Let’s take that seam you named and stitch it into something tactile, ugly, and honest. Below I’ll (1) map an Objective–Data–Architecture Trade-off Table into your ukb-fractal (θ′→θ→Σ→h(t)→ΔS), (2) critique the MBA/business-school default at each layer, and (3) give concrete corrective moves (curriculum, hiring, governance) so firms don’t just “engineer report cards for Wall Street.”
I’ll be cheeky where necessary and blunt where true.
| Lens / Concern | θ′ — Data (Soil / What) | θ — Architecture (Roots / How) | Σ — Optimized Function (Neo / What they teach) | h(t) — Execution Layer (GPUs / Ops) | ΔS — Energy & Compute (Yield / Cost) |
|---|---|---|---|---|---|
| MBA status-quo | Broad, noisy, managerial anecdotes, casebooks; heavy on secondary summaries, light on primary data. | Organizational templates (org charts, matrix, M&A playbooks), bulky but shallow — lots of rules, little systems engineering. | KPI design for quarterlies: revenue growth, margin, EPS, IRR; emphasis on symbolic optimization (reports, dashboards). | Project management, spreadsheets, PowerPoint, outsourced tech teams; low in-house compute literacy. | Low visible compute but high human cognitive/coordination energy; high financialized energy (share buybacks, incentive misalignment). |
| Engineering-centric program | Narrow, curated datasets; reproducible experiments, telemetry, instrumented products. | Modular, test-driven architectures; product/tech stacks, CI/CD, data pipelines. | Product metrics + experiment-driven objectives (A/B tests, causal metrics, safety constraints). | In-house infra (tracking, small clusters, MLOps), engineers with capacity to deploy and measure. | Higher literal compute & tooling cost, but lower waste from bad decisions; better energy→value ratio for product improvements. |
| Ideal hybrid (what firms should teach) | Mix: curated domain datasets + broad market signals; metadata, provenance, and sampling priors taught explicitly. | Dual literacy: org design and systems/ML/engineering design; scaling laws taught alongside governance. | Multi-objective optimization: value to users, robustness, long-term IRR, externalities; explicit Pareto front education. | Ops that can both run experiments and constrain them ethically: shared infra, observability, feedback loops. | Accounted thermodynamic ledger: cost per bit of insight, carbon/compute accounting, attention as scarce resource. |
Optimize for signal that pleases markets, not for truth. MBA pedagogy trains students to optimize symbolic metrics (EPS, ROIC) rather than causal truths (does the product actually serve people?). That is optimizing the report (Σ) rather than the system (θ, h(t)). Thermodynamically: lots of low-entropy signalling, little high-value computation.
Treat data as story fodder, not a substrate. Cases teach narrative selection and plausibility, not sampling bias, provenance, or how to curate datasets. Students learn to craft plausible narratives from the soil (θ′) without understanding whether the soil is sterile or poisoned. Result: robust narratives built on brittle data.
Teach organization without engineering. Org charts and incentive contracts are taught as if they were design primitives. They aren’t substitutes for real system design/architecture: queuing, pipelines, failure modes, scaling laws. The school models agency; it rarely models computation. So executives can supervise engineers, but cannot engineer the product or the measurement system.
Reward short-term signal engineering. The incentive structure privileges actions that increase visible KPIs immediately (ΔS flows to markets), even when those actions destroy long-term value. That’s thermal shortsightedness — draw power now (buybacks), starve the engine later (underinvest in R&D/infra).
Neglect end-to-end accountability for compute and energy. Energy budgets (literal compute, human attention, carbon) are externalities in the pedagogy. MBAs manage money and people, but rarely the thermodynamics of computation that drive modern product lifecycles.
Short ladder of interventions any school or firm can adopt tomorrow:
Teach causal data literacy, not just descriptive cases. — Mandatory curriculum: sampling theory, provenance, experiment design, measurement error, and counterfactual thinking. — Practicals: instrument a simple web product, run pre-registered A/B tests, and publish reproducible notebooks (θ′ → Σ).
Add a compulsory “Systems Engineering for Managers” module. — Topics: queuing theory, latency vs throughput tradeoffs, scaling laws, cost per inference, and simple cloud economics. Teach how design choices amplify or attenuate ΔS. — Practicals: deploy a pipeline, examine failure modes, optimize for both cost and accuracy (θ → h(t)).
Replace half the case method with small, reproducible projects. — Students must ship a tiny product or an experiment and show telemetry. That forces learning across data, architecture, and objectives (full ukb loop).
Score managers on long-horizon causal outcomes, not just short KPIs. — Governance metric: multi-year value retention, user-level retention, robustness to stress tests. Build delayed incentives for sustained product health.
Teach thermodynamic accounting. — Simple ledger practice: energy/compute per unit of signal, carbon per KPI improvement, attention cost per notification. This forces trade-offs to be explicit (ΔS visible).
Embed engineers into strategy classes and strategists into engineering sprints. — Cross-posting: one quarter working on an engineering team, one quarter in strategic planning with access to product telemetry.
If you’re running a firm and tired of MBA report-card machinations, do these now:
When execs lack engineering literacy, the easiest path to hitting quarterly numbers is to redesign signals (Σ) — change accounting, timing, or recognition policies — rather than improving the underlying system (θ, h(t)). That’s not management; it’s signal engineering. It spends low visible compute but enormous social/financial energy and often reduces long-term ΔS (the company’s ability to produce real value).
True engineering raises the entropic density of a firm’s soil: better data, better measurement, better architecture. That costs compute and time — but it converts human energy into durable value rather than transient scoreboard movements.
Business schools have institutionalized a pedagogy that privileges plausible epistemology (narrative, scores) over constructive engineering (instrumentation, systems, scaling laws). The seam you feel is where telos (ΔS: corporate goals as value gradients) meets physics (θ & h(t): what can be represented and computed). Stitch those together and the firm stops being a signal factory and becomes an engine.
Be political: reward long-horizon truth, teach the thermodynamics of computation, and force managers to graduate a single, ugly product before they can be promoted. That’s less romantic than networking, but far more useful.
If you want, I’ll convert this table into a one-page syllabus for an “MBA for Builders” (modules, prerequisites, week-by-week projects) — already mostly written in my head.