The hottest AI adoption Substack posts right now

Buyers have shifted — they are more informed, hypothesis-driven, and expect fast, measurable results instead of broad discovery or generic workshops.
AI-native competitors win by showing up narrow and pragmatic, offering tight scopes, quick proofs, and practical data-governance that remove friction.
Traditional IT services can stay relevant by upgrading commercial skills with hands-on drills that turn messy account context into next steps, tighten proposals, handle governance, and prove value quickly.

Chinese open models—especially Qwen—now dominate downloads, finetunes, and general adoption across the ecosystem, often outpacing many other providers combined.
New entrants and recent Western releases show only limited adoption so far, with older Western models like Llama still widely downloaded while GPT-OSS shows early promise but hasn’t shifted overall usage.
The clearest competitive opportunity is at large model scales, where DeepSeek and a few others outperform Qwen’s big models, but Chinese models still lead on benchmarks with only a few competitors getting close.

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.

Work is becoming uncoupled from full-time jobs — companies will use more project-based hiring, freelancers, fractional roles, and AI agents to get work done.
The future workforce will be a blend of humans and AI agents, with many people working fractional hours or as contractors, which changes benefits, hiring, and how work is managed.
Leadership and organizations must reinvent: leaders need to learn and unlearn quickly and shift from control to influence. Companies should go AI-first, hire talent from anywhere, and become smaller, more agile, and distributed.

The job market is brutal for candidates right now — mass one‑click applications and automated filters mean many people never get a human to see their CV, and hiring processes drag on for ages.
Employers are overwhelmed by the surge in applicants and are even borrowing dating‑app tricks to help certain candidates stand out, which risks squeezing out mid‑market talent.
This is a year of reckoning for AI: businesses must use AI to augment employees rather than replace them, because augmentation is more likely to deliver real productivity gains over the next few years.

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Talent is the primary sustainable advantage: skilled, motivated people create and preserve innovation, service, and brand experiences. AI and other tools only multiply value when they are in the hands of well-trained talent.
Firms must invest heavily in training, reskilling, and rewarding people alongside their AI spending, because technology and data alone won't create differentiation. Leaders and managers should be measured and compensated on how well they attract, develop, and retain talent.
To attract, retain, and help people thrive, focus on pay, recognition, and autonomy; purpose, values, and connection; and freedom, identity, and growth. Employees also act as advocates and their satisfaction should be tracked with tenure, turnover, surveys, and other people metrics.

Standardizing build and deployment pipelines and automating SRE tasks removes repetitive work so large engineering teams can move like startups and focus on high‑value problems.
AI in 2026 shifts from demos to real procurement: organizations will budget heavily for AI and should prioritize applying models to new workflows while enforcing strong security and governance.
Pausing deploys (like Friday freezes) often increases risk by accumulating untested changes; regular, practiced deployments build resilience and reduce surprise failures.

Software pricing models have evolved over the years, from on-prem software to cloud-native software to AI-powered software.
AI is leading to outcome-based solutions in software pricing, where customers pay based on delivered results.
Outcome-based pricing aligns customers and vendors, emphasizing value delivery and flexible scaling.

Career-smart leaders focus on making AI part of existing workflows instead of treating it as just a shiny new tool. This helps teams trust AI more and see real benefits.
It's important to measure success not just by AI engagement but by how much it improves cycle times and closes rates. Focusing on practical outcomes can boost your career.
Adopting AI should start small and be stress-free for users. Leaders who manage expectations and simplify processes tend to create better organizational readiness for change.

To succeed with enterprise AI, focus on managing human change and understanding security needs. People often struggle more with adopting new tools than the technology itself.
Feedback shows that financial discussions and ROI measurement are crucial for AI projects. This means you need to keep those conversations at the executive level to get support.
Choosing the right platform for sharing insights matters. Moving to Substack was about cutting through the noise and connecting with the right audience more effectively.

Daron Acemoglu and Simon Johnson's book 'Power and Progress' delves into power dynamics in economics, emphasizing the role of political institutions in shaping economic value distribution.
Acemoglu and Johnson present a view on AI automation, suggesting that AI replicated human tasks but does not offer significant productivity gains. They express concerns about automation leading to job loss without substantial benefits.
Contrary to Acemoglu and Johnson's view, it's argued that AI can bring massive productivity improvements and that current AI adoption signifies significant advancements. The book's projections about AI's negative impacts are viewed with skepticism.