Blaxel

June 2026

I joined Blaxel as its first marketing hire, reporting to the CEO. Blaxel builds infrastructure for autonomous agents: the compute, storage and networking behind software that runs code, uses tools and carries work from one task to the next.

My remit was growth and revenue. I owned the marketing strategy and budget, directed agencies, and worked across paid acquisition, product marketing, customer proof and brand. I also wrote the technical stories, built their companion examples and developed tools for the marketing work itself.

For a usage-based product, getting someone to sign up is a beginning. The next question is whether they can put the infrastructure to work. That connected the parts of the job: make the product understandable, give developers something useful to try, and measure what happened after they arrived.

Give people a way to inspect the claim

The customer-story work made the product concrete. I wrote about what customers were building, where Blaxel fit into their architecture, and what the customer still owned. Where a working example helped explain the mechanism, I paired the story with a GitHub cookbook.

A developer could read about persistent files, then inspect an example that deleted a sandbox and recovered the files in a new one. They could read about code generation, then follow the build, check and repair cycle in a working environment. The code gave the writing something to stand on.

I brought that same specificity to positioning and launches. I chose and led the rebrand direction, wrote product-positioning articles and made paid creative that explained the infrastructure through the work an agent does. For the OpenAI Agents API launch, I wrote a draft article and supporting social copy, with the destination, visual and approval requirements defined for each piece.

Connect acquisition to what happens next

I owned paid acquisition across Google, LinkedIn, X and ChatGPT Ads. The measurement work connected ad delivery with product activity and payment outcomes, while keeping each metric's attribution clear.

I also built AI-agent workflows for recurring marketing reviews. Paid media review moved from a monthly cadence to a daily one, with conversion data synthesized into proposed changes for human approval. Market research and answer-engine citation analysis had their own evidence and review workflows.

Results

The paid program delivered 46% more paid-ad conversions on 36% less spend, with cost per paid-ad conversion down 56%, versus the prior 30 days.

Over the same period, product signups increased 37% and workspace-created activity increased 41%. Those are overall product measures; the paid results don't establish paid attribution for them.

In my first three months, the broader growth work contributed to two successive doublings of seven-figure ARR. That was a company result, supported by the work across acquisition, technical content, customer proof and brand.

Explore the work

• Paid Growth & Measurement: the paid results, measurement boundaries and review cadence.
• Customer Stories & Cookbooks: technical writing paired with examples a developer can inspect and run.
• Positioning & Brand: rebrand direction, published writing and the product story in paid creative.
• Product Launches: the OpenAI Agents API launch draft, channel adaptations and release preparation.
• AI Marketing Systems: the tools I built for paid review, market research, citation analysis and shared context.