College & career · Guangzhou

The AI Advantage Bootcamp.

Seven days where college students, master’s students, and early-career professionals launch a live product and build the agency, taste, and craft that set them apart as AI reshapes work.

  • DatesDec 27, 2026 to Jan 2, 2027
  • WhereGuangzhou, in person
  • WhoCollege and early career
Jointly hosted withLux Scholar 千麓学者
The gap between a degree and a first role

Four gaps, seven days.

What separates a strong candidate when AI is part of the work, and the day of the week that trains each.

  1. The gap

    Choosing what to build

    Coursework hands you the problem; work rarely does.

    Where the week closes it

    Days 1 and 2 · Problem and users

    Each participant commits to a problem, interviews users, and writes a mini-PRD with prioritized features.

  2. The gap

    Building with AI, not just prompting it

    Knowing a tool’s name is not the same as shipping with it.

    Where the week closes it

    Day 3 · Build with AI coding tools

    Participants build with AI coding tools and make every decision.

  3. The gap

    Finishing under real constraints

    Projects without users, prices, or deadlines stay as drafts.

    Where the week closes it

    Days 4 and 5 · Brand, launch, freeze

    A name, a landing page, and an advertisement; then the product goes live, gets priced, and finds its first users.

  4. The gap

    Telling the story to an employer

    Interviews test how you explain what you built and why.

    Where the week closes it

    Days 6 and 7 · Story and demo day

    Pitch and interview framing, the target-job build, a full rehearsal, then a live pitch to founders and operators.

Why it matters

With AI, a first version of almost anything comes together faster. Knowing what to build, and finishing it, sets a new hire apart.

In one hands-on week, participants learn how a product is made, how to use AI tools in real work, and how to keep building on their own.

  • 01How a product is made

    From choosing a problem and interviewing users to launch, pricing, and demo day.

  • 02Using AI tools in real work

    More than twenty tools, each learned on the participant’s own product, with every decision theirs.

  • 03Building on their own, next time

    The same method carries into a first role, a side project, or a course assignment.

Outcomes

What participants leave with.

Built for internship and job applications, and for a first role where AI is part of the work.

  • 01A live AI product

    Launched during the week, with real users, and still running afterward.

  • 02A role-specific project

    A tool built for the role each participant is applying to.

  • 03A practiced presentation

    How to explain what they built, practiced on stage at demo day.

  • 04A portfolio

    Code on GitHub and a personal website that presents the work.

The week

Seven days, from problem to demo day.

Pre-work arrives before day one. Two company visits and guest firesides run across the build days.

  1. Day 01Problem selection

    Feedback on pre-work, then each participant commits to a problem.

  2. Day 02User research and PRD

    Interviews with users and a mini-PRD with prioritized features.

  3. Day 03Build with AI coding tools

    Participants build with AI coding tools and make every decision.

  4. Day 04Brand, landing page, ad

    A name, a design system, a landing page, and an advertisement.

  5. Day 05Launch, pricing, build freeze

    The product goes live, gets priced, and finds its first users.

  6. Day 06Story workshop and target-job build

    Pitch and interview framing, the target-job build, and a full rehearsal.

  7. Day 07Demo day

    A live pitch to founders and operators, with awards.

More than twenty AI tools, each learned on the participant’s own product

Think & research
ChatGPT
Claude
Gemini
DeepSeek
Perplexity
Build & launch
Kimi Code
Doubao 豆包
GitHub
Vercel
Design & brand
Figma
Canva
Ideogram
Midjourney
Video & audio
Kling 可灵
HeyGen
ElevenLabs
Suno
CapCut 剪映
Operate & grow
Notion
Stripe
Our team

Our team has walked the same path.

Arena’s team studied at and worked for the institutions students aim for, and teaches from that experience.

Studied at
  • Caltech
  • Harvard University
  • Brown University
  • Tsinghua University
  • International School of Beijing
Worked at
  • McKinsey & Company
  • Goldman Sachs
  • Y Combinator
  • Apple
  • Microsoft AI
  • Deloitte
  • Kimi (Moonshot AI)
Arena leadership
Meet our leadership →

Info session slides

Slides from our info session with Lux Scholar (in Chinese). Click through them here.

Open the slides →
Program facts

What’s included and how to apply.

LanguageEnglish and Chinese
IncludedPre-work, seven full days of teaching, two company visits, guest firesides, and demo day
HostsArena School and Lux Scholar
ApplyRequest an invitation with your school or current role. Enrollment opens in October.
Do I need to know how to code?

No coding experience is needed. Participants build with AI coding tools, learn how their product works, and make every decision along the way, which is how many teams now build at work.

What if I already have a job offer?

It is a good time to attend. Participants start the new role having already built and launched a product with AI, with a portfolio and a practiced way to explain their work.

What do I need to bring?

A laptop and a problem you care about. Pre-work arrives before day one, so you start the week ready to commit to that problem.

Where the method comes from

The same approach, run before.

  • International School of Beijing · Summer 2026

    A five-day student program

    Over five days, students built working websites and apps with AI and presented them to judges, on the school’s campus with mentors in the room.

    Where
    International School of Beijing
    Judges
    Founders and operators from Harvard, Stanford, Y Combinator, and AI labs
    See results →
  • Bodley Academy · Hong Kong, summer 2025

    Twenty students, five days

    A five-day course designed and taught by Phillip An. Each student worked on one real problem they chose.

    Students
    20
    Rating
    9.5 / 10 Net Promoter Score
    See results →
  • Tsinghua University · Staff training

    Schwarzman College

    Training for staff built around their own work, with their judgment in charge. The same format as AI for Educators.

    Who
    Staff, Schwarzman College
    Format
    Hands-on workshop
    Taught by
    Phillip An
    See results →
Tools participants learn
  • ChatGPT
  • Claude
  • DeepSeek
  • Kimi
  • Cursor
  • GitHub
  • Vercel

More than twenty tools across the week, each learned on the participant’s own product.

How to apply

Request an invitation.

Tell us your school or current role. We reply within three working days with next steps.

Two students present their product at a final pitch
Final pitches at an earlier Arena cohort, Beijing, 2026