The Future of Restaurants in an AI World: Hudson Smith with Olo Founder Noah Glass

Mar 26, 2026

Noah Glass, founder and CEO of Olo, the restaurant tech leader behind digital ordering for 750+ brands. He recounts Olo’s rise from text-based ordering and why structured order data creates a durable moat. He discusses networked guest identities, why restaurants are data poor, and practical AI uses reshaping guest experience and operations.

Ask episode

AI Snips

Chapters

Transcript

Episode notes

ANECDOTE

Olo Began As A Text Ordering Experiment

Noah started Olo as a text-ordering prototype after seeing mobile adoption was nascent and got $500,000 from an investor to quit his job and build it full-time.
The early Palm Pilot/text ordering origin showed the value of being directionally right before the smartphone era and seeded two decades of product evolution.

INSIGHT

Structured Orders Create A Durable Moat

Restaurant orders are highly structured with many modifiers, making accurate digital capture technically complex and defensible against naive AI competitors.
Olo captures granular attributes (size, milk, modifiers) across 750+ brands and ~90,000 locations, creating durable structured data advantages.

INSIGHT

Borderless Accounts Resolve Guest Identity

Olo builds a borderless Olo account to resolve guest identity across brands, turning fragmented restaurant transactions into unified guest profiles.
With ~20M guests in Olo accounts and visibility into digital and off-platform payments, restaurants can finally see full guest lifetime behavior.

Get the Snipd Podcast app to discover more snips from this episode

Get the app

At Thoma Bravo’s 2025 AI Summit, Partner Hudson Smith sits down with Olo Founder and CEO Noah Glass to unpack one of the firm’s most compelling recent investments.

From its early days as a text-based ordering tool to becoming a category leader powering digital experiences for over 750 restaurant brands, Olo has spent two decades shaping how consumers interact with restaurants. In this conversation, Noah shares the origin story behind the company, the strategic thinking behind taking Olo private in 2025, and how data, network effects and AI are redefining hospitality at scale.

They also explore what makes restaurant technology uniquely complex, why structured data helps create a durable moat, and how AI is expected tol transform both the guest experience and restaurant operations in the years ahead.

For more information on Thoma Bravo's Behind the Deal, visit https://www.thomabravo.com/behindthedeal

Learn more about Thoma Bravo: https://www.thomabravo.com/

Learn more about Olo: https://www.olo.com/

Disclaimer:

This podcast may contain forward looking statements reflecting current views regarding market conditions, technological evolution, and anticipated developments, which are based on current expectations and assumptions, involve risks and uncertainties that could cause actual results to differ materially, and speak only as of the date of recording with no obligation to update.

This podcast is for informational purposes only and does not constitute an advertisement. Views expressed are those of the individuals and not necessarily the views of Thoma Bravo or its affiliates. Thoma Bravo funds generally hold interest in the companies discussed. This podcast should not be construed as an offer to solicit the purchase of any interest in any Thoma Bravo fund.

Certain statements about Thoma Bravo made by portfolio company executives are intended to illustrate Thoma Bravo's business relationship with such persons rather than Thoma Bravo's capabilities or expertise with respect to investment advisory services. Portfolio company executives were not compensated in connection with their podcast participation, although they generally received compensation and investment opportunities in connection with their portfolio company roles and in certain cases are also owners of portfolio company securities and or investors in Thoma Bravo funds. Such compensation and investments subject podcast participants to potential conflicts of interest. In addition, this podcast includes forward-looking statements. These statements are based on current expectations and assumptions and involve significant risks and uncertainties that could cause actual results to differ materially.