AI is becoming the place where work starts. That changes what we need from assistants, how we judge their cost, and what we expect from the software they use.
In last week’s Alephic conversation, James Gross, Noah Brier, and Sam Knight discuss their first experiences with OpenAI Dots, the tradeoffs between models, and the friction that appears when an AI assistant needs access to another app. The conversation closes with a question that matters well beyond the technology: how do we help people see what AI can do for them?
What stood out
Assistants need to own their tasks. As AI takes on work in the background, we need a clear view of what it is doing, what is finished, and where it needs our help. Noah’s experiment with a custom task manager makes that problem concrete.
Cost per completed task is a useful measure. A higher subscription bill can coexist with cheaper work. Noah shares a rough estimate that Alephic’s cost per completed development ticket has fallen as the team combines different models.
Access is becoming part of the product. The discussion around Figma’s MCP access leads to a larger question: can customers use the software they pay for through the AI interface they prefer? James connects this to building software around a company’s own ways of working.
Trust starts with useful experiences. The group discusses the gap between daily AI use and public perceptions, and why helping someone save money or finish a real task may be a better introduction than another sweeping prediction.
Jump to a chapter
00:00 · Welcome back, James
James returns from Munich and the group sets up the week’s conversation.
00:46 · Putting OpenAI Dots to work
Noah describes testing an always-on assistant on everyday tasks, including gathering insurance quotes.
02:44 · Who owns the AI’s tasks?
A custom task manager separates the assistant’s responsibilities from the work that needs human attention.
07:06 · AI hardware beyond the laptop
James considers how new devices could change the way people interact with assistants. The hardware discussion is speculative.
10:57 · One assistant or many apps?
The group weighs a single conversation against multiple threads and tools, including the difficulty of tracking background work.
13:49 · Sol, Opus and model choices
Noah explains how he assigns different kinds of work to different models based on capability, efficiency, and price.
15:51 · The price of faster AI
Pricing changes prompt a discussion of compute, willingness to pay, and the value of better creative output.
19:37 · Cost per completed task
Noah describes Alephic’s software workflow and why he is watching cost per ticket alongside total spend.
22:22 · The Figma access dispute
The hosts discuss restrictions on AI integrations and what customers should expect from the tools they buy.
25:45 · AI becomes the workspace
James lays out the idea of AI as the main interface to work, with access to existing systems and software built for specific needs.
32:55 · How companies adopt AI
Adoption varies across companies. The group considers how consumer experience could help people bring new habits into work.
36:14 · A working day with AI
James walks through automated morning briefs, research, presentations, and email and Slack triage.
38:57 · SNL and AI in popular culture
A Saturday Night Live sketch opens a discussion of how AI is being understood outside the technology industry.
40:18 · Why AI has a trust problem
The group examines public skepticism, industry messaging, and the distance between headlines and hands-on experience.
47:30 · Responsibility and useful AI
Noah argues for accountability and practical consumer benefits, including helping people save money.
51:20 · Helping people try AI
The conversation turns to encouraging experimentation, preserving skills, and helping colleagues gain time and autonomy.
55:25 · BRXND NYC and closing
James previews the upcoming BRXND gathering and the group wraps up.
For more on the idea of AI as the interface to work, read James’s The killer AI app might only matter to you.
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