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Technology, software and artificial intelligence
Build durable technology value as AI reshapes software economics, product architecture and the boundaries between models, platforms and applications.
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Read articleFocus
AI is forcing software companies to question the economics of software itself
Agents can automate work previously performed inside applications, putting pressure on user-based pricing and established SaaS product boundaries.
The car industry is becoming a software and semiconductor industry as well
EVs, software-defined vehicles and autonomy are shifting competitive advantage toward computing, architecture and continuous product evolution.
Strategic challenges
How much agricultural risk can technology actually remove from the field?
The challenge is separating useful precision and biological innovation from solutions whose economics fail under real farm conditions.
Where should builders place capacity in an increasingly uneven construction cycle?
The challenge is reallocating labor and capital across sectors whose pipelines, economics and risk profiles are diverging sharply.
POV
The next factory advantage will come from orchestration, not another automation layer
Industrial leaders will separate themselves by connecting intelligence across the system rather than optimizing machines one at a time.
A distributor that only moves boxes is increasingly easy to bypass
The durable role sits in solving availability, complexity and service problems that manufacturers and digital channels handle poorly.
Strategic impact
Agentic AI is shifting software value from interfaces toward completed work
Products may increasingly compete on outcomes and orchestration rather than the number of seats, screens or workflow steps they support.
Processing capacity is becoming as strategic as resource ownership
Supply security increasingly depends on smelting, refining and by-product recovery rather than simply securing additional mineral reserves.
What we observe
Insurers often scale AI before fixing the data and systems beneath it
Advanced models cannot compensate for fragmented policy data, legacy architecture and controls that were never designed for autonomous decisions.
Banks often treat AI as a productivity program when it is becoming market infrastructure
As automated decisions spread across trading and credit, shared models and technology dependencies can create risks beyond individual firms.