Issue 14

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Today’s Brief

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AI News

Texas HB 1709: The Standard for AI Hiring Compliance

The Brief: Texas HB 1709, set to potentially take effect in September 2025, introduces comprehensive regulations for high-risk AI systems used in employment decisions.

The bill establishes strict requirements for transparency, bias mitigation, and accountability, particularly focusing on AI systems that influence critical decisions like hiring and employment background checks.

The details:

• High-risk AI systems are those significantly influencing consequential decisions affecting access to employment, healthcare, or financial services

• Annual reviews will be required, with updates due within 90 days of significant system modifications.

• Non-compliance fines can reach $200,000 per violation for discriminatory outcomes or failing to meet standards.

• Employers will need to establish regular compliance reviews and maintain documentation of AI system assessments.

Why I think it matters: Texas HB 1709 is a chess move. Imagine deploying an AI hiring tool that seems fair… until it quietly filters out neurodiverse candidates or penalizes veterans for resume gaps.

We’ve seen this play out before—remember Amazon’s infamous 2018 recruiting AI that downgraded women’s resumes?

HB 1709 turns those hypothetical risks into $200,000 per violation realities Amazon AI Recruiting Tool Bias (2018)

But here’s what most miss: compliance is the floor, not the ceiling. Pursue fairness, accountability, and transparency to win.

The OECD’s 2023 AI Principles already warned that “trustworthy AI” requires provable fairness, not just good intentions OECD AI Principles (2023).

Texas is making that mandate actionable—and it’s only the beginning.

This will be the new gold standard for ethical AI.

Forward-thinking leaders will treat HB 1709 like bourbon—sip it slow, savor the complexity.

Here’s how to play this smart:

1. Audit your AI like a hostile takeover—document your AI’s decision logic and hunt bias like it’s undervalued stock.

2. Turn compliance docs into marketing collateral—publish your impact assessments like Tesla posts safety ratings. When 72% of Gen Z candidates distrust companies using opaque AI (Deloitte 2024 Gen Z Survey), transparency can be a talent magnet.

3. Bake HB 1709’s DNA into your AI roadmap—future-proof for the global regulatory storm.

Bottom line: You can grumble about the rules—or lean into them early as an advantage. I’m betting on the latter.

Synthesia’s $2.1B Milestone: Redefining Corporate Training

The Brief: London-based AI startup Synthesia has secured $180 million in new funding, reaching a $2.1 billion valuation. The company, which creates realistic AI-powered video avatars, is positioning itself as a key player in corporate communications and training content development.

The details:

• Over half of the Fortune 100 use Synthesia’s AI video avatars for corporate communications.

• Last year’s £25.7 million turnover came largely from outside Europe, and the company now employs 400 people across three continents.

• The platform creates presenter-led videos, can translate into multiple languages, and aims for more lifelike avatars.

Why I think it matters: This is far from investor hype and AI avatars aren’t a gimmick—this is a paradigm shift in how corporate communication is developed.

Synthesia’s $2.1B valuation is a bet that scalable video content creation will define the next era of work, specifically training and development.

If one thing is clear: the days of “PowerPoint parade” and $50k training videos are over – not to mention production delays and missed deadlines.

This is more about amplifying reach than replacing humans.

Synthesia’s avatars act as force multipliers: a single subject-matter expert can now train 10,000 employees across 30 languages. For global teams, this will be efficiency to the maximum degree.

But we need be really careful. The real risk isn’t adoption—it’s misuse. organizational leaders will need to ask:

• Are we using avatars to enhance human expertise, or as a Band-Aid for underinvesting in talent?

• Do our AI-generated videos reflect our culture authentically, or are they sterile corporate puppetry?

• How do we guard against “AI fatigue” when employees crave human mentorship?

There are many more but hopefully you get the idea. The companies that win will treat AI avatars like elite Olympic athletes: rigorously trained on proprietary data, iterated with employee feedback, and deployed where they add unique value—not everywhere.

Bottom line: Executives who master the balance between AI scale and human nuance will not only shed cost, they’ll also redefine what it means to lead a connected workforce. So much is at play here and I’m excited to see how it shapes out.

3 Steps to Accurate and Trustworthy Enterprise AI

The Brief: An article published from the World Economic Forum Annual Meeting outlines three critical steps for implementing accurate and trustworthy enterprise AI in 2025. This includes insights from an Economist Impact survey of 1,100 technical executives. The provided guidance is to (1) Move beyond a single model, (2) Prioritize data governance, and (3) Plan for scale.

The details:

• While 73% of businesses see GenAI as crucial, only 37% believe their projects are ready, showing a significant production-ready gap

• Moving beyond single models to leverage proprietary data is essential for accuracy and resilience and controlling access to sensitive information is critical for compliance and maintaining stakeholder trust.

• Grainger built a smart AI tool to help customer service teams quickly find accurate information about their 2.5 million products using simple language. This speeds up support, keeps information up-to-date, and improves customer satisfaction, which is crucial for a company handling so many products and changes every day.

Why I think it matters: The gap between companies trying out GenAI and those with AI projects that actually work isn’t just a gap—it’s a massive divide. And this divide isn’t about technology; it’s about strategy.

Companies stuck on single-model AI are like chefs using only salt: it works, but it’s basic, boring, and breaks under pressure. The Economist Impact study makes it clear—hybrid models (mixing open-source and proprietary AI) powered by your data are what set you apart.

Mastercard has an AI onboarding assistant that automates repetitive tasks, cuts out manual work, and gets customers using payment solutions faster. This is the standard for effective use of AI technology at this stage of the game.

Companies relying on single-model AI are stuck explaining why their chatbot made up a fake payroll policy.

While execs love flashy “AI strategy” presentations, many skip the hard part of continuous testing and improvement. The best companies won’t wait for yearly check-ins—they’ll review their AI’s performance weekly, making readiness a habit.

Bottom Line: This isn’t just about AI—it’s about how fast and smart your company can adapt. Nail hybrid models, build strong governance, and commit to constant improvement and you’ll innovate faster, earn more trust, and stay ahead. Everyone else? They’ll be the cautionary tales.