Speakers

• Eric Gerster, CFA®: Chief Investment Officer at AlphaCore
• Ashley MacNeill: Co-Head of Capital Markets at Vista Equity Partners
• Brian Loring: Senior Vice President at Vista Equity Partners

Chapter 1 (00:01): Welcome

Eric Gerster, CFA®, Chief Investment Officer at AlphaCore (00:01)
Well, welcome everyone to our monthly AlphaCore webinar series. I’m Eric Gerster, Chief Investment Officer at AlphaCore. We’re excited about today’s discussion and appreciate you taking time out of your schedule to join us.

For those who may not know us, AlphaCore is a registered investment advisor headquartered in La Jolla, California, with offices across the country. We’re a full-service wealth management firm that combines comprehensive wealth planning with a dedicated investment research team, allowing us to build customized investment strategies designed to help our clients reach their financial goals.

With that, let’s jump in. Artificial intelligence has taken the world by storm since ChatGPT launched in late 2022. We’re seeing its impact everywhere—from the stock market to how we make restaurant reservations, plan trips, and even how businesses operate.

Today, we’ll discuss what AI means for public markets and then shift to real-world use cases, including how AI is creating meaningful value across enterprise software companies.

I’m pleased to be joined by Ashley MacNeill and Brian Loring from Vista Equity Partners.

Ashley, I’ll turn it over to you.

Chapter 2 (02:25): Vista Overview + Market Context

Ashley MacNeill: Co-Head of Capital Markets at Vista Equity Partners (02:25)
Thank you so much for having us. Vista Equity Partners is a private equity firm with just over $100 billion in assets under management. We invest across a broad range of market capitalizations—from under $100 million to several billion—and we focus primarily on what historically has been enterprise software, which is increasingly evolving into agentic enterprise software.

Your timing for this discussion is impeccable given the dislocation we’re seeing in public markets around this asset class. We’re excited to dive in.

Chapter 3 (03:10): Public Market Dislocation + AI Value Cycle

Eric (03:10)
Over the last 12 months—especially this year—we’ve seen publicly traded enterprise software companies experience significant selloffs. Vista has invested in software for 25 years through multiple cycles.

Walk us through Vista’s view on what’s happening in public markets and how that connects to AI overall.

Ashley (04:16)
What you’re seeing in the public markets is recognition that, in this AI wave, software is not yet viewed as the fastest-growing, most durable, highest-margin asset class. There’s a lack of data points to get investors excited.

We believe AI adoption will follow three phases—similar to past technology transitions:

1. Hardware and semiconductors
2. Hyperscalers and AI enablers
3. Enterprise software embedding AI into core workflows

That third wave is emerging now. It’s exciting—but until we see data points proving AI-embedded software is delivering results, uncertainty will remain.

Chapter 4 (06:39): Why 2026 Is Pivotal

Eric (06:39)
Where are we in the value creation cycle? How far are we from seeing evidence that software companies are driving real AI value?

Ashley (06:39)
It feels like we’re at the precipice. I believe 2026 will be a critical year for enterprise AI adoption.

For AI investment to justify itself, everyday companies must adopt it. And to adopt AI meaningfully, they need software to facilitate and operationalize it.

It won’t happen overnight. But as we begin hearing from major consumer companies about efficiencies driven by AI, value will start shifting from semis and hyperscalers toward software and agentic enterprise platforms.

Change takes time. Cost curves must make sense. Adoption requires willingness. But we believe the inflection point is approaching.

Chapter 5 (10:22):Why AI Won’t Eliminate Enterprise Software

Eric (10:22)
You mentioned several reasons AI won’t eliminate enterprise software. Can you revisit those?

Ashley (10:22)
Three primary reasons:

1. Incumbency and switching costs
Enterprise sales cycles can take years. Many platforms have 20+ years of proprietary workflows and embedded data. Enterprises won’t rip that out for a three-year-old AI-native company.

2. Trust and determinism
AI is probabilistic. Enterprise workflows often require deterministic execution. Payroll must execute correctly every time. Software will leverage AI thinking—but execute with certainty.

3. Expanding TAM
AI expands software’s total addressable market. If agents perform work, software must orchestrate and manage them. That creates massive opportunity.

Chapter 6 (14:05):Private vs Public Software

Eric (14:05)
What’s unique about private software companies adopting AI versus public ones?

Ashley (15:08)
The public market represents less than 5% of available data points on software health. In private markets, companies often have more flexibility to experiment and embed AI without quarterly earnings pressure.

Chapter 7 (16:59): IPOs, M&A, and New KPIs

Eric (16:59)
How does AI impact exits and dealmaking?

Ashley (16:59)
AI will reshape public equity and M&A markets. We’ve seen cycles like this before.

I also expect new KPIs centered around AI—metrics like revenue per head, number of agent tasks completed, ROI per agent. Investors need clearer ways to evaluate AI usefulness.

Chapter 8 (19:30): Agentic AI Explained

Eric (19:30)
Brian, what does agentic AI mean?

Brian Loring, Senior Vice President at Vista Equity Partners (19:30)
AI evolved from machine learning to generative AI—and now to agentic AI.

Agentic AI can perform tasks on your behalf without step-by-step prompts. For example, you could tell an agent to handle your monthly bills, and it would determine what needs to be paid and execute it.

This moves software from productivity assistance to actual work execution.

Chapter 9 (22:31): Hallucinations + Governance

Eric (22:31)
What about hallucinations?

Brian (23:11)
Hallucinations occur because models are probabilistic—they predict likely answers based on patterns. Sometimes they’re wrong.

In regulated industries, workflows must be deterministic. If the same loan profile is input, the same answer must result every time. That’s why governance layers and guardrails are critical.

Chapter 10 (26:33): What Could Delay Adoption?

Eric (26:33)
What could delay adoption?

Brian (26:33)
Two main factors:

1. Trust — compliance, governance, reliability
2. Culture — employees must adopt tools without fear

The bottleneck is often change management, not technology.

Chapter 11 (28:51): Advice for Everyday Users

Eric (28:51)
What advice would you give individuals trying to use AI better?

Brian (29:22)
Experiment. Try things. Fail fast.

Also—ask the AI how to use it. Describe your workflow and ask how it can help. It becomes collaborative.

Chapter 12 (32:41): Productivity + Revenue in Portfolio Companies

Eric (32:41)
What are you seeing across Vista’s portfolio?

Brian (33:10)
We think about AI impact in two buckets:

• Internal productivity gains
• Revenue growth through agentic offerings

2024 was experimentation.
2025 was tool selection and target setting.
2026 is “show me the money.”

Chapter 13 (37:19): LogicMonitor Case Study

Brian (37:19)
LogicMonitor helps IT departments resolve incidents. With agents, it now reduces resolution time and increases efficiency.

Its agent product grew 3x last year. It’s an early example of agentic monetization.

Chapter 14 (41:55): Seat-Based vs Outcome-Based Pricing

Eric (41:55)
How does pricing change?

Brian (41:55)
We’re moving from seat-based pricing toward outcome-based pricing—sharing in the value delivered.

It will take time. The on-prem-to-cloud transition took a decade. This shift will be multi-year as well.

Chapter 15 (49:51): New Winners + Long-Term Outlook

Eric (49:51)
Will we see new winners like we did in cloud?

Brian (50:32)
Today’s AI-native companies are creating new categories—not disrupting existing ones yet.

Over time, winners will emerge. But agents require context, governance, and workflow integration—advantages incumbents already possess.

Chapter 16 (54:16): Closing

Eric (54:16)
Brian, thank you—and thank you to Ashley and Vista Equity Partners for joining us.

Brian (54:16)
My pleasure. Thanks so much, Eric.

Eric (54:16)
Take care.