The hottest Tech Strategy Substack posts right now

Many large cybersecurity companies risk losing relevance if they keep selling into shrinking, legacy markets and only bolt AI onto old architectures instead of rethinking their products.
AI lets security teams build and deploy code and automated remediation themselves, turning security from gatekeepers into builders and reducing the need for big, seat‑based security products.
Security budgets and ownership are moving into engineering so tools must prove clear, high‑impact value and be API‑first and fast to deploy, or they'll be replaced by AI‑native challengers and in‑house solutions.

Software products and teams aren’t like stocks — they’re tightly entangled, slow to change, and hard to reallocate without big, lasting consequences.
Lean and centralized portfolio approaches can restore flow and stabilize teams, but they often still assume capacity and flow are more liquid and reversible than they really are.
In product-led tech organizations, portfolio decisions naturally live with product leadership and require organizational design choices (team topology, hiring, platform investment) rather than just a separate PMO doing prioritization.

Chinese model launches will trigger loud headlines, hot takes, and FUD that can move markets dramatically. Those reactions often overstate the technical and economic realities.
Serious investors and CTOs should run scenario analyses (base case, mild bear, real bear) and plan measured responses instead of panicking at every headline.
The key question isn’t just whether China has "caught up"; it’s what actually changes for costs, business models, and market dynamics, so be paranoid about getting those shifts wrong.

When AI progress is exponential, waiting can pay off because the last mover often gets a much better product and avoids wasted effort.
Committing early to vendors or large enterprise deals risks big sunk costs and being locked into outdated tech, so negotiate harder and consider building more instead of buying quickly.
Patience is a deliberate strategic choice alongside build and buy: decide what to wait on, what to experiment with now, and use waiting to watch paradigm shifts while you focus resources elsewhere.

Big tech is building lots of AI infrastructure not because it’s betting the farm on core AI products, but to capture the rents from the AI boom by selling infrastructure and services.
The AI labs are the ones digging for breakthrough models and customer demand, but core AI products may have low margins and fickle users, so those businesses carry higher risk of a bust.
Cloud and platform companies often commoditize or give away core AI tools to protect their high‑margin businesses, and investors are increasingly valuing firms based on real cash generation rather than AI hype.

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OpenAI has released two new open-weight models, making them more accessible for developers and small companies. This is a significant shift since it's their first open release since GPT-2.
The performance of these new models is impressive, potentially competing with OpenAI's premium API offerings at a much lower cost, which could disrupt the current market.
OpenAI's release marks a positive change for open-source AI in the West, allowing more competition against models from China, but it also raises questions about the future of open models in the industry.

Apple is focusing on AI that works directly on devices instead of relying on cloud-based systems. This helps them maintain user privacy and keep costs down.
By not competing in the ChatBot market, Apple avoids high expenses and risks associated with developing large language models, which many other tech companies are currently pursuing.
The main challenge for Apple is to improve the execution of their AI features. They need to treat AI as a core part of their strategy and ensure these features work seamlessly for users.

Major media companies are making equity and licensing deals with AI labs so their characters and franchises can be used inside consumer AI products.
As model quality improvements become harder for users to notice, AI firms are increasingly buying exclusive IP and data access instead of just chasing benchmark gains.
Those exclusive IP deals can shut rivals out and reshape streaming and studio battles, turning content ownership into a strategic moat for consumer AI.

AI strategies are often unhelpful because things change so quickly. It's better to focus on just using more AI instead of getting stuck in endless planning.
Experts in each department should choose the AI tools they need, rather than leaving it up to a central committee. This way, the people closest to the work can make the best decisions.
Not every AI tool will work perfectly right away, and that's okay. Being open to trying different tools will help teams learn and improve their choices over time.

AI adoption follows a J-curve: there’s early hype, a frustrating trough where things feel slower, and then real productivity gains once people and processes adapt.
Forcing AI can work for a few big-brand companies, but heavy mandates usually breed resentment and risk losing good people, so coercion is risky for most orgs.
Help adoption by investing in training, time to experiment, and the right tools, and make a clear business case for costs versus expected gains to get finance on board.

Balkanization refers to splitting a region into smaller, competing parts, which can cause issues. In tech, dividing teams can create confusion and inconsistency.
When tech teams work independently with different assumptions, it can lead to problems like bugs and compatibility issues. Teams should ideally work together to maintain a unified product.
Maintaining a single product vision is crucial, so it's important to ensure that all teams align on the same goals and methods. This helps prevent issues down the line.