Mon · 13 Jul 2026·Issue 032
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Back to this week
Week ofMarch 30 / 202605 stories / 3 bonus / ~28 min total

The Reading List.

Contents01State of AI trust in...02Powering Product Discovery in ChatGPT03Lyria 3 Pro: Create longer...04Protecting People from Harmful Manipulation05A people-first vision for the...
01
/ LEADRead Sunday
Bucketbusiness
LevelIntermediate
SourceMcKinsey
Read7 min

State of AI trust in 2026: Shifting to the agentic era

McKinsey’s trust survey frames the big shift from “AI that might say the wrong thing” to agentic systems that can do the wrong thing—trigger actions, misuse tools, or exceed intended scope. Useful if you’re trying to scale AI beyond pilots: it lays out where organizations tend to be weakest (governance, risk processes, and ownership), and what “trust maturity” looks like in practice.

Read on McKinsey ->
# trust# governance# agents
02
Read Wednesday
Buckettools
LevelAccessible
SourceOpenAI
Read5 min

Powering Product Discovery in ChatGPT

OpenAI is expanding shopping in ChatGPT into a more structured product-discovery experience: richer browsing, comparisons, and clearer paths from “I’m exploring” to “I’m deciding.” The strategic signal is distribution—AI is competing with search and marketplaces by becoming the first place people go when they don’t yet know what they want.

Read on OpenAI ->
# shopping# search# commerce
03
Read Wednesday
Bucketmodels
LevelAccessible
SourceGoogle (The Keyword)
Read3 min

Lyria 3 Pro: Create longer tracks in more Google products

Google’s Lyria 3 Pro is a music-generation model aimed at creators: longer tracks (up to ~3 minutes), more control over song structure (intros/verses/choruses), and availability across more Google surfaces. The non-technical takeaway is productization: “models” matter most when they show up as usable features inside tools people already use to create.

Read on Google (The Keyword) ->
# music# creative# products
04
Read Friday
Bucketregulation
LevelIntermediate
SourceGoogle DeepMind
Read6 min

Protecting People from Harmful Manipulation

DeepMind summarizes new work on the risk of AI-enabled persuasion and releases measurement tools for evaluating “harmful manipulation” more rigorously. The value for non-technical readers is the shift toward testable safety claims: instead of vague assurances, this is an attempt to define what counts as manipulation and measure it systematically.

Read on Google DeepMind ->
# safety# misinformation# evals
05
Read Sunday
Bucketregulation
LevelAccessible
SourceBrookings Institution
Read7 min

A people-first vision for the future of work in the age of AI

Brookings argues the near-term risk isn’t only job counts—it’s job quality, bargaining power, and who benefits from productivity gains. The actionable part is a concrete “people-first” agenda: build scalable training pathways, strengthen institutions that support workers, and shape deployment so AI augments work rather than silently degrading it.

Read on Brookings Institution ->
# workforce# labor# policy

Bonus material

For the curious.

Optional / 03 reads
MIT Sloan

Where to look for generative AI risks

A clean non-technical framework: “embedded” vs “enacted” risk, plus a practical inventory + ownership approach for reducing exposure.

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