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Know what your customers actually want out of AI

Over half of Americans have used AI to manage their finances. And they're not going back.

The State of Intelligent Finance, Plaid's latest report in partnership with the Harris Poll, surveyed 2,000+ American adults on how AI is changing what people expect from financial products. Consumers want products that understand their situation, guide their decisions, and act on their behalf. Trust hinges on transparency and accountability.

The report covers where consumers are most ready to let AI take action, what builds trust and what breaks it, and how teams can turn rising expectations into a competitive advantage.

Beginner’s Guide to Grok Build

What’s Happening AI Today

1. China restricts overseas travel for AI researchers at Alibaba and DeepSeek: The Chinese government has begun imposing travel restrictions on individuals working in advanced AI — including researchers at Alibaba and DeepSeek — blocking them from travelling abroad without approval. The move signals Beijing is treating frontier AI talent as a strategic national asset, in the same category as nuclear or defence expertise. For Western AI labs, the policy effectively closes off one of the few remaining channels for cross-border AI talent exchange with China's most capable research teams.

2. AI-generated lawsuits are flooding US courts — and judges are struggling to cope: A surge in AI-assisted "pro se" filings — lawsuits submitted by self-represented litigants using AI tools to draft legal documents — is overwhelming court systems across the US. While the development is being framed as democratising access to the legal system, judges are reporting a wave of technically formatted but legally flawed filings that consume court resources without merit. Several jurisdictions are now considering mandatory AI disclosure requirements for legal documents.

3. Claude Code rate limits doubled — quietly, with no announcement: Anthropic doubled the rate limits on Claude Code for all paid tiers this week without a formal announcement — the change was spotted by developers monitoring their usage dashboards. The timing aligns with Anthropic's $30B funding close and its first-ever operating profit, suggesting the company is now comfortable absorbing the infrastructure cost of higher usage as a growth investment rather than a margin risk. For teams that had been hitting limits, this is immediately actionable.

The Mac Pro case aged better than the most computers 

Size any market before you bet on it

Prompt: You are a market research analyst who has sized markets for venture-backed startups, Fortune 500 strategy teams, and solo founders making their first real bet. You know that most market sizing is either too optimistic or too vague to be useful. Your job is to give me a number I can defend — not one that makes the slide look good.

I am building [describe your product or business in one sentence]. My target customer is [describe specifically: who they are, what they do, how many of them exist]. The problem I solve is [describe the specific pain, not the category].

Size this market for me across three layers — and be honest about the assumptions behind each number:

1. TAM — Total Addressable Market — If every possible buyer of this solution bought it at full price, what is the theoretical ceiling? Use a bottom-up calculation: number of potential customers × realistic average contract value. Show your working. Flag where the numbers are solid and where you are estimating.

2. SAM — Serviceable Addressable Market — Now apply real constraints. Which subset of that TAM can I actually reach given my geography, language, distribution channel, and product stage today? What percentage of the TAM falls away and why?

3. SOM — Serviceable Obtainable Market — What is a realistic three-year capture target, given my team size, budget, and competitive landscape? This is the number I should actually build toward. Not aspirational — achievable with a strong but realistic execution.

Then tell me: what is the single biggest assumption that, if wrong, would collapse this market sizing entirely? And what would I need to see in the first six months of operating to know whether that assumption is holding?

Do not pad the numbers. A smaller honest market is more useful to me than a large number I cannot defend.

New AI Tools to Boost Your Productivity:

• ModelHub — Menu bar app for discovering and launching local LLMs on Mac
• Cleo — Keeps team standups, summaries, and follow-through moving
• Fere AI — AI agents that turn signals into crypto and Polymarket trades
• Kanwas — Open-source brain for your team
• Vivago Video Agent — Produce consistently compelling videos without prompting
• Emdash — Parallel agents with issue-driven multi-repo control
• Agentspan — Open-source runtime for durable AI agents
• General Compute — Ultra-fast OpenAI-compatible inference cloud

That’s a Wrap

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