its own, but that doesn't necessarily mean it knows how to measure itself and improve. To teach our Autopilot how to iterate, we first had to define benchmark success metrics, structure our learning loop to correlate the right inputs with good outputs, and feed it with data.

Early on, we realized we wouldn't be able to rely on old signals like customer upvotes and downvotes to gauge performance. Autopilot makes a different promise than Blaze Copilot. Rather than "great content in half the time," it offers a real-world result that can be measured objectively: growth in followers, traffic, and sales. Targeting high product engagement would have offered the wrong signal: the better Autopilot is at delivering on its hands-off growth promise, the less time customers will spend in the product.

So we had to teach our feedback loop a new way to define success. The answer was simple. If we promised growth, we would measure growth. To do this, we beefed up our integrations with Google Analytics and publishing platforms like Meta to comprehensively measure our content's performance with data.

We now feed those analytics back to our models to attribute effectiveness to content elements within and across customers. With that performance data, we can understand the best photos, copy, captions, and color choices by industry, company size, geography, and channel. And we use those insights to improve new content in real-time.

The best part is there's no noise or human bias in the analysis. The Blaze Autopilot quietly learns every day what it needs to adjust to make our customers' businesses more successful. It's beautiful.

Feeding Our Learning Loop

This virtuous cycle only gets rolling if you're willing to give us permission to publish as you and collect the data we need to understand what's working. I take the trust you're placing in us extremely seriously.

It's why we've been so careful to fill Blaze Copilot with features that give you ultimate control, like our powerful editors and review/approval workflows. When I tell customers about Autopilot, almost everyone asks whether they'll still be able to edit content in Autopilot before it publishes—just in case.

The answer is an emphatic yes. The input and feedback you give Autopilot will always be what sets your marketing apart. Autopilot's learning loops will actually make your edits more impactful than ever. Instead of seeing changes you make reflect in only one document or design at a time, you'll teach Autopilot to sound and look more like you with every turn.

For example, imagine Autopilot generates 10 Instagram posts for you, half with light backgrounds and half with dark. You might not think that light backgrounds are a good fit for your brand, so you'd batch replace those light backgrounds with dark ones. Autopilot will learn from that not to design future posts with light backgrounds.

That's just one way Autopilot will always incorporate your creativity, uniqueness, and taste. Automating away all the tedious logistics of creating great marketing doesn't have to mean settling for something sanitized or impersonal. Because Autopilot always collects and learns from your feedback, it's effortless to scale you—quirks and all—through your marketing.

Getting In Market Now

Autopilot isn't perfect on Day 1. Learning what works has to start somewhere, and even though we've trained our models on millions of pieces of user feedback, this is the first time we are feeding performance data back into our generation models.

So we're doing things that don't scale to get Autopilot into customer hands. We're not waiting until the output quality is "perfect." It likely won't be until we start the learning flywheel.

Initially, we're onboarding every single customer ourselves, including setting up their Google Analytics and platform integrations by hand if we need to. We're acting as a creative agency might, hand-reviewing customers' content and editing it where needed to make sure quality meets the bar.

That's letting us get the product to customers weeks sooner than we otherwise would. Even though we're investing a bit more time upfront, it's the only way to start building that moat of data, improvement, and quality before anyone else.

Speed vs. Quality: A False Choice

Beyond helping us build a resilient business, what does launching Autopilot earlier mean for our customers? It means better growth results faster. When a plumber in Austin posts content that drives 300% more website traffic, understanding what worked doesn't just help them—through our learning loops, it helps our models make better marketing for every other local business on Blaze.

There's a powerful incentive alignment in that dynamic: the better the outcomes our customers experience, the more Blaze grows. Defining our success couldn't be simpler: it's your success.

That's not an accident. Agentic products are making it possible to finally deprioritize noisy proxy metrics in favor of elegant, purposeful learning loops that more accurately reflect the value we want to deliver. That has the potential to rewrite how technology businesses can relate to their customers. By putting cold, hard results at the center of our learning loop, we're moving away from the perverse incentives that many software businesses face to optimize for attentional monopoly, not promises fulfilled.

With Blaze Autopilot, we finally get to retire some of the long-true tradeoffs of building software. No more choosing between speed and quality: now, the faster we go, the better quality gets. No more choosing between engagement and a better experience. The faster our learning flywheel spins, the better the experience we deliver, and the less time our customers waste tweaking content.

If Blaze Autopilot succeeds, it won't just be because we can execute a complex workflow start to finish. It will be because we built it with an opinionated definition of success and the end-to-end infrastructure to automatically improve every day. And it'll be because we launched it as soon as we could, started to compound learning, and figured out how to deliver better growth for our customers faster than anyone else.

Want to help us test Blaze Autopilot? We're slowly opening up beta access in the coming weeks. Reply here and I'll add you to the next beta cohort waitlist.