The AI tech wave will intensify over the next two to three years as enterprises scale AI updates, FDE services expand, and local-inference hardware and software mature, reshaping costs, workloads, and deployment models.

The AI shift unfolds across three realities: higher coder productivity, widespread deployment of forward-deployed engineers (FDEs) in enterprises, and the growing financial impact of stock-based compensation in big tech as AI spending expands.

Google’s Gemma 4 12B showcases on-device AI by enabling audio and video analysis locally on a 16GB laptop, signaling a broader move toward private, on-device AI with privacy, cost, and bandwidth implications.

Personal productivity may be boosted by using private, local AI tools that draw from individual screenshot libraries as inputs, highlighting a vision for highly customized AI applications.

The analysis draws on sources such as The Information on coder overload and outages, insights on FDEs’ enterprise impact, Alphabet’s compensation costs, and VentureBeat’s coverage of Gemma and related ARD/AI-RTZ discussions to ground the discussion.

Advances in small-language models that run on 8–16GB RAM devices, supported by hardware innovations from Nvidia, Apple, and Microsoft, are pushing AI inference toward local, meterless computing and reducing reliance on centralized data centers.

Google’s extensive equity raise translates into significant stock-based-compensation taxes, contributing to roughly $200B in SBC costs across big tech and shaping the financial backdrop alongside the AI boom.

OpenAI’s coders have seen productivity jump 2–3x thanks to AI tools, but the resulting surge in updates and system interruptions can strain both internal and external platforms, potentially impacting user experience.

Forward-deployed engineers, popularized by Palantir and now adopted widely, collaborate with OpenAI and Anthropic to implement AI at scale in enterprises, constituting a multi‑billion-dollar trend within the ongoing IT-services shift.