Telco Network MEV

What if we played positive-sum games and measured Maximal Enablement Value instead of Extraction from an interconnected ecosystem?

The Question: What system generated this behaviour and outcomes, and how would it have to change so that the desired behaviour becomes inevitable?

Introduction

As a customer you don't care who engineered the networking equipment that makes the calls possible, you just want to fulfil an intention to speak with someone at an acceptable range of quality and cost.

The same routing algorithm can extract or enable. The difference is the north star — what the setpoint serves.

Orientation	North Star	Optimizes For	Network Effect
Extraction	Maximize margin from the spread	Self — profit per route	Carriers degrade quality to cut costs. Trust erodes. Network shrinks.
Enablement	Maximize quality-adjusted throughput	Ecosystem — every participant gets stronger	Carriers compete on quality. Trust compounds. Network grows.

A global telco uses its backbone network (Ethereum) to route inbound traffic to its intended destination by handing off (interoperability) to alternative carriers (Alts, L2s & L3s) they trust to deliver at competitive cost and quality of service. The question is whether "trust to deliver" means "cheapest route that clears" or "best route that strengthens the relationship."

A stable cost base is essential for implementing a pricing strategy that balances margins per route vs continued loyalty. But loyalty is the leading indicator. The telcos that squeezed every basis point from carrier spreads won the quarter and lost the network. The ones that invested in carrier quality — sharing reference data, honoring trade agreements, maintaining thresholds that served both sides — built the goodwill that compounded.

Data Inputs

Getting access to siloed data and then consistently processing into clean accurate data is 90% of the battle.

• Reference Data
• Network Data

Reference Data

Data Integrity is fundamental to making accurate data-driven decisions.

• Wholesale vs Retail Strategy
• World Number Plan
• Routing Case
• Quality of Service SLAs
• Network (Bandwidth) Capacity
• Customer Records
• Bilateral Trade Deals
• Time of Day
• Regulations

Network Data

• Inter-carrier Destination Costs
• Call Records
• Aggregated Network Traffic
• International Cost base
• Inter-carrier Destination Pricing

Arbitrage Rules

During the early 2000s many Telco operators had not updated their systems to monitor or create routing decisions for international calls to mobile phones because this required collaboration and co-ordination across many departments and systems that had weak interfaces for transforming and exchanging critical data.

Adding to the complexity, a definitive standard for a world number plan does not exist. To operate effectively each of the following systems must share common reference data taken from a single source of truth.

• Switches
• Mediation
• Pricing
• Billing

Arbitrage opportunities arise against a Telco when its reference data is outdated or is not aligned across business critical systems. As an example of this French mobile numbers begin +336 or +337 but in the early 2000s many international carriers had not updated their routing tables and reference data to reflect this. Therefore international operators based in France could route national mobile terminating calls over their international backbone to another country and pass on to another international carrier that was still charging fixed line rates for calls to France mobiles.

• Want to see the Future of Banks? Look at Telcos

Performance Metrics

Use traffic profiling and growth projections to plan where to invest in infrastructure.

• Routes per switch (capacity)
• Routing cases per switch (destinations)

Network Traffic Profiling

Need to profile expected traffic based on:

• Pricing relative to the market
• Day of Week
• Time of Day
• Special events
  • Christmas
  • Ramadan
  • Olympics

Capacity Planning

Physical cables from one switch to another. Where the other switch could be

• International backbone
• Domestic backbone
• Another carrier

Quality of Service

Product type examples are Retail and Wholesale.

Each product type must have a way to designate what level of service the customer had paid for either by route or A number matching.

Each Routing Case (Destination) had acceptable target KPIs. If a supplier falls below thresholds for a particular Destination and Product, add them to a blocklist.

• Answer Seizure Ratio
• Post Dial Delay

Trade Deals

Prioritised volumes that had to be reached to avoid contract penalties.

• Bilateral Agreements
• Multi-lateral Agreements

Open-ended agreements with prices for Number Plans sent via spreadsheets.

See trade in telecommunications

Algorithm Design

In the dream scenario the network runs at capacity without overflow. Every destination is priced so the ecosystem sustains — carriers earn enough to invest in quality, customers pay fairly for reliable service, and the routing intelligence compounds with every cycle.

All traffic for each product type and destination terminates with the first-choice selection, no routing overflow, and all traffic terminated above benchmark quality.

Pricing at the rate that attracts maximum customer volume while maintaining quality thresholds — not the highest extractable price, but the price that keeps the VVFL turning.

Rules

Sequence of logic to deliver routing plans that optimise enablement value — network quality, carrier sustainability, and customer experience — against available capacity.

• Overflow from first-choice selection is minimised
• Use suppliers most valuable routes, then exclude
• Backbone by customer type
• No circular looping
• Minimal overflow
• Blocked destination suppliers
• Trade Agreements
• Dial Code Destination Matching
• Dial Code Cost Blending

Preparation

Gather and sanitize and standardize data.

Predicted Traffic Profile Projected Minutes by Routing Case code per Hour

• Growth Trend
• Historical Trend
• Global Events
• Pricing
• Time of Day

Blend Rates

Load Blended Rate Profile for each routing case by mapping offering price, code overlap for each Time of Day

Routing Logic

In an iterative process that exits after achieving a set variation threshold or maximum iteration limit.

1. Lock in bilateral agreement deals with any special rules
2. Find the order for each destination

• run in order from highest volume destinations to least
• do not consider blocked providers
• get the first and second routing providers

3. For each node on the network, against anticipated overflow setting, calculate the blended cost for each node in the backbone network add egress cost.
4. Recalculate the best routing options for destination including backbone nodes
5. Ensure no option for circular routing

Run the routing algorithm in iterations, after each pass of the routing order logic;

1. Subtract the second choice cost from the first choice cost
2. Order dataset by greatest saving
3. Loop through to sum anticipated volume per provider for each destination/product profile based on volume demand (as)
4. If a provider's safe outbound capacity is reached;

• Remove provider with full capacity as an option
• Add the provider and route to a list to be ignored as a routing option in the next routing iteration
• Move next provider option forward

Cost and Pricing

Automate routing case decisions to fulfil intentions and enable maximal value across a network of solution providers.

Goal: price so the network strengthens — demand balanced with capability to deliver at sustainable margins

Predict cost base of routing with a factor of safety to account for traffic overflow.

Establish the extremes of what the market with pay by analysing their price sheets.

Review objectives of relation with the provider in the big picture, their relative demand for minutes to destination.

Three Flows

Telecom routing and crypto intents follow the same architecture:

INTENT → ROUTE → INFRASTRUCTURE → SETTLE → FEEDBACK

Flow	What Moves	Who Routes	Who Settles	MEV Enabled By
Telecom	Voice/Data	Carrier switches	CDR billing	Routing algorithm that lifts carrier quality
Crypto	Value	Solvers/Bridges	Blockchain	Solver with best execution for the user
DePIN	Both	Edge AI	Oracles + Chain	Protocol with routing intelligence shared across the network

The pattern: User expresses intent. System finds optimal path. The routing intelligence either extracts from the network or enables it. The north star determines which. When standards rise, every participant's capacity to deliver rises with them — a rising tide lifts all boats.

Intent-Based Architecture

A crypto wallet is just like a phone — you don't care about the network, you just want your intentions fulfilled at acceptable cost and quality.

Telecom Term	Crypto Equivalent	Function
Routing Case	Intent specification	What outcome the user wants
Carrier selection	Solver competition	Who fulfills the intent
Quality of Service	Slippage tolerance	Acceptable execution bounds
CDR settlement	On-chain settlement	Proof of delivery
Reference data	Price oracles	Source of truth for decisions

Why this matters for DePIN: When devices become economic actors, they emit intents (data, compute, bandwidth) that need routing. The telecom MEV playbook — reference data integrity, capacity planning, quality thresholds — becomes the DePIN protocol design.

See Crypto MEV for the blockchain-native expression of this pattern.

Decision Traces

The routing algorithm was a decision-making system. The most valuable output wasn't the routes — it was the traces showing WHY routes were chosen. Every exception, override, and precedent encoded judgment that no rule book captured. When a carrier got blocklisted for a destination, the trace recorded the QoS failure, the threshold breached, the alternative selected. That trace became queryable precedent for the next routing decision.

A problem solved is a problem forgotten. Telcos that fixed a routing arbitrage and moved on got exploited again when reference data drifted. The ones that persisted the decision trace — context, options considered, outcome measured — built compound intelligence. Rules tell you what SHOULD happen. Decision traces capture what DID happen and WHY. That gap is where enterprises bleed margin.

This is the context graph thesis applied to infrastructure. Link decision traces to each other and you get a graph that scales judgment without scaling headcount. Wayne Smith coaches this in rugby — review HOW the decision was made, not IF it was right. Todd Simkin runs the same loop in trading: prior → action → actual → update prior. The decision journal is the instrument.

Context

• Navigation System — The abstract pattern: Value (reference data), Belief (routing logic), Control (capacity planning)
• The North Star — Reference data is the north star of routing — when it drifts, arbitrage exploits you
• VVFL — Enablement is what makes the feedback loop virtuous instead of extractive
• Goodwill — The carrier relationships that compound when routing serves the network
• Standards — A rising tide: when standards improve, every participant's capacity rises
• Positive-Sum Games — Rules that reward contribution, not capture
• Essential Algorithm — The meta-pattern: every business IS its routing algorithm
• Routing Algorithm — The universal pattern: same optimization from person to marketplace
• Information Arbitrage — The principle (SSOT)
• Crypto MEV — Blockchain expression
• Telecom Data Model — Entity relationships
• Intent and Interop — Crypto intents
• DePIN Tech — Infrastructure layer

Links

• Blockchain Intents
• Telco Trade Deals
• Route Optimisation
• Call Routing Optimisation
• Massive Profits are Hidden in Your Data

Questions

What if the most valuable output of a routing algorithm is not the margin it captures but the network quality it enables?

• When reference data drifts out of sync across switches, billing, and pricing, what's the first signal that arbitrage is being exploited against you — and does your decision trace catch it?
• The extraction-vs-enablement table has the same algorithm, different north star. What changes in the routing logic itself when you optimize for ecosystem quality instead of margin per route?
• If 90% of the battle is clean data, what would it mean to share that clean data with your carriers instead of hoarding it as competitive advantage?
• The Three Flows table maps telecom to crypto to DePIN — at which layer does the enablement thesis break down, and what does the gap reveal about where extraction still wins?