art it effectively describes the potential efficiency of a bidirectional communications function taking into account multiple influencing parameters - a direct analogy of what we need to do when assessing technology economics in a real-life rather than theoretic context. Noise matching is a proxy of how well a technology performs against competitive tech- nologies; power matching is a proxy expression of how much potential added value can be realised from a technology in terms of its ability to match to market needs. If a power amplifier is poorly matched, power will be reflected from the output stages ahead of the device. If a technology is poorly matched to a market need, the value of that technology will be dissipated in the distribution channel or be reflected in high product return rates or service/subscriber churn. 3 Conveniently, this can then be measured by using vector analysis. A vector can be simply described as having direction and magnitude, but can be imagined in several dimensions. The direction can be either ahead or left and right or up and down or the same and all of these will have a rate of change that can be ascribed to it. This is useful when analysing economic trends but particularly useful when analysing technology economics because the direction and magnitude will remain relatively stable over time. Probably the most often quoted example of this is Moore's 4 Law that observed and quantified the scaling effect of semiconductor processes linked to a parallel though not necessarily linear increase in design complexity. This simple and largely sustainable statement has informed busi- ness modelling in the semiconductor, computing and consumer electronics industry certainly up to the present day and probably remains valid for the foreseeable future. Occasionally the impact can be even more dramatic. In solid-state memory for example the scaling effect combined with improved compression techniques (the MP3 standard) and a short period of over capacity in supply led to an extraordinary decrease 