The recent a16z piece on The Palantirization of everything by Mark Andrusko is one of the clearer treatments of a problem many enterprise AI companies are already feeling: early traction is often driven by deeply embedded teams doing bespoke work, and without discipline, that path leads to a services business wearing a software costume. That diagnosis is largely correct - and worth taking seriously.
But the more interesting question is not whether forward-deployed work is dangerous. It’s when it becomes dangerous, and what actually separates a compounding platform from a high-end delivery shop in an era where software itself is getting cheaper and faster to build.
In practice, many of the most valuable enterprise AI problems still demand real proximity to the customer. The hardest work lives in messy, high-stakes domains where production outcomes matter: reconciling exceptions across fragmented systems, continuously monitoring disparate systems for early risk and next-best-action signals, and capturing the “in-between” context - decisions, SOPs, undocumented overrides - that determines whether automation is explainable, auditable, and trusted. These are not problems that vanish with better models alone.
The mistake is assuming that because this work is necessary, it should be permanent.
Forward deployment should be a use-case scaling strategy, not an operating model. The first phase - going from zero to one - benefits enormously from tightly integrated field and platform engineering. But that phase only creates leverage if it is explicitly designed to end. The purpose is not to deliver forever; it is to extract a repeatable pattern that can be scaled with far less human involvement.
This is where the article’s warning is most important, and where execution discipline matters more than rhetoric. Time-boxing initial deployments, constraining customization to the edges, and protecting a stable, upgradeable core are not nice-to-haves. They are the mechanisms that force compounding. Without them, every new customer quietly fragments the product, and the organization accumulates entropy rather than advantage.
Where the piece feels underweighted is on how moats actually form in the 2026 timeframe.
The traditional platform moat, large inventories of bespoke integrations and proprietary primitives, made sense when software was expensive and slow to build. That world is fading. AI-assisted engineering is collapsing development time and cost, which means static platforms are easier to replicate and harder to defend. The connector layer, by itself, is no longer durable.
What compounds now is not the surface area of the platform, but the operational playbook behind it. The ability to repeatedly drive real 0→1 outcomes, transition those outcomes to 1→N scale, and feed the resulting learnings back into the system. Most critically, it is the accumulation of context and memory: entity maps, workflows, decisions, state, and institutional knowledge that make each deployment smarter than the last.
That “context layer” becomes a durable advantage for customers, much like data did in prior eras, but only if the system remains modular and upgradeable. Lock-in through rigidity is brittle; advantage through adaptability is not.
Finally, the article implicitly treats SaaS as the default endpoint business model. That assumption is increasingly questionable (and probably requires its own post based on the stock market events of this week!). As reliability improves and margins converge toward software economics, outcomes-based pricing becomes viable - first in hybrid form (platform plus outcomes), and increasingly outcomes-weighted over time.
The key distinction is that outcomes cannot simply be layered on top of bespoke delivery. The winners will be companies that can tie outcomes to platforms that genuinely compound, where each success lowers the cost and increases the reliability of the next one.
The real risk in enterprise AI isn’t forward deployment. It’s mistaking early momentum for durable leverage, and failing to design for compounding from day one.