Steve Hind is co-founder and CEO of Lorikeet, an Australian AI customer support company that have raised over AU$100 million and expanded to the US and UK. Before that he was a product lead at Stripe and Watershed.

He defines being AI native as looking at every problem as an opportunity to define work and hand it to an agent, so the amount one person can get done stops being bounded by hours in the day.

Measured against that, Lorikeet has seen substantial growth across 2026 and virtually no headcount change. Steve walks through how: why it was easier for engineers than for operations teams, why he now believes it's easier to change agents than people, what he puts in the knowledge layer and what he refuses to, and why he thinks most companies will end up with only engineers, sellers and owners.

What you'll take away:
› AI native, defined: "the amount one person can get done or supervise is effectively unbounded" because it's bounded by their ability to package work for agents, not by hours.
› Engineers adjusted fast because the job stayed the same; they climbed "a new rung on the abstraction ladder". Operations teams face a bigger jump: the job itself became building systems.
› "It is easier to change agents than people." His Stripe-era "Good Updates" doc is now a skill that drafts the update and pushes it to the person to edit and send.
› Don't ask people to automate their own job. Ask what's valuable that they never get to. "Instead of automating one job, I might be able to automate five jobs."
› Grinding as a service: a PM once watched a month of sales calls over a weekend and found a feature coming up on 40% of them. Agents now do that on demand.
› Put the raw terminal in non-engineers' hands. Claude Code beat the wrapper products at Lorikeet, even for people who had never opened a terminal in January.
› What still fails: AI writing full of rhetorical flourish, and agents that report ten crises a day when one is real. "It has the energy of a very junior chief of staff."
› Only two roles are certainly durable: engineer and seller. Maybe a third: owner. Hire for builders, and use work trials rather than interviews to find them.

Links
› Steve Hind: https://www.linkedin.com/in/shind/
› Lorikeet: https://www.lorikeetcx.ai/
› Alasdair Bell: https://www.linkedin.com/in/alasdairbell/
› Fractional Head of AI: https://www.alasdairbell.com/

Hosted by Alasdair Bell, fractional head of AI for startups.

Chapters
00:00 Cold open
02:39 Meet Steve Hind
03:06 What AI native means at Lorikeet
04:34 Two kinds of adjustment pain
06:18 Engineers adapt, ops rethinks
08:03 Easier to change agents than people
10:23 One builder per ops team
11:50 Pair builders with experts
13:06 Blessed patterns for agents
14:57 The knowledge layer: build or buy
17:02 Battle cards built at runtime
18:12 Don't automate your own job
19:59 Grinding as a service
22:59 What AI still gets wrong
25:23 Why AI can't write
26:22 Claude Code for non-engineers
28:48 Noncompliance is a system problem
29:56 The last 20% of adopters
31:57 Can versus should
33:59 Engineer, seller, owner
34:54 Hiring for builders
36:30 Work trials over interviews
37:41 Work in ten years

Matt Geleta is Director of AI Operations and Enablement at Harrison AI, one of Australia's leading AI companies — its radiology model passed the UK's toughest exam and beat every frontier lab, it's in 40 NHS trusts, and it recently raised a US $112 million Series C.

You'd assume a company like that gets a free pass to becoming AI native. It doesn't. The pace of change means even Harrison is racing to keep up, so Matt built a dedicated team whose entire job is making the company AI native. He walks me through the whole model, the agent they had to build because nothing off the shelf was good enough, and why he thinks AI should make us better humans, not just more productive ones.

What you'll take away:

• An AI company isn't automatically AI native. The technology moves faster than any organisation, so Harrison built a dedicated team to close the gap.
• The model: one full-stack, generalist team across four pillars — infrastructure, knowledge, tooling, enablement — plus AI champions embedded in every function.
• The biggest unlock was context. Pensive, their in-Slack agent, reads emails, Confluence and Slack through a knowledge graph and vector database that compounds over time. It writes tenders and automates business cases.
• Optimise for speed, not cost. "There's only so much cost that you could ever take out, but there's really no limit to how fast the company could move."
• AI can make you a better human, not just a more productive one. Matt's personal agent reviews everything he writes and says, then flags his EQ and knowledge gaps every week.
• Juniors: hiring has slowed for grads across the market, but younger people often upskill on the tools faster than anyone.

Chapters

00:00 Cold open
03:56 Meet Matt Geleta
05:47 The title he won't put on LinkedIn
07:36 Why commit fully to AI now
10:29 The real cost of waiting
11:33 Harrison's principles for AI
15:16 Bringing every person along
17:31 Even an AI company had to change
18:36 Superhuman, not cost-saving
22:55 Building the AI ops team
26:49 A full-stack generalist squad
31:07 OKRs and owning the work
34:27 Do we still need juniors?
39:46 Pensive, the in-Slack agent
45:07 Matt's personal second brain
50:53 The weekly coaching loop
54:04 AI making us better humans
55:23 What hasn't worked
57:21 Fighting work slop
1:01:00 Why today looks anarchic
1:05:08 A bright, unknowable future

Links

• Matt Geleta: https://www.linkedin.com/in/matthewgeleta/
• Harrison AI: https://harrison.ai/
• Matt's website: https://www.matthewgeleta.com/
• Turing Works: https://www.turingworks.ai/

Hosted by Alasdair Bell, fractional head of AI for startups and partner at AI consultancy Turing Works.

Josh runs Ballpoint, a UK growth agency managing £60M of brand revenue with a team of 12 and 90% of its work is now AI-first where AI does the task.

He also tells me why he thinks the agency model is going to die, and what he's building to replace it. We unpick exactly how he got there: the two-day automation sprint that failed, the January Claude Code month that didn't, the memo and off-site that reset the culture, the team members who chose a different future, and the agent named after a Mad Men character that writes better meeting notes than any transcription tool.

What you'll take away:

› AI-native means AI does the task and you review it. Assisting is the stage before — and most teams are stuck there.
› The adoption playbook: one memo making the change non-optional, one off-site forcing everyone into the command line, build sessions every other Friday.
› Context is the difference between a demo and a tool. Cosgrove, Ballpoint's account-manager agent, works because it reads Notion, Slack, and meeting history — and was tuned against a scoring system until it hit 95%.
› Build the data layer first. Models and tools on top swap in and out.
› Junior roles as we knew them are disappearing. Josh's bet: apprenticeship-style learning by hand before you're allowed to delegate to AI.
› For grads: build something real with Claude Code and put it in a public repo. That's the new CV.

Links

› Josh Lachkovic: https://www.linkedin.com/in/joshlachkovic/
› Ballpoint: https://www.weareballpoint.com/
› Josh's blog: https://read.earlystagegrowth.com/
› The True Believer, Eric Hoffer — the book Josh references on mass movements

Hosted by Alasdair Bell, fractional head of AI for startups and partner at AI consultancy Turing Works.