Daniel Miessler is a cybersecurity and AI engineer turned founder, based in the San Francisco Bay Area. He shares insights on cybersecurity, artificial intelligence, technology, and human behavior through essays, tutorials, and technical content on his blog.
174 articles from this blog
Explores how AI is not the villain in job displacement; companies have always sought to automate work, and the real issue is reliance on corporate jobs.
Analysis of AI layoffs: companies replace mediocre employees with AI tools for top performers, not replace top talent.
A voice transcript conversation about David Deutsch's knowledge theory and the PAI algorithm for personal AI infrastructure.
Explains why AI models optimized for coding improve at all tasks, treating coding as a meta-skill for structured problem-solving.
Analysis of weak vs. strong enterprise AI rollouts, highlighting the importance of solid guidance and practical examples over vague mandates.
Explores how AI enables replacing SaaS tools with custom-built solutions, forcing companies to rethink their value and defensibility.
Analysis of Jensen Huang's interview with Dwarkesh Patel on China chips, highlighting risks of supplying advanced tech to adversaries.
A reflection on the Mythos AI model, arguing it's just the next step in rapid AI evolution, not a shocking breakthrough.
Explores the future of personal AI, arguing we're moving toward a single digital assistant with full context about our lives.
Explains good vs bad harness engineering for AI, emphasizing context over prescriptive instructions.
Argues that AI doesn't need to be perfect to disrupt business and cybersecurity, as most current systems are mediocre (3/10) and AI at 5/10 can still dominate.
Analysis of Mythos AI's impact on knowledge work, arguing its cybersecurity prowess signals a broader disruption to all white-collar jobs.
Analysis of rising AI inference costs and the need for multi-model strategies to manage expenses.
How autonomous AI pipelines can compress decades of cross-domain knowledge transfer into days, with 23 verified historical examples.
Explores key AI ideas like autonomous component improvement, intent-based engineering, and transparency shaping the future of technology.
Analysis of AI inference costs ending subsidies, predicting a split between expensive frontier models and cheap open-source alternatives.
Explores the confusion between Soft AGI (emulating human replacement) and Hard AGI (true human-level learning generality).
AI reveals most knowledge work is scaffolding overhead, not core tasks, making it easy to automate.
Argues that AI will replace knowledge work, challenging the narrative that humans are irreplaceable, and explains why this is beneficial.
Argues for using general SOTA AI models over custom, specialized ones, predicting cheaper, open-source general models will dominate.
