Software Engineering
Under Changing Constraints
How lower-cost generation creates second-order changes across people, organizations, delivery, security, and economics.
Andrey Tapekha · 2026
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System overview
Generation abundance redistributes constraints.
Scarcity moves into understanding, verification, and deployment.
Cheap production shifts pressure into context, architecture, review, release capacity, security, and judgment.
clear intent & acceptancegeneration abundancechange-volume surgeparallel solution searchagentic change bundlessoftware sprawlevidence-ready generation
review-capacity gapchange impact & release safetyAI-assisted reviewattention routingindependent verificationaccountable acceptance
human judgment & learningfoundational skill decaycognitive offloadingmental-model driftautomation biastask stewardshipAI-supported learning
context, architecture & reuseshared ontology & knowledge graphstale-context riskarchitecture alignmentownership & provenancegoverned reuse
workflow redesignvariable autonomyexception ownershipdecision & stop rightscross-functional coordinationoutcome feedback loops
AI & software supply chainattack-surface expansioninsecure change at scaleprompt & tool injectionagent privilege misuseAI-enabled defensecontinuous validation
capacity & prioritiestoken & compute budgetsverification capacitydeployment capacitycost per accepted changevalue-based prioritizationworkforce rebalancing
01Generation
abundanceintent · options · volume
abundanceintent · options · volume
TODAY'S FOCUS02Verification
& trustimpact · evidence · acceptance
& trustimpact · evidence · acceptance
03Human
judgmentlearn · challenge · intervene
judgmentlearn · challenge · intervene
04Org context
& reuseontology · architecture · reuse
& reuseontology · architecture · reuse
05Operating
modelroute · decide · recover
modelroute · decide · recover
06Cyber
securitysupply chain · integrity · defense
securitysupply chain · integrity · defense
07Value
& capacitytokens · review · deployment
& capacitytokens · review · deployment
01 / 07
Generation abundance
AI makes candidate changes cheap to produce. Review becomes the first visible bottleneck.
Hover to preview · click a node or link to hold · click the map to return01 / 19
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A queueing model of software delivery
Generation speed is not delivery speed. Every stage has its own capacity.
REVIEW QUEUE321 checks waitingREVIEW FASTER
SIZELOWGROWTHSTABLE
CUSTOMERPR-1423 commits
DEFECTPR-1432 commits
SECURITYPR-1445 commits
CONTROLPR-1451 commit
DATAPR-1464 commits
PLATFORMPR-1472 commits
ARCH DEBTPR-1486 commits
TAIL · NEWEST ARRIVALPR-145
HEAD · NEXT TO REVIEWPR-143
3 PRs queued
PR-1423 commits grouped
01INTENTdesired outcome
02CHANGE FACTScode + dependencies
03BEHAVIORexpected result
04BLAST RADIUSsystem impact
05SECURITYsupply chain
06INDEPENDENT CHECKseparate evidence
07OWNER + ROLLBACKsafe to operate
ACCEPTED CHANGE CAPACITY
← → POSITION · ↑ TURN · ↓ RELEASE
accepted changeready · monitored · reversible
ACTUAL FLOWcommits→pull request→review queue→seven evidence checks→merge
Commit volume is not throughput. Reviewed and merged change is throughput.
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Research and operating agenda
Seven areas for AI-assisted software delivery
What changes → possible responses → what to explore
Andrey
Tapekha
LinkedInlinkedin.com/in/andreytapekha
Questions, research, and collaboration · all views are my own

