Podcast

Ep 142 – AT&T bets on the last mile (Shawn Hakl)

This week’s guest

Telco in 20 Guest Shawn Hakl AT&T Business View Linkedin profile

Shawn Hakl

Senior Vice President of Product AT&T Business

Every major telco is racing to claim a piece of the AI infrastructure stack. Operators like Telefónica, Orange, Deutsche Telekom, SoftBank, and TELUS are betting on sovereign clouds, AI factories, and GPU-as-a-service to deliver local data, national compute, and a seat at the AI table. 

Then there’s AT&T. In March, it announced AWS Interconnect – last mile: fiber and fixed wireless plugged directly into AWS and engineered for AI workloads. AT&T’s view is that enterprises using AI don’t just need more compute—they need flatter networks and faster connections. So it’s betting on the layer it owns outright—the last mile.

In this episode, I’m joined by Shawn Hakl, SVP of product at AT&T Business. We dig into why the operator is partnering with hyperscalers instead of competing with them, the use case where AT&T deployed AI at the edge to cut latency from 110ms to 40ms, and what an “agent-consumable” network actually looks like.

Listen now to hear:

  • Why AT&T sees hyperscalers as partners, not rivals [05:18];
  • What it takes to put AI into production at telco scale—and lessons learned from AT&T’s internal deployment [07:14];
  • The edge AI deployment that cut latency from 110ms to 40ms for a live customer [08:45]; and
  • What it really means to make a network “agent-consumable” [10:55].

Links and resources

  • Read the AT&T and AWS announcement on AWS Interconnect – last mile, which brings AT&T’s fiber and fixed wireless connectivity directly into AWS environments. 
  • Learn more about AT&T’s edge AI collaboration with Cisco and NVIDIA, which pushes real-time AI inference directly to the network edge.
  • AT&T doesn’t just sell AI solutions; it runs on them. Learn how Ask AT&T has evolved into a full agentic platform, with more than 100,000 users consuming 27 billion tokens a day and teams building custom AI agents with a drag-and-drop tool.
  • Want to scale AI agents without the semantic chaos? Learn how the Totogi Ontology gives AI the business context it needs to discover, act, and execute—so agents stop flying blind.
  • Shawn was on the Canadian national luge team, which means he knows a thing or two about commitment once you’re flying down the track. Elana Meyers Taylor knows that feeling too! At age 41, in her 5th Olympic appearance, she finally won the Olympic gold medal in women’s monobob at Milano Cortina. So, Shawn might be right—stick to the plan!
  • Check out this episode on our YouTube channel.
  • You can find the episode transcript here.

Wanna talk AI and public cloud? Telco execs, set up a meeting with our team to learn how to tap the immense business value it can bring.

Guest bio

As the senior vice president of product at AT&T Business, Shawn Hakl is responsible for the portfolio servicing enterprise, public sector, partner, and small business segments, which generates over $30 billion in annual revenue. A seasoned leader in cloud, AI, and communications, Shawn’s unique knowledge of software, security, and infrastructure is helping enterprises, government, and small businesses harness the power of technology to successfully execute on their digital transformation. In his current role, Shawn is responsible for wireline, 5G/wireless, voice/UC, security/SASE, data center, AIOps, and Network-as-a-Service product lines.

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Frequently Asked Questions

1. Why is AT&T partnering with hyperscalers?

AT&T sees hyperscalers as natural partners, not competitors. As Shawn Hakl explains, AT&T has more endpoints than anyone—it’s the origination point of every transaction, while the hyperscalers are the destination. Rather than investing in GPU infrastructure, AT&T is doubling down on what only it can do: provide the flattest, fastest, most secure path from the network edge into the cloud. The AWS Interconnect – last mile announcement is the clearest expression of that strategy.

2. What is AT&T’s AWS Interconnect – last mile and why does it matter for AI?

AWS Interconnect – last mile connects AT&T’s fiber and fixed wireless directly to AWS environments, letting enterprises manage the network as a resource inside their Amazon instance. For AI workloads, this matters because agentic applications require extremely low latency. Traditional cloud apps were built for 25–30 millisecond round trips, but multi-agent interactions can demand 5–7 milliseconds. A harmonized, managed path from the network edge to the cloud removes a critical bottleneck.

3. How did AT&T cut network latency from 110ms to 40ms for a real customer?

Working with Cisco and NVIDIA, AT&T connected the radio access network edge directly to GPU-as-a-service infrastructure at the edge. In a live deployment with TanMar, running AI across roughly 100 cameras in five states, response latency dropped from 110ms to 40ms. The use case was video processing: license plate and facial recognition where real-time decision-making is essential. Not every workload benefits from edge AI, but for latency-sensitive applications, the results are significant.

4. What does it mean to make a network “agent-consumable?”

As DR highlighted in the episode takeaway, an agent-consumable network needs to be discoverable (agents can find what they need without being told), predictable (agents get consistent responses), and programmatic (agents never have to leave their own workflow). Shawn Hakl describes it as building layered interfaces exposed through established developer ecosystems, with zero-trust security throughout. Agents can discover, access, and act on network resources without human intervention.

5. How does Totogi help enterprises avoid “semantic chaos” as AI agents scale?

As AI agents multiply across enterprise environments, they need shared business context to act consistently and correctly. Without it, agents misinterpret data, duplicate effort, or take conflicting actions. The Totogi Ontology gives AI the structured business context it needs to discover services, understand relationships, and execute reliably—exactly the kind of foundation Danielle Rios emphasizes when she talks about building networks and systems that agents can actually consume.

6. How is AT&T using AI internally, and what does that tell us about enterprise readiness?

AT&T’s internal Ask AT&T platform has scaled to more than 100,000 users consuming 27 billion tokens a day, with nearly 1.8 billion daily transactions running in production environments. Teams are now building custom AI agents using a drag-and-drop tool. Shawn Hakl’s point is that AT&T isn’t just selling AI solutions to enterprises. It’s running on them. That real-world experience informs how AT&T advises customers on their own AI deployments.