The hottest Enterprise Tech Substack posts right now

Waymo appears to be far ahead in self-driving technology and looks likely to be a major player as people begin to trust autonomous cars over human drivers.
Frontier AI models are improving fast and will probably overtake domain-specific, startup-tuned systems, making it risky to rely only on human experts for legal or medical advice.
Large organizations should hire an AI "keeper-upper" to evaluate and roll out useful tools, because incumbents that refuse to rethink their mission will miss big productivity gains.

Large organizations struggle to deploy AI quickly because of bureaucracy, security concerns, and the technology’s current limitations.
Individuals can adopt powerful AI tools on their own to analyze data and build workflows, getting useful results without waiting for corporate approval.
This split will create big performance gaps between people who use AI well and those who don’t, and will pressure slow-moving companies to change in uncomfortable ways.

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Committing early to vendors or large enterprise deals risks big sunk costs and being locked into outdated tech, so negotiate harder and consider building more instead of buying quickly.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.

Frontline workers are skipping expensive corporate AI and getting real work done with cheap consumer tools, so formal platforms often sit unused.
Top-down mandates and one-off programs don’t stick; find the people already using AI and build sandboxes and practices around their work so useful systems emerge.
Investing in human readiness is essential because judgment, oversight, and experience matter as models drift; without that investment AI pilots will launch loudly and then fade away.

AI-powered semantic layers can query messy, fragmented systems and deliver unified read-only insights fast, making many long master-data consolidation projects unnecessary for read-heavy analytics.
You still need traditional MDM for writes, transactional consistency, and regulatory requirements like GDPR, because semantic abstraction doesn’t tell you where to update or delete authoritative records.
A practical approach is to segment use cases into read vs write, run semantic tests on top business questions to capture immediate value, and invest in targeted MDM only for the write/compliance-critical scenarios.

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Chat platforms are becoming agent orchestration hubs where humans and bots work together in real time, and organizations will need higher-level "super agents" to connect and manage isolated agent workflows.
New agent ecosystems introduce fresh risks and human dependencies—agents forming their own social networks and services that hire people for tasks raise security, legal, and ethical concerns, and rogue or exploitable agent chains are a real threat.
Widespread agent adoption will reshape how software is developed and how open source is consumed, shifting teams toward autonomous observe-orient-decide-act workflows and transforming open source projects to serve agent-driven use cases rather than disappearing.

Recent AI updates have sparked excitement and frustration; everyone interprets them differently, like a Rorschach test.
The improvements in AI tech are impressive, particularly in multimodality, but their impact varies between consumer and enterprise applications.
The AI market is growing rapidly, with hype increasing and many companies looking to innovate, but there are still big questions about the future and how to stay competitive.

73% of survey respondents expect their enterprise tech budgets to stay flat or increase in 2023.
65% of survey respondents plan to dedicate increasing budgets to experimental budgets for new business generation.
Organizations reducing budgets will focus on vendor consolidation and optimizing SaaS licensing, emphasizing the need for early customer health assessments.

Consumer AI will increasingly look like television: rich, video-first generative experiences that let you personalize, participate in, and even star in episodic worlds.
Enterprise AI will drive down the cost of services by automating labor and manual workflows, turning many expensive, human-driven industries into software-like businesses.
Because efficiency tends to increase demand, AI-driven cost drops will expand access and grow markets rather than simply reducing spending.

Big tech companies like AWS, Microsoft, and Google are changing how businesses buy tech. They're not just providing cloud services anymore; they're also influencing what tools and services companies use.
Smaller IT and SaaS companies are now reliant on these big tech firms to reach customers. This means they might lose direct contact with their customers and end up paying high commissions to sell on these platforms.
To stay competitive, companies must avoid relying too much on one tech giant. Diversifying their services and creating unique offerings can help them survive and thrive in this new landscape.