The AI Report

Brad Axen, Block's Head of AI Capabilities and the original author of Goose, joins Liam to talk about what actually makes AI useful at work. They cover how Block went from building an early open-source AI agent to MoneyBot, ManagerBot, BuilderBot and Buzz, and why Brad thinks the hardest problems now are memory, access and interface, not just model intelligence.

Brad also explains why AI memory should belong to the business rather than a single bot, what ants can teach us about shared memory systems, why "meat proxy" is becoming a new office problem, and how Buzz is testing a multiplayer model where humans and AI agents work in the same space.

Key Topics Covered

Goose, Block's open-source AI agent, and the Agentic AI Foundation

Agents vs. harnesses vs. interfaces

MoneyBot, ManagerBot and BuilderBot

Why AI memory should belong to the business, not the bot

Stigmergy and ants as a model for shared memory

Buzz and Block's "multiplayer" approach to AI at work

"Meat proxy" and the new office busywork AI can create

Why the bottleneck is shifting from writing code to deciding what to build

How AI is changing hiring, interviews and day-to-day work

The human cost of spending all day working with AI

Episode Timestamps

00:00 Intro

00:07 What Block actually is

02:40 From CERN to Block

05:19 Building Goose and taking it open source

06:29 Agents vs. harnesses vs. interfaces

10:28 MoneyBot, ManagerBot and BuilderBot

15:56 Memory, access and learning over time

22:53 What ants can teach us about AI memory

26:48 Two versions of where AI could go

28:26 Buzz and the idea of multiplayer AI

29:56 "Meat proxy": the new office problem

35:09 The Buzz case study and a 50% productivity jump

37:06 The new bottleneck now that AI can write the code

49:11 Rebuilding institutional knowledge after team restructuring

52:00 How AI is changing hiring and interviews

57:35 The loneliness of working with AI all day

59:13 Why Brad does what he does

Connect with Brad on LinkedIn:

https://www.linkedin.com/in/bradleyaxen/

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In this episode, Loni Stark, VP of Strategy and Product at Adobe, joins Liam to talk about what happens when a 25 year tech career runs alongside a full creative practice in painting, sculpture and writing, and what that split brain teaches her about building for the AI era. Loni explains why she thinks brands may already be invisible, or worse, misrepresented, inside AI answers, why every company needs to start treating AI as a new kind of audience, and how she is running her own home AI lab, complete with a self built server and a personal agent that has now run continuously for over 150 days, to understand what actually gives an AI agent an identity.

Along the way, Loni and Liam get into her Harvard Extension School research into "orphan values," the personal values people can't express in any of their current life roles, and what happens to that alignment as AI reshapes the roles themselves. She also breaks down how she balances Adobe's biggest enterprise bets, including Experience Manager, Commerce, Brand Concierge and LLM Optimizer, against the need to experiment without limits in her own time.

Key Topics Covered

Why Loni keeps a full art practice, painting, sculpture and writing, alongside her tech career

Growing up with parents who didn't understand the arts, and using creativity as a form of rebellion

Whether humans are innately creative, and why AI makes protecting your own voice more important

Why Loni has stayed at Adobe for 25 years, and how she thinks about "growing the aquarium"

Building a personal AI server at home instead of a garden, and what that setup actually involves

Swapping the underlying model and the agent harness to test what gives an AI agent a persistent identity

Her Harvard Extension School research into "orphan values" and how AI is reshaping the roles we express them through

The shift from human mediated to AI mediated experiences, and why that changes what "traffic" even means

Why being invisible to AI isn't the worst case, being misrepresented by it is

How brands should start preparing their content and catalogs to be "agent ready"

The placebo effect of working with agents, and how that belief shapes performance and creativity

How Loni balances limitless experimentation with the governance enterprise AI actually requires

Why she does what she does: an insatiable need to grow, create and become more than she currently is

Episode Timestamps
00:00 - Introduction and welcome
00:05 - Balancing a full art practice with a 25 year tech career
05:38 - Why she's stayed at Adobe for 25 years
08:30 - AI as the biggest creativity enabler she's seen
12:50 - Inside her home AI lab: hardware, memory, and swapping the agent harness
18:11 - Studying psychology at Harvard, and what "orphan values" mean
26:28 - The shift to AI mediated business, and why invisible isn't the worst case
31:46 - How brands become "agent ready" for the AI agent economy
36:23 - The placebo effect of working with AI agents
37:35 - Balancing limitless experimentation with enterprise governance
47:05 - Why Loni does what she does
49:14 - Where to find Loni and closing thoughts

Loni's Socials:
LinkedIn - https://www.linkedin.com/in/lonistark/

Loni’s Art Gallery: https://atelierstark.com/work/

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In this episode, Braden Warwick, Financial Planning Product Architect at PWL Capital, breaks down why so much of the financial advice sold at big banks is a sales pitch dressed up as a plan, and what a real financial plan actually requires. Braden traded a PhD in aerospace engineering for a career rebuilding how Canadians plan their money, and he brings that same engineering mindset to financial planning: define your objectives, map your constraints, then solve for the outcome that actually improves your life.

Braden also walks Liam through the AI infrastructure PWL has built in house, from a proprietary data lake to an AI powered meeting note tool and planning summaries, and explains why they chose to build their own tools instead of buying off the shelf software. They get into Monte Carlo simulations, why financial planning is really about the distribution of outcomes rather than one predicted path, and what a financial planning engagement might look like in 2031.

Key Topics Covered

How Braden went from a PhD in aerospace acoustics to building financial planning tools at PWL Capital

Why PWL's advisors are paid for advice, not for selling products, and how that changes the plan you get

The six areas of a real financial plan: investing, cash flow, tax, insurance, retirement, and estate

Treating a financial plan like an engineering problem: objectives, variables, and constraints

Why Monte Carlo simulations model financial planning as a distribution of outcomes, not one fixed path

What forms of uncertainty most financial software still misses, from real estate values to life expectancy

Why PWL built its own AI meeting note tool and data lake instead of buying an off the shelf solution

How AI is helping PWL's advisors scale personalized, evidence based financial plans

PWL's acquisition by One Digital and what it changed, and did not change, about how Braden works

What a financial planning engagement could look like by 2031

Episode Timestamps

00:00 - Introduction

00:40 - From aerospace engineering to financial planning

03:54 - Why PWL approaches financial advice differently

07:31 - The six areas of a real financial plan

11:48 - Financial planning as an engineering problem

17:56 - The psychology behind financial planning

23:14 - Objectives, constraints, and uncertainty

28:10 - How Monte Carlo simulations work

33:21 - What financial planning software still misses

39:11 - Building financial planning tools at PWL

44:16 - Inside PWL's financial planning system

51:38 - How AI is changing the advisor workflow

57:20 - Why PWL built its own AI tools and data infrastructure

1:03:41 - What changed after the OneDigital acquisition

1:06:34 - The future of financial planning

1:11:47 - Why Braden does what he does

Braden's Socials:
LinkedIn - https://www.linkedin.com/in/braden-warwick-a40b48a3/

Resources Mentioned:

Braden’s article, The Optimal Financial Plan - https://pwlcapital.com/the-optimal-financial-plan/

Partner Links
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In this episode, Andrew McNamara, VP of Applied ML at Shopify, returns to unpack how much has changed in agentic commerce since his last episode. Andrew and Liam dig into why agents are becoming "the new front door to commerce," why orders coming to Shopify stores from AI are up 13x, and what's actually happening inside Shopify's personalized shopping agent in the Shop app.

They also get into the Universal Commerce Protocol (UCP) and why AI commerce is growing 9x faster than social commerce did at the same stage, how Sidekick's architecture and app extensions work, and SimGym, Shopify's system for training AI shoppers to A/B test store changes before they ever reach a real customer.

Key Topics Covered

How shopping is shifting from stores and desktops toward agents as "the new front door to commerce"

Why orders coming to Shopify stores from AI are up 13x, and why catalog-powered AI search converts twice as well as general AI search

Inside Shop app's personalized shopping agent, and how it learns different shopping personas (like shopping for a pet versus a child)

Why customers are shifting from keyword searches to natural language queries, and the higher conversion rates that come with it

Why Shopify keeps shopping data personalized to the individual user rather than training it into a larger internal model

What the Universal Commerce Protocol (UCP) is, and why AI commerce is growing 9x faster than social commerce and 3x faster than mobile did at the same stage

The story of Shopify's CEO giving his own Hermes agent a budget so it can send him gifts in the mail

Sidekick's app extensions, and how partners like Klaviyo and Loop plugged in at launch

Campaign Autopilot's "auto research loop," and its parallels to reinforcement learning

SimGym, and how Shopify trains AI shoppers to A/B test store changes before running them on real customers

Why Sidekick runs on Anthropic's Sonnet model hosted on Google Cloud, and why that choice is model agnostic

Andrew's own habit of shopping by taking pictures throughout the week and searching by image through UCP-connected agents

Episode Timestamps:

00:00 - Introduction and welcome

00:29 - What's changed in AI and shopping since their last conversation

01:47 - Agents becoming "the new front door to commerce"

04:16 - Inside Shop app's personalized shopping agent

07:32 - Why data stays personalized to each shopper instead of training a larger model

11:53 - What the Universal Commerce Protocol (UCP) is, and orders from AI up 13x

14:58 - Merchant tooling for tracking AI-driven traffic and conversions

15:55 - The story of Tobi's Hermes agent sending him gifts in the mail

20:48 - Andrew's own habit of shopping by taking pictures and searching by image

26:59 - Sidekick's app extensions and partner integrations

33:02 - Inside Sidekick's architecture: the Sonnet model and knowledge base

35:18 - Campaign Autopilot's auto research loop

38:58 - SimGym: training AI shoppers to test store changes

42:23 - What's next for Shopify's agentic commerce features

44:17 - Where to find Andrew

Andrew's Socials:

Twitter (X) - https://x.com/DrewCH

LinkedIn: https://www.linkedin.com/in/andrewmcnamara1/

Partner Links

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David McIntosh, Chief Connected Stores Officer at Instacart, joins Liam to explain why the company is betting on smart shopping carts instead of rewiring stores with ceiling cameras. David walks through the $350 million acquisition of Caper, how Instacart is now live in more than 100 cities with thousands of connected carts, and why the screen on the cart, not the checkout speed, turned out to be the real driver of sales lift for retailers.

David also gets into the surprisingly hard engineering problems behind a smart cart, like figuring out whether a basket is actually empty, fusing camera and scale data in real time, and building recommendations that know exactly where a shopper is standing in the store. He and Liam talk about who owns all that shopping data, what agentic AI looks like when it moves from chat into the aisle with tools like Cart Assistant, and why grocery budgets and meal planning are becoming one of the most requested AI features in the store.

Key Topics Covered

Why David left Tenor, the GIF search engine used by billions, to build Instacart's Connected Store business

The strategic bet behind unifying online and in-store grocery shopping

Why Instacart acquired Caper for $350 million instead of building smart carts in-house

The reason Instacart chose carts over ceiling cameras for in-store AI

How a simple running total and real-time coupons drive measurable sales lift

The NVIDIA Jetson hardware and multimodal sensor fusion that let the cart "see" what's in the basket

The strange edge cases in physical AI, like why "is this cart empty" is a genuinely hard question

Who owns retailer and shopper data, and how it's used to improve recommendations

Cart Assistant: how Instacart lets customers shop inside ChatGPT and directly on retailer websites

Using agentic AI to fix store operations like out-of-stock items and supplier issues

How budget-conscious meal planning became one of the most requested AI features in the store

David's answer to Liam's closing question: why he does what he does

Episode Timestamps
00:00 - Introduction and welcome
00:14 - David's path from Tenor to Instacart's Connected Store
02:01 - The bigger bet behind bringing online and in-store shopping together
05:29 - Entering the smart cart market and acquiring Caper
08:07 - Caper's scale today: 100+ cities and millions of daily sensor inputs
10:28 - How the smart cart actually drives sales lift
12:49 - Why Instacart bet on carts instead of ceiling cameras
17:33 - The unglamorous detail that makes or breaks adoption: charging
19:26 - What makes the experience sticky enough to keep customers coming back
24:41 - Inside the hardware: NVIDIA Jetson and multimodal sensor fusion
28:43 - The strange edge case behind a seemingly simple question
35:14 - Who owns the shopping data, and how retailers use it
37:30 - Agentic shopping: Cart Assistant and buying inside ChatGPT
42:16 - Using agentic AI to fix store operations, not just shopping
46:17 - Why David does what he does

Connect with David on LinkedIn:
LinkedIn: https://www.linkedin.com/in/mcintoshdavid/

Partner Links
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