Cheap code turned Sitemate's customers into competitors. Here's how Hartley responded.

Hartley Pike is co-founder and CEO of Sitemate, which builds software for frontline workers in construction, infrastructure, energy and utilities. Sitemate has thousands of customers, offices around the world and backing from Blackbird. Before founding it, Hartley was a construction engineer at Lendlease, working on projects like NorthConnex.

In 2025 Hartley saw his competition jump tenfold. Other software companies were part of it, but the bigger threat was his own customers, who could now build their own tools as the cost of code collapsed. So he set Sitemate a goal: be ten times better, faster and cheaper than the option of building it yourself.

Hartley walks through each one: how moving the whole engineering, product and design team closer to customers is shrinking the feedback loop from months towards days, how Storm takes on the work that happens around Sitemate's software, why hiring is above plan rather than below it, and how a personal AI agent cleared 18 hours of his admin in one weekend.

What you'll take away:

• The new bar: "you have to be ten times faster at responding to feedback directly from users than that user or company could process a change internally themselves."
• Feedback loops took years with on-premises software and months in the cloud. In the AI era Hartley expects days, even hours: "From the point where a user has friction with something to, six hours later, it's on production."
• Forward deploy the whole organisation, not one role. Every engineering, product and design team member moves closer to customers, and "scaled forward deployed engineers" work the daily stream of user feedback.
• Aspirational jobs to be done: work people would do "if they had infinite hours in the day. But no one actually does them." AI can, because "it's just token costs. It's not real human time."
• Storm checks the work, not just the form. Was the inspection done properly, or just ticked and flicked? Hartley estimates Sitemate couldn't help with seventy per cent of that work before.
• New roles are appearing as fast as old ones go: forward deployed roles, AI ops, and people managing Storm's cost and performance. "People should be much more concerned about a peer who's AI native than about a role being made redundant."
• "Use more AI" isn't an instruction. Sitemate removed limits on tools, rewrote "How Sitemate Communicates" for the AI era, and now rates staff on AI usage against impact in performance reviews. High usage, low impact is a "meat proxy".
• Hartley handed his personal admin to Instinct, a personal AI agent, and processed "about 18 hours of administrative work" in his first weekend.

Links

• Hartley Pike: https://www.linkedin.com/in/hartleypike/
• Sitemate: https://sitemate.com/
• 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 Intro
05:09 Competition jumps tenfold
07:30 Customers building their own tools
10:50 Ten times better, faster, cheaper
13:06 Faster: from years to hours
17:00 Bring engineers closer to customers
19:23 Forward deploy the whole org
22:10 Just because we can, should we?
23:23 Better: building Storm
27:35 Aspirational jobs to be done
29:38 Checking the work, not the form
33:41 Cheaper: what AI did to hiring
37:59 Beyond "use more AI"
39:24 Meat proxies and slop cannons
41:06 Getting people to AI native
43:43 Constraint as a forcing function
46:01 How the CEO role has changed
49:26 Instinct: 18 hours in a weekend
53:52 Advice for a 50-person builder

Lorikeet grew quickly through 2026 with virtually no new headcount. Steve tells us how.

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

Harrison AI is already an AI company. It built a whole team to become AI native.

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.