Great article! Curious to see how these software titans monetize agents and convert to token economics. I can see a world where license count remains the same and these software behemoths then take on additional modules/features that will be so productive that their customers will purchase the modules with the same users and productivity, as a result, will explode. I think this scenario makes total sense under the themes of 'AI will not take away jobs, it will create more' and 'productivity continues to increase, not just efficiency'.
I continue to question the logic of whether falling costs to produce software will lead to reduced competitive advantage to incumbent software firms. Software production costs (cost to deliver a unit of functionality) have fallen by orders of magnitude over the past several decades. Over that period, enterprise customer preference for 3rd party SaaS tools over in-house development has gone up and up, and the software winners are bigger than they have ever been. This is despite the prevalence of free open source alternatives in every category imaginable. If we believe in the power of AI coding agents and assistants, why is it bad for software firms for the productivity of their largest cost center to go up by 3-5x?
I don't know what capex means for hyperscalers in the long run but a lot of the software companies getting hit were trading at rather ridiculous multiples. Among the hyperscalers only Google has managed to truly stand out, including investments that have accelerated its cloud considerably.
Great breakdown of the system. The harder question is what interpretive framework you build on top of it. Decomposing the problem well is necessary but not sufficient..the edge is in having a thesis about which structural position survives the transition, not just understanding why the transition is happening.
Interesting points. The shift toward token based or usage based pricing could change how revenue is captured without changing licence counts. At the same time, lower production costs in the past have not reduced concentration in the software industry. If AI materially increases developer productivity, it may strengthen the largest platforms, especially if distribution and integration advantages remain. The key issue may be how the value created by higher productivity is allocated.
Great article! Curious to see how these software titans monetize agents and convert to token economics. I can see a world where license count remains the same and these software behemoths then take on additional modules/features that will be so productive that their customers will purchase the modules with the same users and productivity, as a result, will explode. I think this scenario makes total sense under the themes of 'AI will not take away jobs, it will create more' and 'productivity continues to increase, not just efficiency'.
I continue to question the logic of whether falling costs to produce software will lead to reduced competitive advantage to incumbent software firms. Software production costs (cost to deliver a unit of functionality) have fallen by orders of magnitude over the past several decades. Over that period, enterprise customer preference for 3rd party SaaS tools over in-house development has gone up and up, and the software winners are bigger than they have ever been. This is despite the prevalence of free open source alternatives in every category imaginable. If we believe in the power of AI coding agents and assistants, why is it bad for software firms for the productivity of their largest cost center to go up by 3-5x?
It's a great point!
I don't know what capex means for hyperscalers in the long run but a lot of the software companies getting hit were trading at rather ridiculous multiples. Among the hyperscalers only Google has managed to truly stand out, including investments that have accelerated its cloud considerably.
Great read. I don’t think SW is dead, I think the corporate giants are dying.
Legacy tools are being replaced by lean, AI native and cost effective platforms.
Great breakdown of the system. The harder question is what interpretive framework you build on top of it. Decomposing the problem well is necessary but not sufficient..the edge is in having a thesis about which structural position survives the transition, not just understanding why the transition is happening.
Interesting points. The shift toward token based or usage based pricing could change how revenue is captured without changing licence counts. At the same time, lower production costs in the past have not reduced concentration in the software industry. If AI materially increases developer productivity, it may strengthen the largest platforms, especially if distribution and integration advantages remain. The key issue may be how the value created by higher productivity is allocated.