Tag: enterpriseai
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Reducing token usage
Better Defaults (not Usage Caps) — Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage…
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“this is the voice of American business that is being channeled through me”
“Livid” was the word he used to describe enterprise customers who are “paying for tokens that create no value,” while handing over their data to the major firms and, in the end, taxing the public who will end up paying for it in some form. The reason, he ventured, was the “models have completely… irresponsibly…
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Elusive Enterprise AI ROI: bring in the FDEs
Enterprise AI needs a lot of custom fitting and one off integration, so you need a lot of post-sales engineering. On the one hand, this is lock-in in the making, but lock-in to your own, stack. You build a stack that is unique to you. On the other hand, it means you get exactly what…
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How AI change your software development organization
A catch-up with Purnima Padmanabhan, GM of the Tanzu Division at Broadcom, on what her team has actually learned shipping enterprise software with AI for the last year and a half: code generation is the small part, beautiful code is the new uncanny valley, and you cannot solve the agent boundary problem from inside the…
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Enterprise AI on private cloud
Last year, 56 percent of enterprises used public cloud as the primary environment for production AI inference. This year, that figure has fallen 15 percentage points to 41 percent, while 56 percent of enterprises are now running or planning to run production inferencing in a private cloud. 🔗 The AI tipping point: where enterprise AI…
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“PE 2.0”
We are seeing a distinct movement where infrastructure is being reclaimed as a first-class platform concern. In the early days, platform engineering focused heavily on developer experience, often treating the underlying infrastructure as a utility that was managed separately. That is no longer sufficient. To succeed in the AI era, platform teams must have deep…
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“a staggering 3,611 active or planned use cases for AI across the federal government”
The office of management and budget (OMB) disclosed a staggering 3,611 active or planned use cases for AI across the federal government. The list has balloonedby 70% from the one published in the final year of the Biden administration, and includes many disturbing-seeming plans to hand over sensitive governmental functions to AI. Yes, but: While…
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When management is the bottleneck preventing enterprise AI ROI.
Right now, many companies already have the technology they need to go much faster. The blocker is company systems that are mostly designed to prevent things from happening. The power is centralized and all the team members are treated like a risk vector. Exhausting approval cycles, super tight boundaries on roles, unbreakable title-based hierarchies, and…
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Enterprise harness smack-talk
One enterprise harness maker says the competing enterprises harness makers either suck or are non-existent. When you go to San Francisco and talk to them, their basic vibe is ‘we don’t have to solve your problem today because tomorrow you’re going to go away and all your problems are going to be solved,’” Karp charged.…
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That means each employee’s AI spending cap is ~11% of that median compensation package. Last year, there were a few anecdotes about high growth tech companies spending $100,000/year per head on tokens. That seems like it’s coming to end.a 🔗 Uber Caps Usage of AI Tools Like Claude Code to Manage Costs
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🤖 Valiantys-Glean Partnership Bets That Cross-Platform Knowledge Graphs and Behavioral KPIs Are What Move Enterprise AI Past Pilots
Original: Enterprise AI is still stuck at experimentation – Valiantys and Glean think they know why by diginomica. Summarized by AI on June 3, 2026. Most enterprise AI pilots stall, and the diagnosis from Nathan Chantrenne, Chief AI Officer at Valiantys, is that the field measures the wrong things and fragments its tooling. The dominant…
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50%+ failure is normal
Analyst firm Gartner thinks at least half of all generative AI projects “will overrun their budgeted costs due to poor architectural choices and lack of operational know-how,” and most organizations that try to build custom models “will abandon their efforts due to costs, complexity and technical debt in their deployments.” Yes, and this matches decades…
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Enterprise AI Slop
People are using AI to generate too much work because they think they know what they’re doing: A growing body of work calls this output-competence decoupling. In any previous era, the quality of a piece of work was a more or less reliable signal of the competence of the person who produced it. A novice…
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The users have plenty of feature ideas
AI allows people who aren’t software engineers to build meaningful software. Those of us who are software engineers at companies should stop building features and focus instead on building systems that allow people on the sales team, the factory shop floor, etc. etc. etc. to ship safely. 🔗 notes from o11ycon 2026
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Buy your platform, AI edition
Building an internal agentic AI platform in banking or insurance demands a multi-year orchestration engineering commitment with a regulatory surface area that most organizations underestimate. [Bryan Ross] Tinkers and opexmaxxers take in huge risks when they decide to build their own platforms. And it usually fails, for at least seven reasons. 🔗 The hidden cost…
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Spaghetti DBMS, MCP for Knowledge Workers, The Harness Is the Harness – Related to your interests, Wednesday
Also: 55% of jobs reshaped, power over GPUs, a thousand DIY weekend platforms, and mood-tracking the robot MCP Servers for, and of, knowledge workers This is an excerpt from last week’s Tanzu Catsup. David and I have been talking a lot about the AI harness. We of course sprinkle in talk about how you would…
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Bad advice from Wall Street on enterprise AI.
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Where are the enterprise AI apps? Part n + 1
Outside of programming, there’s still a dearth of enterprise AI apps, it seems. Palo Alto’s CEO: “Consumers are far outstripping enterprise for the moment, but we expect enterprise will surely and slowly get on that bandwagon,” he said on the company’s Q2 earnings call. … “Right now … tell me how many enterprise AI apps…
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