What does it really take to build a competitive company in 2025?

The latest Stanford AI Index Report offers a compelling answer: the firms leading in performance, agility, and profitability are not those dabbling in AI—they’re the ones built around it. These AI-native organizations are operating on a new logic. They’re not using AI to optimize old workflows. They’re redefining how work gets done altogether.

AI-native doesn’t just mean having a few models deployed in the cloud. It means structuring your company from talent and data to decision-making and distribution around the assumption that intelligent systems are core, not optional.

Stanford’s annual AI Index is one of the most comprehensive global reports on the state of artificial intelligence. This year’s edition provides the clearest signal yet that AI-native companies are not only emerging as leaders, but that their operating model will become the standard. From dramatic cost reductions to massive productivity gains, the infrastructure for scaled AI adoption is now in place, and the firms that started native are reaping the benefits.

This blog breaks down the highlights from Stanford’s report through the lens of AI-native growth. We’ll explore the metrics, trends, and examples that show how these companies are scaling faster, smarter, and leaner, and what traditional businesses must learn if they hope to stay in the game.

Here is a look at how AI is becoming an integral part of modern business and daily operations.

Accelerated AI Performance: Why 2024 Was a Breakout Year for Intelligence at Scale

The 2025 Stanford AI Index Report makes one thing clear: we’re not just progressing, we’re accelerating.

AI performance across core benchmarks has jumped dramatically in just 12 months. In particular, models saw a 67 percentage point increase in performance on MMMU and GPQA, two of the most rigorous multi-step reasoning challenges introduced in 2023. What was designed to test the limits of LLMs is already being outpaced by current-generation models.

But it’s not just about being smarter. It’s about being cheaper, and that’s where the shift becomes even more striking.

The cost to run models at GPT-3.5 performance levels dropped from $20 per million tokens in 2023 to just $0.07 in 2024. That’s a 280-fold reduction in compute cost. What once required a venture-funded lab can now be done with off-the-shelf tools and a credit card. For AI-native startups, that cost compression unlocks experimentation at scale. For enterprises, it redefines the economics of automation.

Equally important is the rise of open-source alternatives. In 2023, closed-source models outperformed their open counterparts by a wide margin. But in 2024, that gap narrowed from 8% to just 1.7%, according to evaluations on MMLU, HellaSwag, and TruthfulQA. In other words, open models are now competitive on most real-world tasks, and in some cases, outperforming commercial APIs.

This is more than just a technical trend. It’s a strategic unlock.

AI-native companies can now:

• Customize and fine-tune models without licensing overhead

• Reduce vendor lock-in

• Ship faster by owning the full model lifecycle

With competitive performance, lower costs, and more flexibility, the playing field has leveled. We’re no longer in a world where only a handful of hyperscalers can afford top-tier intelligence. The infrastructure has democratized and the fastest-growing AI-native firms are building on it every day.

From Pilots to Production: AI Is Now Business Infrastructure

The idea of artificial intelligence as a “future trend” is outdated. In 2024, AI cemented its position as business-critical infrastructure across sectors.

According to Stanford’s latest data, 78% of companies globally now report using AI in at least one function, up from just 55% in 2023. And the shift is especially stark when it comes to generative AI, which more than doubled in adoption year over year.

This isn’t just a software phenomenon. AI is shaping how businesses operate internally and externally:

• Customer support teams are augmenting live agents with AI chat interfaces that now handle the majority of Tier 1 inquiries.

• Sales teams are using AI to qualify leads, generate outreach, and even customize pricing packages.

• Developers rely on code copilots to reduce iteration time, clean up legacy systems, and ship features faster.

• HR teams are turning to AI for job description creation, skills matching, and policy drafting.

The investment numbers reflect this shift. In 2024, global corporate spend on AI topped $250 billion, with nearly $34 billion flowing specifically into generative AI applications. That’s a 30% increase year over year, and a sign that AI is no longer viewed as experimental tech, but mission-critical infrastructure.

Some of the world’s largest companies are going even further, embedding LLMs and autonomous agents into their core workflows. Walmart uses AI to predict demand patterns and restock stores in real time. JPMorgan’s COIN platform continues to save 360,000+ legal hours by reviewing contracts autonomously. And UPS uses generative models to optimize delivery routing and reduce idle fleet time.

What’s more telling is that even non-tech companies are joining the wave. In 2024, the sectors with the fastest AI adoption growth were manufacturing, healthcare, logistics, and public sector services. These traditionally slower-moving industries are being forced to adapt, and AI-native firms are showing the blueprint.

AI is no longer confined to R&D or IT. It’s in the boardroom, the warehouse, and the call center. The companies scaling with AI today are not the ones waiting for perfect models, they’re the ones building adaptive infrastructure and learning loops to deploy fast, iterate often, and outpace legacy systems.

Structural Advantages: Why AI-Native Companies Outperform Their Peers

AI-native companies aren’t just using AI more. They’re structured around it. And that gives them a set of compounding advantages that legacy firms simply can’t match.

Traditional organizations often treat AI as a tool, a feature to bolt onto existing systems. But in AI-native firms, intelligence isn’t layered on top. It’s embedded from the ground up, in the data stack, in team workflows, and even in customer-facing products.

This leads to three major advantages:

1. Speed

AI-native teams iterate faster. With fewer handoffs and smarter automation, they move from idea to shipped feature in weeks not quarters. Their workflows are built for real-time input, continuous learning, and fast deployment. In other words, agility isn’t aspirational—it’s operational.

This speed doesn’t just apply to engineering. Customer insights, marketing experiments, and operational changes happen faster because AI systems continuously surface trends, anomalies, and recommendations. Everyone gets better signal, sooner.

2. Integration

Legacy firms often struggle with fragmented data and siloed tooling. AI-native companies design for interoperability from day one. Their product, data, and growth teams work off shared pipelines, unified analytics, and integrated agent frameworks. That means fewer breakdowns, better collaboration, and smarter decisions across the board.

Many use internal AI agents not just to automate, but to connect workflows, e.g., an agent that summarizes daily support trends and feeds them into product roadmap discussions.

3. Alignment

Perhaps the most underrated edge: cultural alignment. AI-native teams hire, organize, and incentivize around intelligence-first thinking. That creates a different type of company DNA—one where experimentation is expected, data fluency is normal, and feedback loops aren’t bottlenecks but power tools.

This shows up in org design, too. AI-native companies often have no traditional sales team, lean operations, and a flat structure that puts AI agents alongside human contributors in core functions.

In effect, these firms have replaced layers of management and coordination with intelligent systems and it shows in their velocity and margin profiles.

As AI becomes foundational to every workflow, companies that designed around it from day one will keep pulling ahead. The cost of retrofitting legacy systems, not just technically, but culturally, is only rising.

Data and Talent as Compounding Moats

AI-native companies treat data not as exhaust, but as the product.

Where legacy firms often collect data for compliance or record-keeping, AI-native organizations engineer every workflow to generate structured, useful signal. Every click, query, and resolution becomes training fuel. Over time, this produces a self-improving loop: more users means more data, which means smarter models, which means better outcomes—and even more users.

This flywheel is especially powerful in vertical SaaS, where AI-native companies can tailor models to specific industries and domains. Instead of relying on generic LLMs, they fine-tune on proprietary data: logistics tickets, insurance claims, supply chain breakdowns, legal templates. The result? Rapid gains in accuracy and usability that competitors can’t easily replicate.

But data alone isn’t enough without the talent to activate it.

That’s where AI-native firms shine again.

According to the Stanford AI Index, private companies developed nearly 90% of top-tier AI models in 2024, up from 32% just five years ago. The talent pipeline has shifted decisively from academia to the private sector.

The best AI-native organizations are magnets for that talent because they offer what researchers increasingly want: the chance to deploy models in production, work with feedback from real users, and push the envelope on scale.

They also move differently. Instead of bloated ML teams isolated from product, AI-native firms cross-staff engineers, product managers, and designers into small pods. These teams are responsible for outcomes, not just outputs. That structure rewards generalists who can prototype, test, and ship rapidly, not just model-tuners who write papers.

Compensation matters too. In a market where top AI researchers can earn over $1 million per year, AI-native startups compete on ownership, mission, and velocity. Engineers want to work where the model gets shipped, not shelved.

Together, data and talent form an economic moat. The more data an AI-native firm has, the better its models. The better its models, the more attractive it becomes to users and builders. And the stronger the flywheel becomes.

This kind of compounding edge can’t be bought overnight. It must be built. And AI-native companies are already years into that journey.

AI Agents Are Reshaping the Operating Model

AI-native companies aren’t just integrating models. They’re operationalizing agents.

Instead of using AI to assist humans, many of these firms are designing workflows where AI agents take full ownership of tasks—acting, not just advising.

The shift became clear in 2024. According to REBench, a benchmark developed to evaluate real-world AI agent performance, the top agents outperformed human experts on short-duration knowledge work by up to 4x. That includes tasks like:

• Responding to support tickets

• Researching customer queries

• Generating marketing briefs

• Reviewing invoices and procurement docs

In AI-native orgs, these agents aren’t side experiments. They’re essential operators. For example:

• In customer support, agents like Beam AI’s virtual CSAs resolve up to 80% of inbound issues without escalation.

• In finance ops, agents reconcile payment errors, follow up on missing invoices, and alert teams to contract anomalies.

• In HR, they draft job descriptions, schedule interviews, and walk new hires through onboarding, all autonomously.

The economic implications are huge. In sectors like BPO, these agents are enabling small teams to handle enterprise-scale workloads, compressing costs while improving SLAs.

And critically, they free up human workers to focus on oversight, edge cases, and strategy. No one is suggesting AI replaces every role, but it’s changing what those roles are.

There’s also an infrastructure shift underway. Many AI-native companies now maintain agent orchestration layers, prompt versioning systems, and feedback reinforcement loops as part of their core stack. They treat agents like employees: onboarded, trained, evaluated, and continuously improved.

This changes how companies think about growth. Instead of hiring headcount, they’re scaling through intelligence. One team can run three functions if 70% of the workflows are handled by agents.

It’s not just cheaper, it’s more resilient. Agents don’t get tired, don’t forget context, and can learn at machine speed. That gives AI-native companies a structural speed and margin advantage that adds up quickly over time.

Regulation, Trust, and the AI-Native Advantage

As governments catch up to the pace of innovation, regulation is becoming a front-line concern. But while many companies brace for compliance costs and slowdowns, AI-native firms are moving differently.

According to the Stanford AI Index, the number of U.S. state-level AI laws passed more than doubled in 2024, from 61 to 131. Globally, the number of countries introducing AI regulations rose to 75, with sweeping frameworks like the EU’s AI Act set to become enforceable within the year.

This surge in oversight would typically introduce friction. But for AI-native companies, it’s often a tailwind.

Why? Because trust and transparency are part of their architecture.

Many AI-native teams build with explainability and auditability from day one. They maintain model logs, bias detection checks, and fallback protocols not because they have to, but because it improves product quality and builds customer trust. Companies that started with this mindset are better positioned to meet evolving standards.

They also move faster. When explainability becomes a compliance requirement, retrofitting a legacy black-box model can take months. For AI-native firms, those features are already integrated.

In other words: regulation slows down the laggards. It favors the prepared.

Conclusion: The Future Belongs to the AI-Native

The companies scaling fastest today are doing it with fewer people, more automation, and radically higher margins. They’re tapping into AI not just as a technology, but as a new business foundation.

As the Stanford AI Index makes clear, the gap between early adopters and everyone else is turning into a chasm. Those who delay won’t just fall behind. They’ll be competing in a fundamentally different game.

The age of AI-native companies is here and the playbook is changing for good.

→ Read the full Stanford AI Index Report