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DS 105 · Module 1

From Web3 to Programmable Finance

Core question: what changes when financial assets and transactions become digital, programmable and observable?

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  • Duration60 min
  • LevelFoundation
  • PrerequisiteNo programming background required; AI 101 or equivalent AI literacy recommended
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Learning objectives

  • Explain programmable finance in non-technical language
  • Distinguish tokenisation from simple digitisation
  • Identify common on-chain evidence fields
  • Ask AI to analyze an unfamiliar transaction record
  • Distinguish an observed event from an interpretation

Why Web3 Matters to Finance

8 min

Why Web3 Matters to Finance

Learning objective: explain programmable finance in non-technical language. This is not a blockchain engineering course : the point is that financial activity can become more observable and programmable, not that learners need to become blockchain developers.

The course's core competency model
  1. FRAME the decision question
  2. SOURCE appropriate and authoritative data
  3. INTERROGATE evidence with AI: inspect, compare, classify, challenge
  4. VERIFY source, assumptions, missing evidence, bias and uncertainty
  5. DECIDE: a human-owned decision with a stated confidence level
  • Internet of Information → Internet of Value: both data and value can now be directly transferred and observed
  • Programmable assets: some asset rules can be encoded into automated workflow
  • Programmable transactions: transfer and settlement steps may be partly automated
  • This course does not teach blockchain consensus algorithms, only what this means for financial decisions

Warning

Simulated-data notice: every dataset in this course is simulated teaching data. All companies, wallets, assets, events and values are fictional and do not represent real companies, investment products or market conditions. Nothing in this course constitutes investment advice.

Info

Human → AI → Human: a human frames the decision question; AI helps inspect and compare on-chain evidence; the human keeps final accountability : this course does not give personalized investment advice.
Check yourself

Which statement best captures the course's use of Web3?

Which statement best captures the course's use of Web3?

Key takeaway: In this course, Web3 matters because financial activity becomes more observable and programmable : not because learners need to become blockchain developers.

Tokenisation vs Digitisation

10 min

Tokenisation vs Digitisation

Learning objective: distinguish tokenisation from simple digitisation. "Turning a record into a digital file" and "tokenising an asset" are often confused, but they differ in ownership, transfer, settlement and rules.

Traditional/digital record vs tokenised representation
DimensionTraditional/digital recordTokenised representation
OwnershipRecorded in a conventional institutional systemRepresented through token/ledger state
TransferInstitution-mediated workflowMay support programmable transfer
SettlementMay be a separate processMay support a more integrated workflow
RulesGoverned by external processesSome rules may be encoded into logic
EvidenceInstitutional recordsLedger plus institutional/contextual evidence

Warning

Important caution: tokenisation does not eliminate legal, operational, market, cyber or governance risk : it only changes how an asset or transaction is represented and observed.
  • Digitisation: converting an existing record to electronic form, without changing ownership or rules logic
  • Tokenisation: representing an asset or right in a form that can interact with a digital ledger/workflow
  • A tokenised asset can still carry real-world legal and operational risk : "going on-chain" does not remove it
Check yourself

Which statement best describes tokenisation in this course?

Which statement best describes tokenisation in this course?

Key takeaway: Tokenisation changes how an asset is represented and interacted with : it does not make risk disappear.

Anatomy of On-chain Evidence

12 min

Anatomy of On-chain Evidence

Learning objective: identify common on-chain evidence fields. This section covers only the decision-useful fields, not technical internals.

  • timestamp
  • wallet / address identifier
  • asset / token
  • action / transaction type
  • amount
  • transaction id
  • contract / event id, if relevant

Warning

Safety reminder: a wallet address does not equal a verified real-world identity. Observable does not mean fully interpretable.
What one on-chain record can directly tell you

A record shows a timestamp, wallet W017, asset Token-X, action BUY, amount 120, transaction id T0001. These are directly observable fields; "who is behind W017" and "why they bought" are not questions this record alone can answer.

Check yourself

Which is most directly observable from a basic on-chain transaction record?

Which is most directly observable from a basic on-chain transaction record?

Key takeaway: On-chain records provide observable factual fields, but "observable" does not mean "fully interpretable."

AI Evidence Reading Lab

15 min

AI Evidence Reading Lab

Learning objective: ask AI to analyze an unfamiliar transaction record, practicing the separation of observation from interpretation. This uses a simulated teaching dataset, not live investment data.

DS105_Onchain_Transactions.csv (simulated data, excerpt)
timestamp,wallet_id,asset,action,amount,tx_id
2026-09-01T09:01:00Z,W017,Token-X,BUY,120,T0001
2026-09-01T09:05:00Z,W023,Token-X,TRANSFER,500,T0002
2026-09-01T09:12:00Z,W017,Token-X,SELL,100,T0003
...
  1. Describe only observed facts
    State exactly what the data shows: time, wallet, asset, action, amount.
  2. Identify notable patterns
    For example, a clear volume spike in one time window.
  3. Suggest possible interpretations
    List one or two plausible but unverified explanations.
  4. Identify what cannot be concluded
    Name conclusions the data alone cannot support.
  5. Name additional evidence needed
    What is missing to make an interpretation more credible?

Info

This dataset has at least 4 wallets, 2 assets, 3 action types, a visible volume spike, and one ambiguous event that cannot be responsibly explained from the dataset alone : designed to practice naming what cannot be concluded.
Check yourself

Transaction volume rose sharply. Which statement is most responsible?

Transaction volume rose sharply. Which statement is most responsible?

Key takeaway: Describe observed facts first, then carefully propose interpretations, and clearly flag what the data cannot answer.

Claim vs Evidence Challenge

10 min

Claim vs Evidence Challenge

Learning objective: distinguish observed fact, inference, and an unsupported/overconfident claim.

Case claim

"Token-X transaction volume increased by 300%, so the market is definitely bullish."

What can be supported

  • A 300% volume increase: an observable fact (if the data shows it)
  • This may be one piece of evidence, worth investigating further

What needs to be challenged

  • "The market is bullish": this is an interpretation, not the fact itself
  • "Definitely": without more evidence, this wording is overconfident and unsupported
  • A volume change may be evidence, but it is not a conclusion by itself
  • "The market is bullish" is an interpretation that needs further evidence to become credible
  • Words like "definitely" are an unsupported, overconfident claim when evidence is incomplete
Check yourself

"Token-X volume increased by 300%, so the market is definitely bullish." Which part of this is inference?

"Token-X volume increased by 300%, so the market is definitely bullish." Which part of this is inference?

Key takeaway: Observed data is one thing; the interpretation and the certainty of the wording are another : the two must be examined separately.

Core Exercise: Digital Finance Evidence Card

5 min

Core Exercise: Digital Finance Evidence Card

Learning objective: complete this module's core exercise, consolidating observation, interpretation and evidence still needed.

  • Event
  • Observed Data
  • Possible Interpretation
  • Alternative Interpretation
  • What We Still Don't Know
  • Additional Evidence Needed

Done

Completing this exercise counts as Core Exercise 1 (ds105-m1-digital-finance-evidence-card).

Key takeaway: Module summary: observable data is not the same as verified meaning.

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