The Gap Between AI Spending and AI Value

The Gap Between AI Spending and AI Value

Author: Software Engineering Daily August 25, 2026 Duration: 54:42

It is widely reported that a gap has emerged between enterprise spending on AI and the durable value captured from that spend. Individual employees have enthusiastically adopted coding assistants and chatbots, yet those gains do not seem to be transforming businesses at an organizational level. One of the most important questions in the tech industry today is understanding why AI is not yet delivering returns that match the investment, and what separates the small number of enterprises succeeding from the many that are not.

Scale AI is known for supplying the human-labeled data behind many frontier models. It now also builds AI applications and agents for large enterprises. That combination of working alongside frontier labs and inside enterprise deployments gives the company a rare view of why enterprise AI may be stalling.

Emily Xue is the Head of Enterprise AI at Scale AI, and previously spent over a decade at Google. In this episode, Emily joins Kevin Ball to discuss the three layers where enterprise AI breaks down, why frontier model benchmarks miss what enterprises actually need, the data foundation problem, how the most successful companies combine internal domain expertise with outside AI specialists, and more.

Sponsorship inquiries:
sponsor@softwareengineeringdaily.com

The post The Gap Between AI Spending and AI Value appeared first on Software Engineering Daily.


Dive deep into the conversations shaping the future of artificial intelligence with the Machine Learning Archives-Software Engineering Daily. This curated collection pulls from the broader Software Engineering Daily library, focusing entirely on the intricate world of ML engineering. Each episode features a detailed, technical interview with engineers, researchers, and architects who are building the systems behind today's most advanced AI. You'll hear them break down complex topics like model deployment, data pipeline challenges, and the practical trade-offs involved in taking research from a notebook into production. The discussions are grounded in real-world implementation, moving beyond theoretical concepts to explore the tools, failures, and successes that define the field. For developers and technical leaders looking to understand the nuts and bolts of applied machine learning, this podcast offers a valuable archive of knowledge. It's a direct line to the practitioners who are solving hard problems every day, providing insights you can apply to your own work. Tune in to gain a clearer perspective on how machine learning is integrated into modern software, one detailed conversation at a time.
Author: Language: en-us Episodes: 50

Software Engineering Daily
Podcast Episodes
Scaling Agent Workloads at Vercel [not-audio_url] [/not-audio_url]

Duration: 51:25
Most AI agent setups today are built around a single session, where one user interacts with one agent at a time. However, that model breaks down when an agent has to serve a business, where thousands of requests can arri…
Inside Google’s Database Infrastructure for the AI Era [not-audio_url] [/not-audio_url]

Duration: 1:18:26
Historically, databases were responsible for storing data and returning exact results in response to queries. However, AI is now bending that contract in a new direction. Applications increasingly expect structured and u…
A Rust Framework to Simplify Distributed Systems [not-audio_url] [/not-audio_url]

Duration: 50:31
A Rust Framework to Simplify Distributed Systems Building software that runs across many machines is notoriously difficult. Developers have to grapple with problems such as race conditions, partial failures, and message…
Moving Beyond RAG with Precomputed Context [not-audio_url] [/not-audio_url]

Duration: 55:48
Retrieval has become one of the central problems in building useful AI systems. The standard approach to grounding a model in one’s own data has been retrieval augmented generation, or RAG, where an agent searches a vect…
The Death of Online Anonymity [not-audio_url] [/not-audio_url]

Duration: 52:42
Age verification is reshaping how people access the internet. An ever-growing patchwork of laws can now require government IDs, facial age estimation, or behavioral inference before you can enter digital spaces. Discord,…
TypeScript 7 and What Comes Next [not-audio_url] [/not-audio_url]

Duration: 57:15
TypeScript is a programming language that builds on JavaScript by adding a system of types. Those types let developers describe the shape of their data and catch mistakes before code ever runs, while also powering the au…
AI and the New Global Security Landscape [not-audio_url] [/not-audio_url]

Duration: 1:12:32
The conversation about AI often focuses on software, automation, and the race between attackers and defenders in code. However, some of the most consequential risks lie further afield, in domains where a mistake is measu…
How LLMs Are Reshaping Recommendation Systems [not-audio_url] [/not-audio_url]

Duration: 47:51
News feeds and recommendation systems have long relied on deep learning architectures that score each candidate item independently. As LLMs have matured, they have opened up a fundamentally different approach, where a sy…
Rebuilding the Cloud for AI Agent Code [not-audio_url] [/not-audio_url]

Duration: 49:57
For two decades, the cloud has been shaped by human developers writing code and managing its deployment. Now a growing share of production code is generated by LLMs with little human review. Because that code is not full…