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  • Post last modified:December 1, 2025
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From Cold Email to Term Sheet: How an AI PR Startup Won Funding

What Changed and Why It Matters

Cold emails are back in style — and AI is the accelerant. Clipbook, an AI PR startup, secured Mark Cuban’s investment after a single, targeted cold email.

At the same time, investors are starting to automate the other side of the table. One VC is publicly using an AI agent to generate, sign, and fund seed term sheets up to $1M.

“I’m using AI to write Seed/Seed-Strapping Term Sheets up to $1M. I’ve signed multiple term sheets and have wired significant funds.”

Zoom out and the pattern becomes obvious: the distance between pitch and term sheet is compressing. AI is moving from inbox personalization to decision execution. Founders who understand this new tempo can raise faster, with less noise and more precision.

Here’s the part most people miss. Distribution — not the model — is the real unlock in early-stage AI. That’s true for product and fundraising.

The Actual Move

  • Clipbook’s founder, Adam Joseph, cold emailed Mark Cuban. Cuban read it and invested. The founder called it a long shot he didn’t expect to land.

“A long shot with a cold email that he never thought anyone would read.”

  • Clipbook builds AI-driven PR tooling. It targets a chronic founder problem: getting earned media, turning mentions into leverage, and managing PR workflows without a heavy agency retainer.
  • Parallel proof points show how cold outreach still converts when done well:
  • A 17-year-old founder, Liam Fuller, raised $1.4M pre-seed for an AI procurement startup by cold emailing.
  • FiscalNote’s Tim Hwang famously cold emailed Mark Cuban — and went on to raise $230M.
  • Jason Lemkin has publicly funded companies off exceptional cold emails.
  • On the investor side, AI is becoming executional:
  • A VC is running an AI agent that drafts and closes $1M seed term sheets — with money wired.
  • In the broader AI market, software is absorbing workflows end-to-end:
  • Paradigm raised a $5M seed to ship a spreadsheet with “an AI agent in every cell.”
  • Fortune’s Term Sheet tracks steady AI funding (e.g., Darwin AI’s $15M Series A to help public agencies adopt AI safely), signaling active demand for focused, workflow-native AI tools.

The Why Behind the Move

  • Model
  • Clipbook’s wedge is workflow automation in PR — summarize coverage, systematize outreach, and package results. Outcome over output.
  • Traction
  • Earned media is measurable and shareable. If you can show lift (mentions, replies, backlinks, inbound), your outreach becomes proof, not promises.
  • Valuation / Funding
  • A brand-name angel compresses future rounds. It sharpens the signal for customers and downstream investors.
  • Distribution
  • Cold email works when it’s targeted, credible, and has a clear ask. That’s true for PR outreach and fundraising. AI now helps personalize at scale — but the message still has to earn the reply.
  • Partnerships & Ecosystem Fit
  • Cuban’s network is distribution. For PR tooling, exposure to operators, founders, and media-facing teams becomes compounding channel access.
  • Timing
  • AI workflows are shifting from demos to daily use. Tools that save time and drive measurable outcomes (press, pipeline, revenue) get adopted faster.
  • Competitive Dynamics
  • PR is crowded. The path through is clear ROI, easy onboarding, and distribution advantages (brand investors, case studies, fast loops).
  • Strategic Risks
  • Spray-and-pray outreach burns trust. Over-automating diligence or compliance in AI-driven investing invites regulatory risk. Founders should keep receipts (metrics, audits, SOC2-in-progress) to maintain credibility.

What Builders Should Notice

  • Write for one person. Personal context beats a long deck.
  • Make the ask obvious: amount, use of funds, and the next step.
  • Lead with proof: a 60-second demo link, live metrics, or customer quotes.
  • Treat outreach as a product: ICP list, messaging tests, follow-up cadence, and conversion tracking.
  • Use AI to scale personalization, not generic volume. Relevance wins.

Buildloop reflection

The moat isn’t the model — it’s the distribution. AI just removes excuses.

Sources

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