Build Like an AI‐First Company

⏱️ Your Morning Brief (TL;DR)

Welcome back!

ICONIQ Capital just dropped its 67‑slide "2025 State of AI Report", built from interviews and surveys with 300 software executives. It’s the clearest snapshot yet of how leading companies budget, staff and ship GenAI.

The TL;DR

1. AI-focused Roadmaps & Resourcing: Clear AI leadership, prioritized hiring, and AI-focused roadmaps drive success.

2. People matters: AI requires expertise, cross-functional collaboration to sustain innovation.

3. Embedded AI workflows: AI’s baked into every workflow and productivity unlock.

In today’s brief

• How AI-first companies are prioritizing AI

• What AI-first culture looks like

• Where AI-first companies are focusing efforts

• 7 curated AI reads

Let’s dive in 🤖

 💡 This Week’s Deep Dive

We breakdown the report to focus on how leading AI companies are prioritizing AI, setting an AI-first culture, and where they’re focusing their efforts.

🥇 Being “AI-first” Requires Prioritization of AI

• Growing AI Budget: On average, companies are allocating ~10-20% of their R&D budget to AI development. As AI products scale, the cost of talent tends to go down as a total proportion of spend; conversely, infrastructure and compute costs tend to increase as products start to see market traction

• AI Roadmaps: For high-growth, AI-first companies, 30-45% of their roadmaps represent AI initiatives compared to AI-enabled companies with 20-25%.

• Resource Prioritization: On average, companies plan to have 20-30% of their engineering team focused on AI, with high-growth firms dedicating a larger proportion.

👤 AI-Focused Culture

• Clear AI Leadership: Many companies have dedicated AI leadership by the time they reach $100M in revenue likely due to increasing operational complexity and the need to have a centralized owner for AI strategy

• AI Hiring: Most companies currently have dedicated AI/ML engineers, data scientists, and AI product managers, with AI/ML engineers taking the longest time on average to hire

• Productivity Budget: Companies are spending from 1-8% of total revenue on productivity tools. Budget for productivity spend is

• Slower Enterprise Adoption: While around 70% of employees have access to various AI tools for internal productivity, only ~50% of employees are using AI tools on an ongoing basis with adoption more difficult in mature Enterprises ($1B+ revenue)

🔄 Use Cases & AI Deployment

• Deployment Challenges: The biggest challenges facing organizations deploying AI for internal use cases are often strategic, with 46% finding difficulty identifying use cases.

• Third-Party AI vs. Fine Tuning: Most companies building AI applications are relying on third-party AI APIs; but a larger proportion of high-growth companies are fine-tuning models to develop proprietary models.

• Model Choice: When choosing models in product development, Accuracy is the most important consideration factor, followed by cost and ability to fine-tune.

• Evaluation vs. Deployment of Agents: Almost half (47%) of high-growth companies are deploying agents compared to the 32% of companies evaluating agents.

• Infrastructure costs: API usage fees top the cost concerns (70%) and most challenging to control and unpredictable. To manage these costs, companies are moving to open-source models.

GPTLDR Takeaways

Here’s what the winners are doing:

• Name an AI Owner: Centralized decisions on stack, spend and talent.

• Budget Bigger: Leaders are spending 10‑20 % of R&D.

• Bake AI Into Roadmaps: Ships differentiation, not add-ons, allocate 30-45% of your roadmap towards AI features.

• Staff Up: Put your brightest talent, roughly 20‑30 % of engineers on AI projects.

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📚 Interesting Reads

• Leading in an AI-driven World (Link)

• Five Key Insights from Strategy Building to Scaled Operations (Link)

• Ford CEO Predicts AI to Replace ‘literally half of all white-collar workers’ (Link)

• CEOs Have a New AI Flex (Link)

• The CEOs Cheat Sheet to the AI Revolution (Link)

• The Top 20 CEOs Defining the Future of AI (Link)

• Transforming SaaS with AI: Insights for CIOs and CTOs (Link)

 ➜ Until Next Week

Generative AI is no longer a feature race, it’s an operating model shift. Enterprises that copy the speed, telemetry, and disciplined cost structures of AI‑native leaders will out‑learn competitors and lock in durable advantage.

Stay curious,
The GPTLDR Team