---
title: "“AI-native” startups"
author: "Mani Sandher"
published: "2025-05-20T04:53:29.000+00:00"
modified: "2025-05-20T04:53:46.000+00:00"
source: "https://www.linkedin.com/pulse/ai-native-startups-mani-sandher-eldde"
cover_image: "https://media.licdn.com/dms/image/v2/D4E12AQFbL8IXJg7pzA/article-cover_image-shrink_720_1280/B4EZbr_6n4HAAI-/0/1747716131002?e=2147483647&v=beta&t=sGjbV1hhyVmcJs0KPaQz9m7ILChKPtdvGJsUpaKBvqM"
---

# “AI-native” startups

Source: [https://www.linkedin.com/pulse/ai-native-startups-mani-sandher-eldde](https://www.linkedin.com/pulse/ai-native-startups-mani-sandher-eldde)

Published: 2025-05-20T04:53:29.000+00:00

![Cover image](https://media.licdn.com/dms/image/v2/D4E12AQFbL8IXJg7pzA/article-cover_image-shrink_720_1280/B4EZbr_6n4HAAI-/0/1747716131002?e=2147483647&v=beta&t=sGjbV1hhyVmcJs0KPaQz9m7ILChKPtdvGJsUpaKBvqM)

The next surge of creative destruction will come from “AI-native” startups—companies built from day one around large-scale machine learning, automated decision-loops and streaming data, not retro-fitted with them. Ten years ago software ate the world; in the next ten, self-improving software will chew through industries that still rely on manual throughput or linear R&D cycles. Where should leaders look first? Three clues: (1) an expensive latency or error problem, (2) mountains of digital exhaust, and (3) incumbents constrained by regulation, legacy IT or head-count economics.

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### 1 Law: from billable hours to billable outcomes

Generative-AI platforms are already drafting clauses, surfacing precedents and forecasting case trajectories in minutes rather than days. The fund-raising signals are loud: Harvey AI, barely three years old, is in late-stage talks to raise another $250 million at a **$5 billion** valuation, driven by partnerships with PwC and dozens of elite firms. If a third of routine legal work can be automated—as Gartner now estimates—the traditional leverage model of associate pyramids begins to wobble.

### 2 Drug discovery: compressing a decade to a year

Wet-lab pipelines once ran on trial-and-error chemistry; AI-native “TechBio” startups prefer inverse-design. Alphabet spin-out **Isomorphic Labs** just secured **$600 million** to expand its generative protein engine, promising to identify lead compounds in weeks rather than years. Every day the model trains on structural data it becomes harder for a late-moving pharma giant to catch up.

### 3 Warehouse robotics: the labour crunch accelerator

E-commerce SLAs have collapsed from days to hours, but fulfilment centres still chase seasonal labour. Covariant’s neural-net pick-and-place systems now clear more than **800 boxes an hour** for early adopters, running 24/7 without extra shifts. Start-ups that own fleets of progressively learning robots build a data moat with every pick.

### 4 Finance for small firms: credit at the speed of an API

Open-banking mandates expose granular transaction feeds; AI-native neobanks underwrite loans in seconds and retrain nightly. Venture finance into “AI-native fintech” topped **$9 billion in 2024**, according to Forbes’ latest AI 50 list, and continues to rise. Incumbent lenders, weighed down by COBOL cores and multi-week KYC, are starting a lap behind.

### 5 Process heavy back-offices: the rise of the DTO

By 2026 Gartner expects a quarter of global enterprises to maintain a **Digital Twin of the Organization**—a live model of every order, invoice and exception. AI agents grafted onto these twins already cut hire-to-retire or order-to-cash cycles by **40 percent** in pilot programmes. Any sector that still depends on swivel-chair work is fair game.

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### Why the incumbents’ usual defences are weaker this time

- **Scale is rented, not bought.** Foundation-model APIs, robotics-as-a-service and edge-cloud pipelines let start-ups assemble capabilities that once demanded nine-figure capital budgets.
- **Models learn faster than humans hire.** Every interaction becomes training signal; switching costs rise daily.
- **Regulation cuts both ways.** Policy-as-code and privacy-preserving ML allow newcomers to embed compliance inside their stack, while legacy players shuffle PDFs between departments.

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### How to spot the tremor before the earthquake

1. Map any workflow where information is copied, reconciled or queued for more than a few hours.
2. Ask where proprietary or open data sets have reached “escape velocity”—enough volume for an AI insurgent to train a viable model on day one.
3. Check margins that depend on head-count rather than differentiated IP: if revenue scales linearly with people, the business is exposed.

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### What leaders can do now

*Run a one-day “AI-native audit.”* For each critical value stream ask: **Could a founding team with an LLM credit card, a robotics SDK and six months of runway undercut our cycle time by half?** Where the answer feels uncomfortable, assume someone is already pitching your board’s pain point to a venture capitalist.

The good news: incumbents own customer trust, domain expertise and deep moats of historical data. The bad news: those assets lose value if they’re not reshaped into continuously learning systems—fast.