This newsletter covers SpaceX’s $6.3 billion AI compute contract, a new exploit targeting Cisco devices, and Microsoft’s push for AI-driven cloud observability agents. It also highlights ongoing Linux network‐share headaches, the role of LLMs as software front ends, and the link between AI adoption and security incidents.

• SpaceX signed a $6.3 billion deal for custom AI compute hardware to power in-house model training and inference.
• Attackers are actively exploiting a newly disclosed Cisco IOS vulnerability to deploy ransomware on enterprise routers.
• Microsoft and others are moving toward "agentic observability," where AI agents triage anomalies and suggest fixes instead of relying on static dashboards and alerts.
• A survey found heavier enterprise AI use correlates with more security incidents, pointing to a need for access controls and governance before deploying AI in critical workflows.

The article traces tech’s rise from cloud in 2016 to today, showing software firms now rival entire economies in market cap. It then draws parallels to 19th-century railroads, explores AI’s potential to reshape corporate hierarchies, notes stablecoins shifting toward payments, and examines plunging trust in mass media among younger generations.

• The ten largest public companies by market cap now exceed the combined GDP of the G7 (excluding the US), achieved within roughly a decade of cloud computing's rise.
• Railroads once commanded up to 63% market share and forced the invention of modern corporate hierarchy; some argue AI could similarly flatten management structures today.
• Stripping out speculative and treasury flows, stablecoins generated $350–550 billion in genuine payment transactions last year, with consumer usage growing fast.
• Trust in mass media has collapsed from 72% in 1975 to 28% in 2025, with young Americans increasingly relying on social platforms instead.

The article explores the definition of an engineer and what engineering truly entails, especially in the context of advancing AI technology. It emphasizes that engineering is about taking the right actions in the right sequence to achieve various intentions, highlighting the importance of clarity in project goals and the art of sequencing tasks.

• An engineer is defined as someone who takes the right actions in the right sequence to achieve a set of intentions, not someone who builds physical things—making software engineers legitimately engineers.
• Engineering projects have multiple simultaneous intentions (resources, stakeholders, users), and failing to articulate them causes teams to build the wrong product or misalign with goals.
• Sequencing matters as much as choosing the right actions—order of operations determines success, and sequences nest recursively within larger sequences (illustrated via the hand-washing example).

Different software markets will experience distinct impacts from AI coding, as constraints vary across sectors. While some areas, like personal software, may see explosive growth due to lowered skill barriers, others, such as enterprise products, will face competitive pressures without significant market expansion. Understanding these nuances is essential for predicting the future of software development.

• Software isn't one market—at least eight distinct segments exist, each with different constraints, so AI won't affect them uniformly
• Enterprise internal tools have huge pent-up demand and high developer-time costs as the bottleneck, so AI unlocks real market growth by clearing a backlog
• Enterprise SaaS has a fixed competitive market size, so AI-driven faster iteration just intensifies feature-race competition rather than expanding the market, often at the cost of quality
• Personal/hobbyist software is being democratized like photography once was, letting non-programmers build one-off tools where functionality matters more than durability or quality

In a podcast discussion, predictions for the tech industry in 2026 are shared, highlighting the undeniable improvement of LLMs in writing code, advancements in coding agent security, and the potential obsolescence of manual coding. Other predictions include a successful breeding season for Kākāpō parrots and the implications of AI-assisted programming on software engineering careers.

• LLMs will keep getting undeniably better at writing code through 2026, shifting more programming work to coding agents
• Security around coding agents will become a major focus as they gain more autonomy and access to systems
• Manual hand-coding may start becoming obsolete for significant portions of software engineering work, reshaping the profession
• Kākāpō parrots are predicted to have a successful breeding season in 2026

Claude Opus 4.5 is launched as a cutting-edge AI model designed for coding, research, and office tasks. It boasts significant improvements in efficiency, reasoning, and task management, making it accessible for developers and enterprises at a competitive price. The model excels at complex workflows, demonstrating advancements in self-improving abilities and safety measures.

• Claude Opus 4.5 is priced more competitively than previous Opus models, lowering the barrier for developers and enterprises to adopt it
• The model shows notable gains in coding, research, and office/agentic task performance compared to earlier Claude versions
• It demonstrates improved efficiency and reasoning on complex, multi-step workflows
• Anthropic highlights advances in self-improving capabilities alongside continued safety measures