Load-Bearing

Research

A compiled, source-verified corpus — twelve papers on how AI changes the firm. Every claim cites a downloaded source; every figure is drawn from the data behind it.

A — The nature of the shift

  1. Paper 01 AI as a General-Purpose Technology

    Why a technology this powerful can still be near-invisible in the productivity statistics — and what the steam and electricity records say about the gap between capability and realized value.

    18 verified sources

  2. Paper 02 Where the Value Pools

    In every technology wave the money is made twice — first on the infrastructure, then on the applications.

    18 verified sources

  3. Paper 03 Rewiring Work vs. Automating It

    Why the productivity payoff from AI comes from redesigning how work is done — not from dropping a tool into an unchanged process.

    19 verified sources

  4. Paper 04 Task-Level, Not Job-Level

    Jobs are bundles of tasks. AI acts on tasks, not job titles — which is why occupation-level predictions of mass displacement keep missing, and why the operative question is which tasks to hand over, not which jobs to cut.

    25 verified sources

  5. Paper 05 AI as a New Kind of Labour

    The dominant vendor framing for agentic AI is no longer a tool you buy but a workforce you manage.

    23 verified sources

B — Adoption & organizational mechanics

  1. Paper 06 The Adoption Playbook & the Human Constraint

    Organizations have bought the technology; most have not captured the value. The binding constraint is not model capability — it is the human and organizational work of trust, identity, workflow, and incentive that decides whether a tool in hand becomes a tool in use.

    22 verified sources

  2. Paper 07 The 80/20 of Human Value & the Career-Ladder Trap

    AI is strongest on the codifiable majority of work and weakest on the residual that requires tacit judgment — and that same residual is exactly what entry-level work was teaching.

    14 verified sources

  3. Paper 08 The Organizational-Change Frameworks

    Why a well-run organization absorbs a new technology rather than being transformed by it — and the half-century of theory that explains the gap between AI adoption and AI value.

    17 verified sources

C — Business model & the demand side

  1. Paper 09 Business Model, Pricing & Governance

    When software starts doing the work instead of helping a person do it, the unit you sell, the margin you earn, and the liability you carry all change at once.

    27 verified sources

  2. Paper 10 The Demand-Side Disruption

    How conversational AI is collapsing the marketing and commerce funnel — turning discovery into an answer, optimization into reputation, and the checkout into something an agent does for you.

    20 verified sources

D — Safety & the frontier

  1. Paper 11 Safety & the Frontier

    Frontier AI safety has become a layered engineering discipline and a judgment problem — not a content filter — at the same moment the scaling recipe that built these systems is running into walls.

    28 verified sources

E — The firm lens

  1. Paper 12 AI in Financial Services & Wealth Management

    Adoption is near-universal and spending is real; measured impact is still early and small.

    21 verified sources