Weekly Signal · Reporting period: September 29–October 4, 2026
Published October 19, 2026 (Pacific).
What should you delegate to AI—and what should stay behind a human decision? Three developments from September 29–October 4 sharpened that question: a small-business agent, a cheaper model for demanding work, and a research preview of more lifelike video interaction.
This look back uses the capabilities, prices, and access limits described at announcement. The practical theme is simple: expand the test before you expand the permissions.
1. Meta brings Muse into small-business workflows
What happened
Meta announced Muse for Small Business on September 29, extending its personal agent toward business tasks and connected tools. The launch positioned Muse to help with work such as managing social pages and finding customers, with connections across Facebook, Instagram, Meta ad accounts, and business tools including Canva and Shopify. (source)
Why it matters
For a small marketing team, a connected agent is most interesting at the handoffs: gathering campaign context, identifying unfinished work, and preparing the next draft. That is a more useful evaluation target than asking it to “grow the business.”
GrowthGary’s take
A connection is access, not evidence of competence. Before adding customer records or an ad account, decide what the agent may read, what it may prepare, and what needs approval. Our guide to how AI agents work is a useful foundation for that conversation.
What to do next
Start with a sanitized weekly campaign summary. Ask for three priorities, the supporting evidence, and draft actions—not execution. Check the reasoning and missing context before considering broader access. Keep sending, publishing, and spending behind explicit approval.
2. GPT-6.1 Sol changes the cost question for repeatable work
What happened
At its September 29 DevDay, OpenAI introduced GPT-6.1 Sol, positioning it as approaching GPT-6 Astra on agentic coding, computer use, and professional work at one-fifth of Astra’s standard input and output token prices. Its launch API prices were $2 per million input tokens and $10 per million output tokens. The performance comparison is OpenAI’s claim; the price ratio is not a guarantee that every finished task costs 80% less. (source)
Why it matters
For repeated research summaries, content repurposing, or reporting drafts, lower model prices could create room for more verification. But longer answers, retries, and human cleanup can erase the apparent saving.
GrowthGary’s take
Buy a reliable workflow, not a price-per-token headline. The question in our guide to which AI tools are worth paying for still applies: what verified work do you get back for the total time and money spent?
What to do next
Choose one low-risk, repeatable task and a fixed acceptance checklist. Compare completed outputs against your current model using the same inputs. Include retries, tool costs, and reviewer minutes in the comparison. Keep the stronger option for cases where the cheaper one fails.
3. Tavus Griffin makes disclosure part of the experience
What happened
On October 1, Tavus introduced Griffin, a model designed for real-time face-to-face video interaction. Griffin-Lite was a research preview for selected trusted testers, not a generally available customer product. Tavus reported that 26 of 54 participants believed their partner was human after a one-minute call in its study. That narrow, company-reported result should not be read as proof that AI is indistinguishable from people in every setting. (source)
Why it matters
The marketing implication is not to rush an avatar onto a sales page. It is to think through what a more natural AI conversation would ask of your brand: clear identity, accurate answers, sensible boundaries, and an easy route to a person.
GrowthGary’s take
Looking human is not the same as earning trust. A useful evaluation should reward understanding and helpfulness, not confusion about who is speaking. Apply the same separation of claims, evidence, and interpretation described in our editorial and review methodology.
What to do next
Prepare a future evaluation script with explicit AI disclosure, a human-handoff request, an interruption, and a question outside the approved knowledge base. Define what should happen in each case. Do not plan a Griffin rollout around research-preview access or assume that a polished demo establishes production readiness.
The takeaway: Connected agents expand the scope of delegation. Lower model prices change its economics. More natural interfaces raise the stakes for trust. For your next experiment, measure the finished result and keep the approval boundary visible.