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SHORT ⚡ CIRCUIT
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KEEPING CURRENT
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Vol. 1 · Issue 3 · April 21, 2026
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What AI Has Earned (and Hasn't Yet)
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After a year of using these tools heavily, here's the honest read.
AI has earned a place in four kinds of work: drafting first versions of almost anything, synthesizing research across many sources at once, generating code for known patterns, and translating between formats — data into prose, prose into outline, code into explanation, English into a dozen other languages.
It hasn't earned a place — yet — in four others: holding context across a long, complex project; knowing when it's wrong; producing genuinely novel ideas instead of remixed familiar ones; and making judgment calls in ambiguous situations where there's no clean right answer.
The boundary between those two columns is where to draw the line. AI drives the first set. You drive the second. The discipline is knowing which task is which — and not letting one creep into the other.
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Try This:
Should You Use AI for This? A 30-Second Decision
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Three questions, in order. The answer determines how much rope to give the AI.
1. Could a junior employee with full context do this in an hour?
If yes: AI can probably handle it. Drafting an email, summarizing a meeting, creating a simple report, formatting data. Hand it over.
2. Could you Google the answer to verify it?
If yes: AI drafts, you fact-check. Research summaries, technical explanations, comparisons across products. AI saves you the search; you confirm what matters.
3. Does the wrong answer cost something irreversible?
If yes: AI assists, you own. Strategic decisions, customer-facing messaging, anything regulated or compliance-critical. Use AI to think through options, but the final call is yours and the accountability stays with you.
Most non-strategic, non-compliance work lives in the first two questions. Start there. The third is where AI is helpful but not in charge — that's the line worth getting comfortable with.
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This Week in AI
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Anthropic Releases Claude Opus 4.7
On April 16, Anthropic shipped Opus 4.7, its new flagship model aimed at complex reasoning and long-running agent workflows. The headline number: 87.6% on SWE-bench Verified, a coding benchmark — beating GPT-5.4 on the same test. The bigger signal: AI vendors are now competing on long, multi-step task reliability, not just chatbot quality. Read more →
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OpenAI Ships ChatGPT Images 2.0
On April 21, OpenAI released a major update to ChatGPT's image generation. Better prompt adherence, more consistent style across iterations, sharper text rendering. If the first generation of AI images had a "looks like AI" tell, this update closes that gap considerably — useful if you've been considering AI for marketing assets, internal docs, or product mockups. Read more →
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Adobe Rebrands Experience Cloud as "CX Enterprise"
Adobe rebranded Experience Cloud as CX Enterprise this month — an AI-first platform built around agentic workflows that connect creative, marketing, and customer-experience tools. The thesis: enterprise software is reorganizing around AI agents that operate across functions, not separate apps each handling a slice. Expect this pattern to repeat across enterprise categories over the next 18 months. Read more →
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🎓 CIRCUIT SCHOOL
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AI 101 · Part 3
Building your AI vocabulary from the ground up
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What Is a Hallucination?
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A hallucination is when an AI confidently tells you something that isn't true.
Not a typo, not a guess hedged with "I'm not sure" — a fact-shaped statement, delivered with the same calm certainty as a real answer. The model invents a citation, invents a quote, invents a person's job title, invents an API that doesn't exist. It looks right. It is wrong.
This happens because of how AI models work. They predict what comes next based on patterns in their training data. They don't know what they know. They generate text that statistically fits — and most of the time that's accurate, because real text follows the same patterns. But when there's a gap in the training data, or the prompt is ambiguous, the model fills it with something plausible-sounding rather than admitting uncertainty.
How to spot one: anything specific that you can't verify against an outside source. Names, dates, statistics, citations, technical specs. The fix is the same as for any unreliable narrator — check the load-bearing claims yourself.
Next issue we'll cover how to write prompts that reduce hallucination risk.
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HOW WE MADE THIS
This issue's lead came from a year of working with these tools — what AI handles cleanly versus where it still falls short. Claude helped tighten the framing, pull the benchmark numbers, and structure the Field Guide's three-question flow. The news items were researched by Claude and selected by Rich for relevance to the audience.
See you next Tuesday.
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Sponsored By
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Neo Crucible
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Thanks to Neo Crucible for being our first sponsor. Follow the Short Circuit Project to see what I make with AI and a new Creality 3D printer!
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