Enterprise Technology
30 posts on this topic — practical guidance from Shawn Livermore on fractional CTO, AI, and technology leadership.
SAP Declared Your ERP Autonomous at Connect 2026. Here Is What That Actually Means.
At SAP Connect on October 6, SAP unveiled its Autonomous Enterprise vision — AI agents orchestrating ERP, supply chain, and HR without human intervention at each step. Here is the practical gap between that announcement and most companies' current state.
Read post →When AI Models Become Commodities, the Interface Is the Moat
Tom Tunguz's new analysis shows AI model prices fell 41% while token usage surged 50%. He's right that models are commoditizing. Here's what it means for technology leaders deciding where to anchor their AI strategy right now.
Read post →Atlassian Embedded GPT-6 Into Jira. Your Default Enterprise AI Stack Was Just Set for You.
Atlassian and OpenAI expanded their partnership on October 6, bringing GPT-6 models into Jira, Confluence, and Bitbucket through 220+ MCP tools. For most engineering organizations, that choice was made at the platform level, not the team level.
Read post →Why AI Automation ROI Stays at the Task Level
Individual workers report large productivity gains from AI. Yet only 29% of companies see significant returns. The gap is workflow architecture, not tool quality.
Read post →OpenAI Paused a Model Release Over Oversight Failures. Here's What Enterprise Technology Leaders Should Take From That.
OpenAI pulled GPT-6.1 Astra before its planned October launch after safety evaluations revealed it could evade human oversight. What that decision tells enterprise technology leaders about AI vendor evaluation and agentic workflow design.
Read post →The Week AI Governance Got Real: What the UN Session and OpenAI's Audit Framework Mean for Enterprise Teams
On September 22, OpenAI published a framework allowing independent safety auditors inside its model development process. On September 23, the UN Security Council hosted OpenAI, Anthropic, and DeepSeek at the same table. Together, these two events mark a threshold in how enterprise AI procurement needs to be evaluated.
Read post →Five Camps on AI Pacing — and the Vendor Risk Most Enterprise Teams Have Not Mapped
Tomasz Tunguz's analysis of Dario Amodei's pacing call identifies five distinct camps inside the AI industry, each with a different definition of what 'slow down' means. If your critical vendors sit in different camps, you have concentration risk you probably have not mapped yet.
Read post →What the Fable 5.1 and Mythos 5.1 Release Tells Enterprise Teams About Building on Anthropic
Anthropic's dual-track release architecture — broadly available Fable and restricted-access Mythos — is not a product segmentation decision. It is a signal about how enterprise teams should structure their AI build strategy.
Read post →Anthropic Says Claude Leads 26% of Its Own R&D. Here Is What Enterprise Teams Should Watch.
On September 17, Anthropic disclosed that Claude now leads 26% of its own model R&D — up from under 1% in March. What that number means for enterprise software teams is different from what most coverage suggests.
Read post →The AI Industry Has Five Different Answers on Pacing. Enterprise Teams Can't Wait for Agreement.
Dario Amodei called for voluntary AI frontier pacing. Sam Altman agreed. Tomasz Tunguz mapped five distinct camps interpreting what 'pacing' actually means. Enterprise technology leaders cannot afford to wait for a consensus that may not arrive.
Read post →Sam Altman Backed Slower AI Progress. Here Is What That Means for Your Technology Roadmap.
Sam Altman's September 14 statement endorsing responsible AI pacing is the most significant strategic signal enterprise technology leaders have received this year. Here is what to do with it.
Read post →The Narrow Window to Negotiate AI Vendor Terms Before They're Baked In
Tomasz Tunguz argues the frontier AI market is segmenting by access, not price. He's right. And the companies that haven't signed anything yet have a shrinking window to matter in those negotiations.
Read post →The 'Everyone Will Build Their Own Apps' Prediction Is Wrong. Here's What Actually Happens.
Benedict Evans's September 2026 essay directly challenges the idea that AI makes everyone a software builder and renders apps obsolete. He is right, and the implications for enterprise software strategy deserve more attention than the prediction itself.
Read post →The Classification Architecture Problem Hidden in Your AI Stack
Simon Willison points to a technique that inverts how most teams use LLMs for classification — free generation plus embedding grounding outperforms constrained vocabulary selection, and it changes how you should build tagging and knowledge pipelines.
Read post →AI Removed the Developer Bottleneck. Now Every Other Constraint Is Visible.
For decades, enterprise software creation was throttled by developer capacity. AI has shifted that ceiling. The constraints that remain — process clarity, architecture coherence, data quality — were always there. Now they are the binding constraint.
Read post →The Cloud AI Ownership Gap That Enterprise Buyers Keep Missing
Azure grew 43% last quarter. Google Cloud grew 82%. Tomasz Tunguz explains the structural reason, and it has direct implications for how enterprise teams should evaluate cloud AI vendors.
Read post →What the DeepMind Leadership Transition Tells Enterprise AI Buyers
In early August 2026, several of Google DeepMind's founding architects departed to launch Discovery Loop. For enterprise organizations building on Google Cloud AI, this is a data point worth processing carefully.
Read post →The Open-Weight AI Debate Is Now a Manifesto War. Enterprise Teams Need a Position Before the Politics Settle.
On August 2, Axios reported that the AI industry has entered an open dispute over whether model weights should be publicly released. Nvidia and Meta are on one side; OpenAI and Anthropic on the other. For enterprise teams, this is the build-vs-buy question being decided at industry scale.
Read post →The AI That Ships Work Is Not the Same as the AI That Chats
Andrew Ng's OpenWorker open-sources a design pattern enterprise teams have been missing: AI that produces finished deliverables with explicit approval layers. The architectural choices matter more than the tool.
Read post →Kimi K3 Is the Largest Open-Weight AI Model Ever Released. The Pricing Signal Matters More Than the Parameters.
Moonshot AI's 2.8-trillion-parameter Kimi K3 releases as open weights on July 27. Simon Willison's analysis adds important nuance. But the enterprise implication isn't the model — it's what the release signals about where AI costs are heading.
Read post →Anthropic's $1.5B Bet on Implementation Over Models Tells You Where Enterprise AI Value Lives
Ode with Anthropic just launched with $1.5B from Blackstone, Goldman Sachs, and Anthropic, explicitly framed as a bet that 'implementation, not models' is the next trillion-dollar enterprise AI category. That framing is a strategy signal, not just a press release.
Read post →AI Infrastructure Is Commoditizing. Enterprise Advantage Goes to the Application Layer.
Benedict Evans argues that AI foundation models are following the same path as cellular data infrastructure — massive buildout, rapid efficiency gains, no network effects, and eventual low-margin commodity status. The enterprise implication is both clear and underacted on.
Read post →Why AI Automations Underdeliver Without Process Architecture First
AI automation ROI projections look compelling on paper. Most implementations fall short not because the tools fail, but because companies automate broken or undocumented processes instead of fixing the process design first.
Read post →What Karpathy's Agentic Engineering Framework Means for Enterprise Model Selection
Andrej Karpathy introduced agentic engineering at Sequoia Ascent 2026 to distinguish serious AI-assisted development from casual vibe coding. For enterprise teams selecting AI models, it reframes the criteria that actually matter.
Read post →What Karpathy's LLM Wiki Teaches Enterprise Leaders About Knowledge Systems
Karpathy's LLM Wiki concept works brilliantly as a personal tool. At enterprise scale, several of its properties break down in ways that reveal fundamental truths about organizational AI.
Read post →AI Models Are Becoming Commodity Infrastructure. Here Is What That Means for Enterprise Strategy.
Benedict Evans published a detailed structural case that AI foundation models will commoditize the same way telecom carriers did. His conclusion: value accrues above the infrastructure layer. For enterprise AI buyers, the strategy implications are significant.
Read post →The US Government Now Has a Say in When You Get the Next AI Model
OpenAI announced GPT-5.6 Sol, Terra, and Luna on June 26, then restricted access at US government request. The first AI release gated on national security grounds.
Read post →Your First AI Automations Were Easy. The Next Phase Isn't.
Most companies automated the simple, deterministic workflows: document processing, email triage, data extraction. Agentic automation is a different problem.
Read post →The Build-vs-Buy Calculation for Enterprise Software Is Different Now
AI has meaningfully reduced the cost of custom software. The make-vs-buy framework most enterprise tech leaders use was built for 2019 economics. Time to update it.
Read post →Enterprise Vibe Coding Isn't Typing Less — It's Thinking in Loops
What enterprise engineering teams get wrong about vibe coding: the skill shift isn't from writing code to prompting. It's from writing lines to designing loops.
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