Twilio vs. Sinch: The Tussle of Two Reports and Five AI Books for Your Summer
Messaging insights up top. Five books that don’t waste your time
In this week’s edition
Twilio vs. Sinch: The Tussle of Two Reports
The top two players in customer comms just dropped big reports on what’s wrong with customer engagement. Twilio calls it the State of Customer Engagement. Sinch goes with State of Customer Comms. Each one reads like a manifesto. Each claims to know what the customer actually wants. But zoom in, and they’re telling two different stories—about what’s broken and how to fix it.
What they both share is a quiet discomfort: Engagement tools aren’t delivering like they were supposed to. The AI bugle is playing loud and proud, but the marching band’s still missing.
Twilio: Everyone’s a Builder, but the Bridge Is Broken
Twilio opens with a paradox: 82% of businesses say they deeply understand their customers—but only 45% of customers agree with them. It’s a familiar refrain. AI was supposed to close the expectation gap. It hasn’t. The tools got better, but the trust hasn’t followed.
Its answer? Use AI intentionally by building real-time, contextual, and transparent solutions. Solutions that solve actual human problems, tools with purpose, and not just prototypes looking for a reason to exist.
Sinch: Integration Comes First, Engagement Follows
In Sinch’s telling, messaging is complex and fragmented. Disconnected systems, compliance sprawl, and shifting expectations are compounding the problem.
Its fix? Integrate your channels, systems, and intent. Only then does messaging become personal, timely, and trusted.
Sinch’s report breaks into four pillars: Keep customers engaged, informed, safe, and happy. Where Twilio zooms in on AI, Sinch pulls back to the entire system: alerts, reminders, verifications, customer service.
Sinch sees messaging more as a coordination problem than a personalization one.
The Trust Deficit
Despite the framing differences, both are circling the same core problem: Customers don’t believe brands understand them.
Twilio sees it through the AI lens: Personalization feels more invasive than intuitive. Sinch sees it through the infrastructure lens: Even the right message feels wrong if it arrives at the wrong time, in the wrong tone, or through the wrong channel.
This is the engagement gap. It’s not just that we miss the moment; it’s that when we hit it, customers don’t trust us.
Finally
From Twilio, we learn that relevance with control is the new currency. Don’t just know your customers. Let them know you know them. And give them the ability to opt out, modify, or walk away.
From Sinch, we learn that customer experience is a thread of moments. If your tech stack can’t carry the conversation across that thread, trust unravels.
Together, these reports say the same thing: Customer engagement isn’t a marketing problem; it’s a trust problem. And trust is a design-layer decision.
What to Do Next
Beyond reading the reports yourself, here is what I’d do next:
Start with one customer, one thread. Follow a real customer’s journey across promo, support, and security. If it feels stitched together, it probably is.
Replace “engagement” with “confidence.” Every message should ask: Does this build trust or spend it?
Make fewer assumptions. Present smart options. Ask your customers what they want, how often, and on what terms. Then let them change their minds.
Five AI Reads for Your Summer
If you’re looking for a few AI books to add to your summer reading list, here’s what I recommend—and what I’m reading right now.
What I Just Finished
The Coming Wave—Mustafa Suleyman
Worth reading. Strong framing around containment and acceleration. But I was left wanting more from the DeepMind section. For a book by its cofounder, there’s surprisingly little detail on how it worked—or didn’t.
But Read These First
If you haven’t read Tegmark, Fei-Fei Li, or Kai-Fu Lee—start there. These three gave me the footing to make sense of everything else.
Life 3.0—Max Tegmark
He says you can skip the first chapter. Don’t. It’s unsettling in the best way—and sets up the rest of the book with clarity and weight. From my review:
Using a fictional scenario that’s too real for comfort, he shares why the fears of AI achieving consciousness are overblown and do a huge disservice to the real issues that need to be addressed.
The Worlds I See—Fei-Fei Li
A remarkable memoir from the godmother of AI. You’ll notice right away how distinct her voice is from her research writing. From my review:
This book is about entrepreneurship at the edge of the wave and the emotional and intellectual fortitude it takes when you’re the only one working in the lab for years on end.
AI Superpowers—Kai-Fu Lee
When DeepSeek made headlines earlier this year, Kai-Fu wasn’t surprised. Thanks to this book, neither was I. A clear, pragmatic look at China’s AI ambitions—and why they shouldn’t be underestimated. From my review:
Kai-Fu says that “unencumbered by lofty mission statements,” Chinese startups have no problem following trends in user activity wherever it takes them. Layer on that the latest AI improvements have made it very easy to think and build big.
What I’m Reading Now
Artificial Intelligence—Melanie Mitchell
It starts slow—more textbook than narrative—but picks up once the author starts challenging Kurzweil. That part’s worth the wait.
Not About AI But Helps You Understand It
The Scientist in the Crib—Alison Gopnik, Andrew Meltzoff, Patricia Kuhl
Not an AI book, but it reframes how I think about learning systems. And it’s a good check against the intellectually smug Valley speak of “We’ve figured out how the brain works.” From my review:
If algorithms constantly need data to fine-tune their models, babies do the same—always building new neural connections and pruning old ones. In fact, by age two, a child’s brain is the largest it will ever be as a percentage of body mass. To understand how neural networks work, it’s essential to first understand how humans learn. They say our days begin and end as stories, and this book reveals how our brains create those stories.
Finally
There’s a lot of noise in the AI book space right now. From faux trendspotting to inflated certainty. These five books cut through that. They either gave me language I still use or challenged assumptions I didn’t realize I had.
If you’ve already read these, I’d love to hear what stuck with you—or what you think should be next.
Thank you for reading, and have a great week!
TJ