Every company suddenly became “AI-powered” sometime around late 2023. The pitch decks updated. The product pages grew a new tab. The demos featured a chatbot floating in the corner. But when I started pulling at the threads, something didn’t add up. The companies that felt steady weren’t the loudest about AI. They were the ones quietly rebuilding their foundations around it.
That difference—AI-first versus AI-added—is going to decide the next cycle.
On the surface, AI-added looks rational. You have an existing product, real customers, real revenue. You layer in a large language model from OpenAI or Anthropic, maybe fine-tune it a bit, wrap it in a clean interface, and call it a day. It’s faster. It’s cheaper. It feels lower risk. Investors understand it because it resembles the SaaS playbook of the last decade.
Underneath, though, nothing fundamental changes. Your infrastructure—the databases, workflows, permissions, pricing model—was built for humans clicking buttons, not for autonomous systems making decisions. The AI is a feature, not a foundation. That matters more than most teams realize.
Because once AI isn’t just answering questions but actually taking actions, everything shifts.
Consider the difference between a chatbot that drafts emails and a system that manages your entire outbound sales motion. The first one saves time. The second one replaces a workflow. That second system needs deep integration into CRM data, calendar access, compliance guardrails, rate limits, cost monitoring, and feedback loops. It’s not a wrapper. It’s infrastructure.
That’s where AI-first companies start. They design for agents from day one.
Take the rise of vector databases like Pinecone and open-source frameworks like LangChain. On the surface, they help models “remember” context. Underneath, they signal a deeper architectural shift. Instead of structured rows and columns optimized for human queries, you now need systems optimized for embeddings—mathematical representations of meaning. That changes how data is stored, retrieved, and ranked.
It also changes cost structures. A traditional SaaS company might pay predictable cloud fees to Amazon Web Services. An AI-native company pays per token, per inference, per retrieval call. If usage spikes, costs spike instantly. Margins aren’t a quiet back-office metric anymore—they’re a live operational constraint. That forces different product decisions: caching strategies, model routing, fine-tuning smaller models for narrow tasks.
When I first looked at this, I assumed the difference was mostly technical. It’s not. It’s economic.
AI-added companies inherit revenue models built on seats. You pay per user. AI-first systems trend toward usage-based pricing because the real resource isn’t the human login—it’s compute and task execution. That subtle shift in pricing aligns incentives differently. If your AI agent handles 10,000 support tickets overnight, you need infrastructure that scales elastically and billing logic that reflects value delivered, not just access granted.
Understanding that helps explain why some incumbents feel stuck. They can bolt on AI features, but they can’t easily rewire pricing, internal incentives, and core architecture without disrupting their own cash flow. It’s the same quiet trap that made it hard for on-premise software vendors to embrace cloud subscriptions in the 2000s. The new model undercut the old foundation.
Meanwhile, AI-first startups aren’t carrying that weight. They assume models will get cheaper and more capable. They build orchestration layers that can swap between providers—Google DeepMind today, OpenAI tomorrow—depending on cost and performance. They treat models as commodities and focus on workflow control, proprietary data, and feedback loops.
That layering matters.
On the surface, a model generates text. Underneath, a control system evaluates that output, checks it against constraints, routes edge cases to humans, logs outcomes, and retrains prompts. That enables something bigger: semi-autonomous systems that improve with use. But it also creates risk. If the evaluation layer is weak, errors compound at scale. Ten bad responses are manageable. Ten thousand automated decisions can be existential.
Critics argue that the AI-first framing is overhyped. After all, most users don’t care about infrastructure—they care whether the product works. And incumbents have distribution, trust, and data. That’s real. A company like Microsoft can integrate AI into its suite and instantly reach hundreds of millions of users. That distribution advantage is hard to ignore.
But distribution amplifies architecture. If your core systems weren’t designed for probabilistic outputs—responses that are statistically likely rather than deterministically correct—you run into friction. Traditional software assumes rules: if X, then Y. AI systems operate on likelihoods. That subtle difference changes QA processes, compliance reviews, and customer expectations. It requires new monitoring tools, new governance frameworks, new mental models.
Early signs suggest the companies that internalize this shift move differently. They hire prompt engineers and model evaluators alongside backend developers. They invest in data pipelines that capture every interaction for iterative improvement. They measure latency not just as page load time but as model inference plus retrieval plus validation. Each layer adds milliseconds. At scale, those milliseconds shape user behavior.
There’s also a hardware layer underneath all of this. The surge in demand for GPUs from companies like NVIDIA isn’t just a market story; it’s an infrastructure story. Training large models requires massive parallel computation. In 2023, training runs for frontier models were estimated to cost tens of millions of dollars—an amount that only well-capitalized firms could afford. That concentration influences who can be AI-first at the model layer and who must build on top.
But here’s the twist: being AI-first doesn’t necessarily mean training your own model. It means designing your system as if intelligence is abundant and cheap, even if today it isn’t. It means assuming that reasoning, summarization, and generation are baseline capabilities, not premium add-ons. The foundation shifts from “how do we add AI to this workflow?” to “if software can reason, how should this workflow exist at all?”
That question is where the real cycle begins.
We’ve seen this pattern before. When cloud computing emerged, some companies lifted and shifted their servers. Others rebuilt for distributed systems, assuming elasticity from the start. The latter group ended up defining the next era. Not because cloud was flashy, but because their foundations matched the medium.
AI feels similar. The loud demos draw attention, but the quiet work—rewriting data schemas, rethinking pricing, rebuilding monitoring systems—determines who compounds advantage over time.
And that compounding is the part most people miss. AI systems improve with feedback. If your architecture captures structured signals from every interaction, you build a proprietary dataset that no competitor can easily replicate. If your AI is just a thin layer calling a public API without deep integration, you don’t accumulate that edge. You rent intelligence instead of earning it.
There’s still uncertainty here. Model costs are falling, but not evenly. Regulation is forming, but unevenly. Enterprises remain cautious about autonomy in high-stakes workflows. It remains to be seen how quickly fully agentic systems gain trust. Yet even with those caveats, the infrastructure choice is being made now, quietly, inside product roadmaps and technical hiring plans.
The companies that treat AI as a feature will ship features. The companies that treat AI as a foundation will rewrite workflows.
That difference won’t show up in a press release. It will show up in margins, in speed of iteration, in how naturally a product absorbs the next model breakthrough instead of scrambling to retrofit it.
When everyone was looking at model benchmarks—who scored higher on which reasoning test—the real divergence was happening underneath, in the plumbing. And if this holds, the next cycle won’t be decided by who has the smartest model, but by who built a system steady enough to let intelligence flow through it. @Vanarchain $VANRY #vanar