AI's first wave of successful implementation will probably look more like the first wave of computing, which was led by enterprise installations that cut jobs
I don't want it to write a blog post for me; I want help rephrasing a passage. — I don't want it to produce code for me; I want assistance digging through the web for examples of what I need. … Baldur Bjarnason / @baldur@toot.cafe : “Enterprise Philosophy and The First Wave of AI - Stratechery by Ben Thompson” — https://stratechery.com/... > Benioff isn't talking about making _employees_ more productive, but rather _companies_; the verb that applies to employees is “augmented”, which sounds much nicer than “replaced”; the ultimate goal is stated as well: business results. … X: @deepvalue47 : “I didn't fully understand the company until this 2023 interview with CTO Shyam Sankar and HoG Commercial Ted Mabrey; I suggest reading or listening to the whole thing, but I wanted to call out this exchange in particular” Eyes are opening. Awake?? $PLTR https://stratechery.com/... Jake Colling / @jacobcolling : This Stratechery article is worth a read for anyone building AI products, especially enterprise focused ones https://stratechery.com/... @serknight_ : Long read on the history of tech platforms from @benthompson Google, Facebook, Microsoft, Salesforce Meet @PalantirTech “the models themselves become more of a commodity, and all the value gets created by you how steer, ground, fine-tune these models with your business data [image] Peter Wilczynski / @petewilz : Great article from @stratechery. Digital transformation requires creating new roles and ways of working, not just making the existing enterprise more efficient. Transformation must come from within. https://stratechery.com/... [image] @stratechery : Enterprise Philosophy and The First Wave of AI The first wave of successful AI implementations will probably look more like the first wave of computing, which was dominated by large-scale enterprise installations that eliminated jobs. https://stratechery.com/... Bradford / @bwradford : “My core contention here, however, is that AI truly is a new way of computing, and that means the better analogies are to computing itself. Transformers are the transistor, and mainframes are today's models. The GUI is, arguably, still TBD.” LinkedIn: Guillermo Ayestaran : The integration of AI into enterprises is set to reshape how corporate culture is defined and experienced. … Shanan Delp : A good (and long) take from Stratechery today with an analogy for how AI will take shape. Worth a read if you're puzzling over “how to use this thing” and “is my job obsolete.” … Shyam Sankar : AIP is realizing a generational platform shift. — “To that end, the company I am most intrigued by, for what I think will be the first wave of AI, is Palantir Technologies. …
Context & Ripple Effects
The argument shifts attention from consumer-facing AI features to enterprise deployment: value is framed around business results, redesigned work, and systems that can be grounded in proprietary data. That aligns with earlier coverage of competing approaches to AI integration and modularization among major cloud and model providers.
It also complicates the product-level framing seen in Apple’s effort to embed generative AI across its products. The claim here is that the consequential early deployments may be back-office and operational, where adoption can change headcount rather than simply add a visible feature.
First-order effects
- Enterprise buyers are pushed to judge AI projects by measurable business outcomes and workflow redesign, not by whether individual employees can use a standalone assistant.
- Workers in processes selected for automation face role changes or job reductions; firms that can adapt models to internal data and workflows gain the most immediate leverage.
Second-order effects
- AI vendors and integrators must compete on deployment, governance, and workflow-specific tuning as much as base-model quality, because generic models are described as increasingly commoditized.
- Large enterprises may consolidate spending around platforms that can connect data, steer models, and support operational change, raising the bar for point tools that only promise personal productivity.
Third-order effects
- If enterprise implementation consistently substitutes for roles rather than merely assisting them, AI adoption becomes a management and labor-organization issue, not just a software-purchasing decision.
- The pattern points toward value concentrating at the deployment layer—among firms that own workflow access and proprietary context—while foundation models become less differentiated.
The trend: AI’s early commercial phase is moving from broadly available assistants toward institution-specific systems built to reshape enterprise operations.