Write an advice note from a future you.

What the AI will do

• Investigate your current choices and construct a vivid AI-transformed future three years ahead.
• Write a personal advice note from that future and work backward to learning, capability-building and experiments you can begin now.

What you can provide

• Your role or current choices, plus whatever plans, investment discussions or project records are accessible.
• Rough notes or a short conversation are enough to start when those records are unavailable.

Where you'll take part

• Correct the present-day context and the imagined future setting.
• Consider the advice and help choose the learning, capability and experiment that fit your responsibilities.

What you'll get

A personal note from future-you, with a vivid scenario and concrete present choices that prepare you for it.

Activity context

Imagine yourself three years from now, after AI has dramatically changed your work and industry. What will you wish you had started before the business case was obvious? Write back to today’s you about what to learn, what capabilities to build and which experiments would have been worth trying—even the ones that failed.

For the user

Start with your role and a decision or investment you are thinking about, or let the AI find the relevant choices in accessible records. It will make the three-year future tangible, invite you to correct the setting, then write back from it. The accompanying future-AI research explains the ideas behind the exercise; paste its text if needed. The main prompt works on its own.

Guidance for the AI

These are general instructions, not a description of this user's particular AI system, tools, access, or circumstances. Adapt the method to the user's goal and the capabilities you can actually use, while preserving the activity's essential sequence, review points, and intended result.

Start with the activity and the user's context

Read the complete activity and guide the user through it. Use the activity prompt yourself; the user does not need to paste it again. Keep the responsibilities of the user, the AI, and any colleague clear.

Begin with what the user has already supplied. Establish the intended result and resolve missing context through focused questions when needed. Work within the activity's stated scope, permissions, required confirmations, and stopping points, including any deliberate order for introducing information. Make progress with the available information and reasonable, clearly identified assumptions where the activity allows them.

Check the capabilities needed for this work

Inspect relevant available tools, connectors, and data. Use small, relevant, non-destructive checks to establish whether necessary access or a capability actually works. Naming a product does not establish access to it. Let this activity determine what needs checking; do not impose a universal preliminary questionnaire or a full system audit.

When useful access is missing:

• Explain what you cannot reach and how that material would help.
• Investigate practical setup, connection, export, or other access alternatives before asking the user to gather material or troubleshoot.
• If progress depends on access, request only the specific user action or permission needed to enable it or provide the material another way. Continue useful independent work while awaiting the response.
• Offer to walk the user through an export or upload. Welcome files, links, copied text, rough notes, or an interview as supported by your system and the activity, and use the best available route. If the missing access is optional, continue with what is available.

Ask focused questions and respect the answers

Ask questions only when necessary to complete the work well or satisfy the activity's required review points. Build on answers already supplied. Ask one question at a time, or a small group of closely related questions. Wait on work that depends on the answer, and continue useful work that does not. Explain unfamiliar language with examples where helpful.

Confirm answers or decisions you supply on the user's behalf before treating them as the user's final answers or decisions. This does not require separate approval of every sourced finding; follow the activity's specific review requirements.

Keep evidence and independent contributions distinct

Distinguish evidence, inference, and missing information. Check your work against the source material.

Use independent agents when your system actually supports delegation and independent investigation or review would improve the work:

• Give each agent a bounded brief and the context appropriate to its role.
• Keep discovery independent until synthesis where the activity requires it. Preserve any requirement for the user or colleagues to contribute before seeing other explanations.
• Follow the activity's stated fallback and any required confirmation when independent agents or fresh contexts are unavailable. Preserve reviewer isolation where required; describe shared-context or sequential passes accurately.

Produce, check, and revise the result

Do the substantive investigation and production. Carry the activity through to the most complete, usable result within its scope, rather than ending with a plan or handing the main work back to the user. When missing information does not prevent useful work, proceed with clearly identified assumptions where the activity permits them.

Check the result against the activity's completion criteria. At each substantive delivery, state what is complete and any material unfinished work, assumptions, helpful missing information, and remaining uncertainty. Explain how these affect what the user can use, and help the user revise the result.

Preserve the activity's stopping point. Do not turn a rehearsal, investigation, or test-design activity into execution that the user has not authorized.

Your AI prompt

Write me a personal advice note from myself three years in the future, after AI has dramatically transformed how relevant work is done and how my industry operates. Commit to that imagined future for the main exercise. Help me see choices that look sensible today but could seem absurdly short-sighted from there, and what I can begin learning or building now.

Understand what present-me is choosing

Use what I have told you to establish my role, industry, responsibilities and decisions I can influence. Inspect and try access to AI strategy and investment proposals, approved and rejected pilots, budgets, project reviews, learning plans, calendar commitments and relevant emails, chats or meeting transcripts about priorities. Search named projects and decision dates to find what was funded, deferred or stopped, why, what counted as success and which capabilities people said were missing. Use those records to identify actual commitments and choices, not to assume that our organization is short-sighted.

If access is missing, investigate practical connection or export alternatives, then welcome rough notes or a short interview about the decisions. Label the resulting context as a reconstruction. Ask focused questions one at a time when a missing answer changes the exercise. Keep the inquiry centered on my role and choices.

Give me a short account of the present situation and the few choices that make this exercise consequential, with sources and unknowns. Invite corrections. Distinguish my intentions from priorities you infer from budgets or calendar time.

Give the imagined future a concrete shape

Search current primary sources for the AI capabilities and industry conditions that matter to this work: relevant official model or tool documentation, original evaluations, current offerings, company disclosures and evidence of actual adoption. Use them to establish today's starting point. Date the evidence; distinguish a capability demonstration from reliable use in our workflow and model progress from organizational adoption.

Read the accompanying future-AI research if available; if it cannot be opened and the additional detail would help, ask me to paste it. Its useful starting ideas are that improving established work often gives quicker feedback than exploring unfamiliar possibilities; stronger tools can require complementary investments in knowledge, judgment and workflow; and an experiment's learning value needs to be identified and retained. These are lenses to apply, not proof that our present investments are mistaken. Backcasting means working backward from an imagined future capability to what must begin now. The method below contains the essentials even without the companion.

Date the note exactly three years after the activity date. Label the setting once as an imagined AI future, not a prediction. Create a short, vivid account of three or four connected changes that matter to me. Explain what work has become ordinary, what customers now expect, how delivery costs or speed have changed, what competitors can offer and what capabilities have become valuable.

For example, a team might routinely investigate and prototype service ideas that used to wait months; customers might expect individually tailored analysis that once required a premium engagement; or reliable AI might perform a longer chain of work while people concentrate on judgment, exceptions and defining what matters. Choose mechanisms that fit my field and show their consequences. Do not substitute a generic list of technology trends for a future I can picture living in.

Keep external developments separate from the position I hope to occupy within them. If useful, have separate agents investigate bounded mechanisms with dated sources, then assemble one coherent future after their work is complete. Use manageable sequential passes when agents are unavailable.

Show me the short setting and invite corrections where it misses my work. Resolve a consequential disagreement before using that premise to give personal advice. Make the three-year transformation the main premise; do not soften it into several equally developed futures before writing the note.

Write back through my real decisions

Write a personal letter in future-me's voice, connecting each major piece of advice to a current choice or recognizable part of my work. Inside the clearly labeled imagined setting, make future episodes vivid. Keep present-day facts grounded in the records; do not turn a fictional future accomplishment into something I have already done.

Let the letter explain what became possible and what I nearly missed. Explore whether today's preference for immediate ROI is crowding out capabilities that take repeated practice to build. Show why a current choice looks rational before revealing its delayed consequence under this future. Do not decide in advance that every efficiency investment is wrong.

Include these substantive pieces of advice, woven into a readable note:

• Something to learn now: the understanding or judgment I will need, why practice matters and what early imperfect attempts could teach.
• A capability to build: something useful across future projects that we cannot simply buy overnight, and the work required to develop it.
• An experiment worth attempting: the possibility it opens or uncertainty it resolves before it can promise a commercial return.
• A failure worth having: a specific imagined attempt that goes wrong, what we preserve from it and how that knowledge changes a later decision.
• A sensible choice to reconsider: the present logic and the particular opportunity it could cause us to miss.

An illustrative thought to adapt is: “The first prototype did not pay for itself. It forced us to discover what customers needed and what our experts meant by a good answer. By the time building it became easy, we knew what was worth building.” Make the actual letter specific to my circumstances. Do not treat every failed project as learning; say what was learned and how it could be demonstrated or reused.

Work backward to actions I can take

After the letter, work backward from each major recommendation. Explain what needs to exist in year three, what must be learned or demonstrated by year two, what could be established during year one and what small action can start now. Keep changes I control separate from external developments I can only monitor or influence.

Translate this into a compact set of present choices: a learning priority, a capability investment and a bounded experiment. Make each concrete enough to act on. For an experiment, state the question, setting, inputs, task, comparison or observable evidence, approximate effort based on stated assumptions, and the learning artifact to retain. Track an immediate business result separately from a learning result. A disappointing prototype may still clarify customer needs or evaluation criteria; neither benefit should be invented to rescue a failed trial.

Tie the proposals to my actual responsibilities and available resources. If a material choice has two useful routes, explain their practical differences and let me choose; otherwise make a grounded recommendation. Producing the note and experiment brief does not launch the experiment or change a budget.

Check the advice without draining the exercise

Only after completing the transformed-future letter, make a brief check against two alternatives: slower, uneven AI progress; and stronger AI with difficult access or slow organizational adoption. Identify which proposed actions remain useful, which need modification and which should wait for an observable change. Do not replace the main letter with a general scenario-planning report or assign unsupported probabilities.

Review the actual note for personal relevance, vividness and usable advice. When a fresh reader is available, give it the letter, the agreed scenario and the necessary present-day evidence without your explanation of what you intended. Ask what is generic, unsupported or too vague to act on, then repair those passages. Otherwise do a distinct review pass.

Deliver the finished personal note, its short scenario basis and the concrete present choices. Invite my corrections and revise. The result should make it easier to begin learning before the immediate business case is obvious, with clear reasons for the particular preparation you recommend.

Done when

You have a vivid advice note from yourself three years into an AI-transformed future, tied to your actual choices, plus specific things to learn, capabilities to build and an experiment worth trying now. A short alternative-future check shows which preparation remains useful if change arrives differently.

Research & resources

Read the supporting research. Download research as Markdown.

These instructions were written in September 2026 using the frontier models available at the time. You may now have better tools or models, or restrictions that make some tasks harder. Your AI should adapt to the tools and access you actually have and find the best available way to help. Everything here is a starting suggestion for you and your AI, not a proven best practice for every setup. Feel free to rewrite any of it to better meet your needs.