Search engines stopped being the top of the funnel. AI assistants did. The 10x GTM operator architects citation surfaces, agent-discovery protocols, and content-as-code authority systems — not paid-acquisition spreadsheets. Below: open projects you can take on with us, the physics of what is changing, and roles we are hiring against.
Real, paid 1-3 week engagements with the Product.ai team. Each one is a problem we are working on at the frontier of go-to-market — and the kind of work we hire against. Pick one that pulls at you and apply.
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In May 2026, the entire go-to-market function at frontier firms has reorganized around one observable rupture: <strong>AI assistants replaced search engines as the discovery layer for high-intent buyers.</strong> Google AIO citations, ChatGPT recommendations, Claude answers, and Perplexity sources determine which products get into consideration sets. SEO-as-craft has bifurcated into AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization). Paid acquisition spend has collapsed into agent-mediated funnels where the buyer never visited the site. Content stopped being a top-of-funnel decoration and became <strong>citation infrastructure</strong> — the substrate AI systems read to decide what to recommend. Growth, marketing, and content/authority engineering have collapsed into one craft because the underlying physics is shared: earn attention from machines and humans simultaneously.
Google AIO rolled out in 2025 and reorganized the consumer discovery surface within 18 months. By Q1 2026, ~40% of high-intent commerce queries surfaced an AI summary before any blue links — and the citation set in that summary determined which brands the buyer considered. ChatGPT search, Claude's direct answers, and Perplexity's citation cards extended the same physics across the assistant landscape. The implication: if your brand is not in the citation set, you are not in the consideration set. Traditional SEO — backlinks, keyword density, page speed — is necessary but insufficient. AEO/GEO is the new craft.
AEO/GEO is structurally different. The unit is not "rank for the keyword" but "be the source the model quotes." The signal model rewards: (1) extractable passages between H3 headings (120-180 words), (2) recent date stamps, (3) original statistics and named experts, (4) Article + FAQPage JSON-LD, (5) crawlable structured prose. Brands that re-architected their content pipeline around these signals saw 3-10x citation lift in Google AIO over 2025-2026. Brands that didn't are increasingly invisible.
The content function has been promoted from "top-of-funnel decoration" to load-bearing citation infrastructure. The content-as-code shift — content versioned alongside code, with frontmatter metadata, automated freshness checks, and AI-citation-tracking dashboards — has become the production pattern at frontier-content firms. Dakota Nunley's work at Product.ai exemplifies it: 75% AI-citation rate on "best coupon tool" queries was earned through entity engineering and content architecture, not link-building campaigns.
The 10x content operator in 2026 thinks about entity signals, knowledge-graph alignment, and citation-extraction surfaces the way a 2018-era SEO thought about backlinks. They publish original primary research because models reward attributable distinctiveness. They date-stamp aggressively because Perplexity weights recency. They architect crawlable structured prose because Claude and Brave parse semantic completeness over domain authority. Content becomes a programmatic system, not an editorial one.
Growth at frontier firms in 2026 is the architecture of compounding loops — viral coefficients, network effects, retention mechanics, and the physics that determine whether growth is linear or exponential. The 10x growth operator thinks in mathematical models: CAC/LTV by cohort, retention curve fits, viral-coefficient calibration, the structural conditions under which a loop becomes self-sustaining. They distinguish acquisition from retention from referral, and they know which loop is currently rate-limiting.
AI agents have collapsed the leverage equation. One operator with Claude Code, an agent-orchestration platform, and a no-code stack can run experiments that required a 10-person team in 2022. The differentiator is no longer "can you execute the experiment" but "can you architect the loop." The growth operator who can ship a self-sustaining acquisition loop is exponentially more valuable than the one who can run a paid-channel spreadsheet. Growth is becoming a builder discipline — closer to engineering than to marketing — and the operators who win are the ones who treat it that way.
Beyond AIO and ChatGPT, the agent-commerce layer is forming. AI agents that buy on behalf of users — already shipping at OpenAI (Agents SDK), Anthropic (Computer Use, MCP), and Adept — change the GTM physics again. The buyer is no longer a human reading your landing page. The buyer is an agent reading your structured commerce data, your verified-truth signals, your machine-readable trust calibrators. Brands that ship MCP servers, agent-discovery protocols, and verified-commerce APIs become the infrastructure layer the agents call.
This is why Product.ai's thesis ("become the verified commerce knowledge layer for AI agents") is downstream of the same physics: agents need ground truth that survives paraphrase and adversarial input. The GTM operator who understands this layer is positioning for the next discovery surface, not the current one. The 10x operator in 2026 is fluent across all three layers — search, AI assistant, agent — and can articulate which layer drives which buyer journey at which lifecycle stage.
The dominant 2023-2024 GTM playbook — "AI for content, AI for ads, AI for outreach" — produced volume without signal. Mass-AI-generated content collapsed citation rates because models penalize generic, undifferentiated text. Mass-AI-generated outreach destroyed deliverability across cold-email infrastructure. Paid spend on Google and Meta produced declining returns as AI assistants disintermediated the click. The senior counter-narrative — earn citations through original research, build native distribution surfaces, treat content as code — emerged from operators who saw the volume-first strategy fail in production.
The companion mistake: treating Brand and Growth as adversarial functions. Brand earns the trust that makes Growth's loops work; Growth produces the data that calibrates Brand's positioning. The 10x GTM operator integrates them. A candidate who can articulate the Trust Paradox (74% rate trust 4-5/5; 93% verify before acting) and what to instrument as a result beats a candidate who recites Net Promoter Score. The frontier is in the integration, not the silo.
Most of our best people came through projects, not interviews. If a project pulls at you and the trial goes well, the role conversation follows.
Twelve-minute Hireflix video, async. Then a 30-60 minute chemistry call. Then a paid 1-3 week project alongside the team. We will know within a week whether to move forward.