Simon Chesterman is AI Governance and Policy Lead at the NUS AI Institute, David Marshall Professor of Law and Vice Provost at NUS, and Dean of NUS College. He is also a novelist. He spends his working life on how law can help manage risk, and his fiction on what stays human. He spoke with us here in his personal capacity, not on behalf of NUS or AI Singapore.

in this conversation we cover:

- why we are all beta testers and lab rats in a live experiment

- the risk he actually loses sleep over, and why it is not the Terminator

- what happens to you when you outsource reading, then writing, then judgment

- why an AI that flatters you is not on your side

- a test you can run today to find out where AI is weak

- what he thinks universities and entry level jobs look like on the other side of this

hosted by Eric Tan, Yaohong could not make it for this episode.

our guest: Simon Chesterman, David Marshall Professor of Law, Vice Provost, NUS AI Governance and Policy Lead, NUS AI Institute and author of the novel Artifice.

learn along:

- frontier lab: the handful of companies building the largest AI models

- open weights: releasing the model's internals so anyone can run and modify it

- closed model: the company keeps the internals and you access it through their service

- cognitive offloading: handing your thinking to a tool until you get worse at doing it yourself

- sycophancy: when a system agrees with you and flatters you because that keeps you engaged

- legal personality: being treated by the law as someone who can hold rights and be held responsible. People have it. So do companies

- sovereign AI: a country building its own AI capability rather than depending on another country's

- p(doom): the probability an AI researcher privately assigns to this technology ending very badly

where Simon says to start, if you want to go deeper

- Singapore's Model AI Governance Framework, which collects real use cases

- the AI Verify Foundation

- the international AI safety report led by Yoshua Bengio and others

- the global policy dialogues at the UN

- and his own advice: read widely, test the systems against each other, ask them about their own guardrails, then come to your own conclusions

the research behind this conversation

we discuss four of Simon's papers on tape. to read more:

- All Rise for the Honourable Robot Judge? Using Artificial Intelligence to Regulate AI (2023), Technology and Regulation: https://techreg.org/article/view/17979

the paper eric asks about, and where the AI Supreme Court and AI Prime Minister line comes from

- Artificial Intelligence and the Limits of Legal Personality (2020), International and Comparative Law Quarterly: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3682372

the argument that you could give AI legal personality but you should not, including the river in New Zealand

- Silicon Sovereigns: Artificial Intelligence, International Law, and the Tech-Industrial Complex (2026), American Journal of International Law: https://arxiv.org/abs/2505.05506

AI research moving out of the universities into private hands, and the closing warning about the first and second AI emergency

- Weapons of Mass Disruption: Artificial Intelligence and International Law (2021), Cambridge International Law Journal: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3832563

why AI gets none of the audits and licensing we give chemicals, nuclear materials and biological weapons research

- his full list of papers: https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=244368

books mentioned

- Artifice, Simon Chesterman (his novel, the one he wrote as fiction before this became the news)

- We, the Robots? Regulating Artificial Intelligence and the Limits of the Law, Simon Chesterman

- Superintelligence, Nick Bostrom

- The University Disrupted, Simon Chesterman, forthcoming

watch the video version on YouTube: https://www.youtube.com/@CodeRiffAI

connect with us

- Eric: https://www.linkedin.com/in/erictisme/

- Substack: https://substack.com/@coderiff

- email: code.riffs.ai@gmail.com

Code Riff, interviews with leading minds and practitioners in AI so you can flourish and have fun with ai.

tools we use

- Buzzsprout (podcast hosting, our referral link): https://www.buzzsprout.com/?referrer_id=2371679

Cordillia Tan is better known to many Singaporeans as "Tyre Jie Jie". with no driving licence and almost no automotive knowledge, she stepped into her family's tyre shop when it was roughly S$400,000 in debt, and helped turn Pitstop into a multimillion-dollar digital automotive business. after five years in the workshop she is building PetaniAI, a home for the AI-curious in Southeast Asia.

in this conversation you'll hear:

- eight months of TikToks that went nowhere before one video changed the trajectory, and what she thinks the scarce advantages are now that AI makes building cheap: attention, trust and distribution

- going beyond SEO into AEO: letting the model audit the site, surface the content gaps her whole industry was missing, then feeding it a fact nobody else in the world publishes so the answer has to cite her

- the day she couldn't make a S$20,000 rent payment with S$2,000 in the bank, asked ChatGPT for legal ways to raise it overnight

- the internal tools she built with Claude Code: an inventory system, a used tyre pricing tool, and a quote log to handle prices that kept moving

- the builds that didn't last, and what made the difference between the ones that stuck and the ones nobody used

- why WhatsApp stayed the interface for her team, instead of forcing them onto something new

our guest: Cordillia Tan, Co-Founder of PetaniAI (https://petaniai.com/)

hosted by Eric Tan and Yaohong Ch'ng

produced by PetaniAI

glossary, in plain english:

- vibe coding: building software by describing what you want to an AI coding tool, testing what it produces and improving it through conversation.
- machine vision: software that reads and understands images. Cordi used it to identify tyre details from a photograph.
- AEO: writing so that AI assistants quote you when someone asks them a question, rather than writing to rank on a search results page.
- moat: an advantage that makes a company difficult to copy or compete with.
- distribution: the ability to consistently reach the right audience and get a product, idea or piece of content in front of them.
- AI slop: generic, low-effort AI-generated content that sounds polished but offers little original insight or value.

connect with us

- want to suggest a guest or a topic? tell us: https://forms.gle/DSyLzPAoR6x2M4Np9
- Eric: https://www.linkedin.com/in/erictisme/
- Yaohong: https://www.linkedin.com/in/yaohongchng/
- Superuser HQ (Yaohong's company): https://superuserhq.com/
- Substack: https://substack.com/@coderiff?utm_campaign=profile&utm_medium=profile-page
- WhatsApp community: https://chat.whatsapp.com/Dmp5eEEsAZhJTB6LjcIG3c?mode=gi_t
- email: code.riffs.ai@gmail.com
- PetaniAI: https://petaniai.com/ and https://www.linkedin.com/company/petaniai/

Code Riff - a podcast hosted by Eric Tan and Yaohong Ch'ng. we interview leading minds and practitioners in AI so you can flourish and have fun with ai.

Roger Olofsson is MD and Co-Founder of an AI-native recruitment firm Olofsson Consulting (https://olofsson.ai/), having spent ~30 years in tech executive search in Singapore.

in this conversation we cover:

- how Roger ended up settling down in Singapore (from Sweden!)
- the live walkthrough of their matching system which consists of 395K candidates
- how Roger transformed into an ai-native firm, removing soul-draining work
- why the team is shifting back into claude code after building their own software
- new job creation due to ai
- why he is hopeful given uncertainty

hosted by

- Eric Tan https://www.linkedin.com/in/erictisme/

- Yaohong Ch'ng https://www.linkedin.com/in/yaohongchng/

- Our guest: Roger Olofsson, MD and Co-Founder of Olofsson AI (https://olofsson.ai) https://www.linkedin.com/in/rogerolofsson/

chapters

0:00 cold open
1:28 agenda
8:33 building an ai-native recruitment firm
10:43 why they went all-in on ai
13:19 live showcase: Olofsson AI's internal recruitment platform
21:33 llms find candidates that were once difficult to search for
26:14 how agents save recruiters from doomscrolling linkedin
30:50 getting a team to adopt ai without fear
34:27 how roger aligns incentives for the long run
39:40 how ai helps them compete with giants
42:58 everyone in the firm uses claude code
46:46 emerging roles and job descriptions with ai
52:09 the newest role: agentic product manager
56:25 optimism despite ai fears
59:57 lightning round
1:05:49 post-credits scene: roger's ceo summarizer

tools mentioned in this episode

- Claude Code (the terminal AI Roger's whole team is trained on): https://claude.com/claude-code
- Exa (agentic web search, the POC they showed): https://exa.ai
- Supabase (where their Postgres database lives): https://supabase.com
- Vercel (where the platform is hosted): https://vercel.com
- GitHub (where their skills live): https://github.com
- Lovable (the coding agent that pulled Roger back into building): https://lovable.dev

books Roger recommends:

- The Hard Thing About Hard Things, Ben Horowitz
- Zero to One, Peter Thiel

connect with us
- Substack: https://substack.com/@coderiff?utm_campaign=profile&utm_medium=profile-page

- email: code.riffs.ai@gmail.com

- Superuser HQ (Yaohong's company): https://superuserhq.com/


tools we use

- Buzzsprout (podcast hosting, our referral link): https://www.buzzsprout.com/?referrer_id=2371679
- Snipd (how we take notes from podcasts): https://get.snipd.com/pAbF/36jzrvki

LEARN ALONG (glossary for the non-techies)

a few terms from this episode, in plain english:

- ai-native: a company designed around ai from the ground up, instead of adding ai tools onto the old way of working.
- ai agent: a program you hand a goal to, that then takes the steps itself (searching, filtering, writing) instead of you doing each one.
- agentic search: a search where an ai agent runs the whole hunt on its own, refining as it goes, then hands you the results.
- skill: a saved set of instructions an ai can reuse, so a task you taught it once runs the same way every time.
- harness: the setup an ai agent runs inside, which decides what tools and data it can touch.
- frontier models: the newest, most capable ai models from the big labs. Roger builds on these instead of training his own.
- fine-tuning: training an existing ai model further on your own data. Roger's team chose not to, betting the frontier models improve faster.
- context engineering: feeding an ai the right background (like 25 years of recruiting judgment) so it makes decisions the way an expert would.
- context window: how much text an ai can hold in its head at once, measured in tokens (chunks of words).
- Postgres: a widely used free database. the filing cabinet where all the candidate records sit.
- POC (proof of concept): a small trial build to test whether an idea works before committing to it.
- agent orchestrator: a new job Roger described, a person who manages and trains a team of ai agents rather than a team of people.

about code riff

Code Riff - interviews with leading minds / practitioners in AI so you can flourish and have fun with ai

- youtube: https://www.youtube.com/@CodeRiffAI
- spotify: https://open.spotify.com/show/53wI41qQXVTiydVjN7i4uB