The hottest AI Strategy Substack posts right now

And their main takeaways
Category
Top Technology Topics

8 Best Practices for AI Strategy Development By An AI—Strategist

High ROI Data Science • 158 implied HN points • 13 Oct 24
🕹 Technology AI Strategy
  1. AI is changing how we think about technology, moving beyond just improving what we have to creating entirely new ways to interact with it. This means businesses need to look for big, new opportunities, not just small tweaks.
  2. Having a strong data strategy is key for successful AI projects. This involves treating data as an important asset, gathering context, and making sure it's easy to access for training AI models.
  3. It's important to develop real, functional AI products that deliver clear value. Companies should focus on creating products that solve specific customer problems rather than just showing off cool technology.

#53 - Forward Deployed AI Engineers: Great Expectations?

OSS.fund Newsletter • 18 implied HN points • 19 Mar 26
💼 Business AI Strategy
  1. Forward Deployed Engineers can build and embed AI tools, but they alone can’t rewire how a company actually works; enterprise AI is mainly an organizational change problem, not just a deployment problem.
  2. Companies need an internal, load-bearing layer—functional leaders, process owners, risk, HR, finance and exec sponsors—to redesign workflows, decision rights, incentives and vendor boundaries for AI to stick.
  3. The real talent gap will be people who can translate AI capability into operating-model change under real constraints, and the biggest advantage will come from making governance and the organization ready for AI, not just adding models to workflows.

February 24: come have lunch with us

Respectful Leadership • 54 implied HN points • 21 Feb 26
💼 Business AI Strategy
  1. A lunchtime event on February 24 in NYC will bring people together to discuss how AI is changing business, with abundant healthy food and pizza provided.
  2. Speakers will share practical AI use cases like automating residential building permits and warn about legal pitfalls, including the risk of losing attorney-client privilege when using AI tools.
  3. Talks will also cover startup and agency strategy — who to hire early (X-shaped people), how to integrate outside agencies, and new go-to-market opportunities driven by AI.

#51 - Two Detailed Approaches to Pilot-to-Prod In Enterprise AI

OSS.fund Newsletter • 56 implied HN points • 05 Mar 26
🕹 Technology AI Strategy
  1. Fixing pilot-to-prod needs two bridges: engineering and risk controls to make pilots safe and evidence-backed, and org redesign of operating model, decision rights, and roles so AI actually changes outcomes.
  2. A focused human pod sprint with clear owners and cross-functional roles can rapidly triage pilots, create workflow-truth pages, and deliver repeatable production gates in weeks rather than months.
  3. A hugent model pairs humans for judgement with tightly constrained agent workers to automate inventory, evidence assembly, and continuous checks, giving higher throughput and a persistent triage pipeline but requiring strict safeguards and org changes.

OpenAI's Enterprise AI Strategy Is Coming Together (2026)

Enterprise AI Trends • 168 implied HN points • 21 Jan 26
💼 Business AI Strategy
  1. OpenAI is shifting from selling raw API tokens to outcome-based, value-sharing deals where it takes a percentage of the value its models help generate.
  2. After cutting inference costs and building ad and free-tier infrastructure, OpenAI is reaccelerating enterprise efforts with consulting, partnerships, and senior sales hires to win back customers.
  3. Combining value-based pricing with service-led sales and org changes aims to prevent model commoditization, capture more application value, and raise switching costs for rivals and clients.
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The specialization moat

Generating Conversation • 116 implied HN points • 22 Jan 26
💼 Business AI Strategy
  1. Betting on the hardest, hardest-to-adopt problems builds a durable moat because unique customer contexts and deep integrations create institutional data and barriers that competitors can’t easily replicate.
  2. Agents that accumulate tenure inside a company become increasingly valuable and sticky — their historical experience speeds up troubleshooting and can replace senior human expertise, delivering big economic ROI even at imperfect accuracy.
  3. Combining cross-customer pattern learning with high-touch, customized implementation and social proof creates a process and technical moat, making solutions harder to displace and easier to expand into adjacent workflows over time.

How to crush AI native startups (as an incumbent): Part 1

Enterprise AI Trends • 21 implied HN points • 07 Dec 25
💼 Business AI Strategy
  1. Big incumbents are building playbooks to defend their enterprise market share from AI-native startups.
  2. Their main play is to force startups into expensive pricing and capital wars, turning competition into a high-stakes fight of resources.
  3. Pricing for enterprise AI (especially token pricing) is becoming a frontline battleground in 2026, with M&A and product moves set to follow.

AI isn't a feature of your product

Sunday Letters • 139 implied HN points • 25 Feb 23
💼 Business AI Strategy
  1. AI should be seen as a platform, not just a feature of your product. Treating AI as a foundation can lead to more innovative and valuable solutions.
  2. The real potential of AI comes from creating products that can't function without it. This approach can lead to significant advancements and new possibilities.
  3. Ask 'what if' questions to explore the full potential of AI. This mindset can help you think creatively about building solutions for the future.

E6 - 🏰 Building a Moat for Your AI Startup 🏰

The Product Channel By Sid Saladi • 10 implied HN points • 15 Dec 24
💼 Business AI Strategy
  1. Building a competitive 'moat' is crucial for AI startups to protect themselves from competitors. It means having unique advantages that others can't easily copy.
  2. Startups should focus on specific industries or problems to create tailored solutions. This helps them collect valuable data and improve their models over time.
  3. Using proprietary data and building complex systems can strengthen a startup's position. It’s about going beyond just using popular AI tools and making something unique.

Gradient Flow #30: Pricing Data Products, National AI Strategy, Elastic Computing

Gradient Flow • 19 implied HN points • 11 Mar 21
🕹 Technology AI Strategy
  1. Challenges in pricing data products and assessing the value of data are significant for data science and machine learning teams.
  2. The U.S. National Security Commission on Artificial Intelligence report covers essential topics like data infrastructure, adversarial ML, and more, offering valuable insights.
  3. Elastic deep learning with Horovod on Ray and contextual calibration for tools like GPT-3 are advancing efficiency and effectiveness in machine learning.

AI: Make or Build

TeamCraft • 6 implied HN points • 13 Nov 23
🕹 Technology AI Strategy
  1. Focus on a few high-impact AI projects aligned with core company objectives.
  2. Apply AI to your core value proposition for strategic value.
  3. Prioritize building AI capabilities around high strategic value and core value propositions.

Issue #10: Pay Bonus? To Drive AI Copilot Adoption? Sure!

OSS.fund Newsletter • 0 implied HN points • 22 May 25
💼 Business AI Strategy
  1. Offering bonuses can increase the use of AI tools like Copilot in businesses. When people are paid to use these tools, it often leads to better results.
  2. Different roles in a company may require different incentive strategies to promote AI usage. For example, software engineers might benefit from bonuses, while some roles might not need them yet.
  3. Creating a fun and engaging training environment helps employees learn to use AI tools effectively. Simple activities can keep teams motivated and increase their usage.

Inflation data disappoints, notes on Crunchbase's AI strategy, and the latest OpenAI math

Alex's Personal Blog • 0 implied HN points • 10 Oct 24
💰 Finance AI Strategy
  1. September's inflation data showed a 0.2% rise, with the yearly change at 2.4%. This suggests some ongoing economic pressure.
  2. Crunchbase is focusing on AI by enhancing its data tools. They introduced AI-powered search features to improve access to their extensive data.
  3. OpenAI is projected to have significant cash losses but could still become profitable by 2029 with a strong revenue base. The risks of high spending in this sector are considerable.

20240203

Curious futures (KGhosh) • 0 implied HN points • 03 Feb 24
🕹 Technology AI Strategy
  1. Researchers in AI field have varying opinions on the pace of AI development and its impact on humanity.
  2. Tech topics include lobbying for AI regulation and discussions on language and mind connection.
  3. Diverse topics like sand mafias, AI strategy focusing on minimizing risks, and DIY projects like SDR with RP2040 are covered in the post.

Issue #1: Why Enterprises Are Lukewarm on AI Model Providers

OSS.fund Newsletter • 0 implied HN points • 20 Mar 25
💼 Business AI Strategy
  1. Enterprise buyers like AI solutions that fit into their current systems. They prefer options that include security and compliance, rather than just standalone AI models.
  2. Major companies like Microsoft, AWS, and Google are leading the AI market by bundling AI with their existing services. They are seen as more reliable partners compared to AI model providers.
  3. AI model providers need to focus on industry-specific solutions that help businesses improve revenue and efficiency. Simply having the best technology isn't enough for success in the enterprise market.