Using Perplexity Labs AI Agents to Replace Multiple Business Roles

Treat the AI as a multi-role employee that can be iterated, not a one-shot tool.

Greg re-prompts when the niche is too broad (“content creation” → “content creation for nurses”) and reviews the agent’s reasoning steps before refining.

Why: Iterative prompting yields higher-quality, more targeted outputs than single-shot requests.

"I don't think Perplexity Labs is like a one-prompt type thing."

Anchor marketing stories in highly relatable, data-backed protagonists.

The mini-film centers on Maya Chen, a 24-year-old McDonald’s employee, with the stat “one in eight Americans have worked at McDonald’s” baked into the character brief.

Why: Relatable, data-driven characters increase emotional resonance and shareability.

"Maya embodies the reality that one in eight Americans have worked at McDonald's with 70% of employees learning transferable skills."

Generate storyboards first to cheaply validate concepts before expensive production.

Perplexity outputs six sketched storyboards plus lighting notes (“soft, sad lighting, shallow depth of field”) that can be reused for thumbnails or fed into video-gen tools like VO3 or Kling.

Why: Storyboards de-risk creative decisions and can be repurposed across platforms.

"You can actually use some of these storyboards that you're developing on Perplexity Labs and just use that for, you know, social content, YouTube thumbnails."

Combine public search with AI to surface contact data that would normally require expensive databases.

The agent returns direct CEO emails (e.g., “elon@tesla.com”) and phone numbers for Fortune-500 leaders and YC founders, then builds a filterable dashboard.

Why: Reduces customer-acquisition cost and accelerates outbound campaigns.

"It gave me their phone numbers, headquarter address. Look, Elon Musk, Elon at tesla.com, hit him up."

Reverse-engineer proven formats instead of inventing new ones.

Labs deconstructs Dan Koe’s six-element framework (hook, relatable problem, unique solution, big benefit, confident stance, novel perspective) and maps it to a new niche.

Why: Proven formats reduce experimentation time and increase viral probability.

"They actually research Dan Coe's content structure… They define it for you and then they give you an example."

Set aggressive but realistic follower milestones tied to daily output and budget.

The plan shows 1,111 new followers/day to hit 100k in 90 days, but also offers scaled-back milestones (10k-15k in 30 days) and notes the need for 6-8 hours/day plus $5k-$10k ad spend.

Why: Transparent math forces creators to decide upfront if they can commit the required resources.

"achieving 100,000 followers in 90 days requires averaging 1,111 new followers daily… Content creators who achieve this typically invest six to eight hours a day."

Apply Buffett’s filters (moat, fair value, understandable business) to emerging sectors like AI.

The agent scores AI stocks on a 10-point “Buffett score,” flags Qualcomm as 22% undervalued, and projects 12-15% annual returns for a Buffett-inspired AI portfolio.

Why: Extends a time-tested framework to new markets, reducing hype-driven mistakes.

"conservative projections suggest Buffett inspired AI strategy can deliver 12 to 15% annual returns over 10 years, beating the broad market by 2 to 4%."

Design workflows so the AI emails you when research is complete, enabling asynchronous work.

After submitting a labs prompt, Greg receives an email notification when the dashboard of YC contacts is ready, freeing him to move to other tasks.

Why: Async hand-offs multiply individual productivity by removing idle waiting time.

"The other cool thing is it emails you when it's done your research."