China AI: From Challenger to Export Industry Globally with Jon Russell

Fresh out of the studio, Jon Russell from Asia Tech Review and executive director at Ellerton & Co., returns to trace how Chinese AI crossed from technological challenger into a commercial industry and a geopolitical export model. He unpacks the first public earnings from Z.ai and MiniMax, where revenue growth above 300% still cannot explain the valuations, and why cutthroat domestic competition makes international expansion existential for Chinese labs. Bernard and Jon examine the hidden on-premises market, the sovereignty stacks forming in Europe and Asia, why the copying narrative around distillation collapses under scrutiny, and China's remaining bottleneck in lithography. Jon closes on what he calls the dome: an AI ecosystem now self-sufficient from technology through to capital."The really impressive thing isn't just that China's building its own tech stack. It's that—it's so integrated. Companies are going public, they're raising money. It's entirely self-sufficient from the technology to the financing side too, right? So I think that's the really crazy bit for me. It's not like they have to build these companies and at some point they have to go public in America, right? They can do this all now in China and Hong Kong. But that's the ultimate thing, right? Capital is what these companies need, and if they can also get that from China too, then that completes the system. So it's almost like a dome has now got on top, right? Everything's in there." - Jon RussellProfile: Jon Russell from Asia Tech Review and executive director at Ellerton & Co LinkedIn: https://www.linkedin.com/in/jmarussell/Newsletter: https://www.asiatechreview.com/Episode Highlights: [00:00] Quote of the Day by Jon Russell[01:30] Introduction: Jon Russell[04:00] Chinese CEOs step onto the stage themselves [06:30] Has China AI crossed from challenger to industry? [07:00] Private AI valuations beyond anything ever seen [09:15] Z.ai revenue growth above 300% [10:30] Valuations conventional ratios cannot justify [11:00] Moonshot postpones its IPO, waiting for Anthropic [13:15] Cloud is marketing, on-premises is the real market [16:00] Why most AI revenue stays out of public view [17:45] Segmenting China AI companies from BAT to Upstarts [19:45] First time the world cares about a Chinese model release [21:15] The belief about Chinese AI the Bay Area gets wrong [23:45] Qwen's problem: huge adoption, tiny revenue [25:15] Chinese models undercutting US margins tenfold [26:00] Not the dotcom bubble — the railway bubble [29:30] Distillation, old books, and the irony of the copying claim[33:30] AI as the next iteration of One Belt One Road [35:30] Why Anthropic prioritises Japan, Korea and Australia [35:45] The shift to ChatGPT Enterprise across the region [37:00] Switching to Qwen and DeepSeek at one-tenth the cost [38:15] Distribution is the real piece [42:45] The surprise: enterprises run both Claude and ChatGPT [45:30] Where is China's real bottleneck in the AI stack? [47:15] China is not a monolithic organisation [48:30] Lithography and ASML remain the hardest component [50:45] The dome closes over the Chinese AI ecosystem [51:00] The bigger prize: default infrastructure for the Global South [53:00] Belt and Road, Central Asian rail, and long-horizon returns [55:45] Most of the world cannot afford frontier models [57:00] ClosingPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.

Fresh out of the studio, Ang Li, CEO and co-founder of Simular and previously a research scientist at Google DeepMind, joins us to explore why computer use is the last mile to AGI. Ang traces his path from studying catastrophic forgetting and continual learning at DeepMind to founding a company on a single conviction: models break in production because the data distribution never stops moving, and the only place to close that loop is the real world. He explains why the chatbot metaphor misleads enterprise buyers, why the right unit of measurement is tokens per task rather than price per token, and how the power law of practice should make an agent cheaper every time it repeats the same work. He separates capability from reliability through pass@k and pass^k, argues that deterministic work belongs in code rather than in a model, and makes the case that frontier labs exist to sell tokens while Simular exists to remove them. Last but not least, Ang shares his test for AGI — one you feel rather than see — and what it will take to bring autonomous work down to the cost of water."So when I go to the gym, [doing] the same workout everyday, as I do this more and more, more reps, more sets, I don't even need to think. It's called muscle memory. I just do the same in a standard way, my consumption or my tokens in my brain dramatically drops. And there's a term describing this behavior in human cognition called 'the Power Law of Practice'. So that's the whole idea. Do we see power law practice in AI agents? We don't have that yet."Profile: Ang Li, CEO and co-founder of Simular AI X: https://x.com/angli_aiLinkedIn: https://www.linkedin.com/in/angli-ai/Simular AI: https://simular.aiEpisode Highlights: [00:00] Quote of the Day by Ang Li from Simular[01:09] Why AGI arrives through the mouse and keyboard[02:27] Legacy systems with no APIs block automation[03:45] Computer use is the last mile[04:05] One device, a hundred agents in the cloud[05:51] The gap between research and production[07:45] The day Covid became a dominant search keyword[08:13] Continual learning over evolving distributions[09:21] Catastrophic forgetting remains unsolved[10:11] Why the learning loop requires a product[11:04] Why AGI must be built commercially, not academically[12:23] Chat versus work: two different problems[13:40] Every company needs a repeatable playbook[15:10] Tokens per task beats price per token[16:13] Same task 100 times, 100 times the tokens[16:34] The gym analogy and the power law of practice[18:18] Where the scaling frame breaks for computer use[20:46] Why AGI will not be a single model[21:43] The 72.6% peak versus repeated-run reliability[22:32] Hiring analogy: the interview versus daily work[25:22] Replaying trajectories to drive token cost down[26:17] The idea almost nobody in the industry discusses[29:01] Democratising autonomous computers to the cost of water[30:26] SMBs, not enterprises, are the real market[32:59] Why healthcare and insurance came inbound[34:06] Launching Sai on Windows virtual machines[34:38] Simulang and the unity of opposites[35:52] Frontier labs sell tokens; Simular removes them[38:01] The hidden cost of self-hosting open models[39:23] Why CPU and memory matter more than GPU[41:10] You only have two hands: the physical constraint[42:48] The AGI test you feel rather than see[44:19] Why humanoids take longer than software[45:23] The motivation: doing your work from the forest[47:03] What Great Look Likes for Simular AI[48:34] ClosingPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.

Fresh out of the studio, recorded in Singapore, Steve Clayton, Senior Vice President and Chief Communications Officer at Cisco, joins us to discuss what it takes for AI to scale inside enterprises globally. Steve argues that narrative belongs at the top of the AI pyramid, not the bottom — in an era when models generate more material than any organisation can absorb, the story a company can credibly tell becomes the scarce asset, not the technology itself. He explains why the truth lies in the field rather than at global headquarters, how regional dynamics reshape where a story lands, and walks through Cisco's advances in silicon and quantum computing networking. Showing a real customer deployment, he argues, is what earns permission to talk about security and trust at all. Last but not least, he shares what great look like for Cisco to build their story in the age of AI. "Take your phone and switch it into airplane mode and see how much you can do on your phone. Very little. You can use the calculator, but you can't use an AI model. You can't surf the web, you can't send messages. And people get excited about GPUs and data centers and models and applications and devices. But sitting at the center of all of that is the network. And that's what Cisco is all about. So that's our job, is to help people understand the value of the network and to realize that AI, as exciting as it is, it simply doesn't happen without the network." - Steve Clayton, SVP & Chief Communications Officer, CiscoProfile: Steve Clayton, SVP & Chief Communications Officer, Cisco LinkedIn: https://www.linkedin.com/in/stevecla/Friday Note Newsletter: https://www.linkedin.com/newsletters/the-friday-thing-7412856034161229824/Episode Highlights:[00:00] Quote of the Day by Steve Clayton from Cisco[00:30] Introduction: Steve Clayton from Cisco[02:13] Why storytelling began around the campfire[02:55] Telling stories about impact, not technology[03:43] Why the AI debate over-indexes on models[04:51] The airplane mode test for AI dependence[06:17] Steve inverts the pyramid, narrative on top[07:04] Communicating without leading with the technology[07:49] Breaking through by doing the opposite[08:37] Why "content" has become a bad word[09:59] Audience-first: meeting policymakers where they are[11:10] The trap of filling the pipeline[11:34] Attention, not creativity, is the scarce resource[12:15] Curation and judgment against AI slop[12:51] We are not entitled to people's attention[13:26] Silicon One, the Cisco business nobody expects[14:26] Cisco's universal quantum switch at room temperature[14:57] Balancing ambition against credibility[16:08] Show the possible, do not just describe it[16:35] Earning permission to talk security and trust[17:18] The truth lies in the field[18:16] McLaren F1 and Networking Academy stories[19:12] The question Steve wishes people would ask[20:56] The Friday Thing: human first draft, AI editor[21:26] Humans create the novel ideas, AI supports[22:07] What Cisco should mean to business and to family[23:21] ClosingPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.

Fresh out of the studio at the Dataiku Summit Singapore 2026, Nur Hafiza Mutalif, Assistant Head of International Affairs at the Singapore Red Cross, joins us to explore why the humanitarian sector distrusts AI, and what her team built once they worked through that scepticism. Coming to this from international law rather than data science, Nur Hafiza explains why the sector's caution is a feature rather than a lag: humanitarian work rests on doing no harm and on neutrality, and any solution must clear that bar before it clears a business case. Dataiku cleared that bar. Its pro bono experts taught the team how the models worked and let them set the boundaries — which is what earned their confidence. Hafiza walks through the disaster surveillance pipeline that freed four people from daily collation, and the Thailand leptospirosis model forecasting outbreaks from case and climate data. She argues AI should never be justified on efficiency, that data efficiency itself carries risk, and that acting on a forecast demands low-regret choices. Closing the conversation, she shares why capability outlasts any model."We cannot see efficiency, too, I think what I should see, as a humanitarian organization, is datasets and the different cases around the world be compressed into something that simplifies value and help us better serve communities." - Nur Hafiza MutalifProfile, Nur Hafiza Mutalif, Assistant Head of International Affairs, Singapore Red CrossLinkedIn: https://www.linkedin.com/in/nurhafizaab/Episode Highlights: [00:00] Quote of the Day by Nur Hafiza Mutalif from Singapore Red Cross[00:55] Three angles: distrust, what they built, where the line sits[01:45] From international law to leading an AI project[02:53] What made a cautious team comfortable with AI[03:24] Pro bono experts who did not impose solutions[04:04] Attention is a luxury for humanitarian organisations[05:13] "We curate, we don't create" — countering compassion fatigue[05:59] Do no harm, neutrality, and innovating with care[07:06] Why AI must not be treated as an efficiency tool[07:55] Disaster surveillance: four people, days reclaimed quarterly[09:09] Does AI only automate the boring work?[10:24] The Thailand leptospirosis outbreak prediction model[11:21] Getting humanitarians comfortable acting on a prediction[12:47] Where AI changes the disaster cycle most[13:44] What outsiders get wrong about data efficiency[14:38] The line AI must not cross at the Red Cross[15:40] Why internal capability beats the models themselves[16:33] Where an organisation with no data team starts[17:19] The question people ask, and the one they should[18:04] AI as a cross-cutting theme over five years[19:01] ClosingPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.

Fresh out of the studio, Sophie Dionnet, Senior Vice President of Product and Business Solutions at Dataiku, joins us at the Dataiku Summit in Singapore to discuss what turns enterprise AI investment into measurable value. She lays out the three ingredients Dataiku builds around — the right people, orchestration across technologies, and supporting controls — and makes the case that governance is a scaling mechanism rather than a brake. She points to Roche, where a patent lawyer encoded his own professional expertise into a working system of agents, discusses Dataiku's answer to agent sprawl with agent management launching in October, and closes on strong momentum across banking and the public sector in Asia Pacific."A lot of the changes that organizations need to do today actually don't require the latest model. That's not really the problem. It's about doing the hard thing, the change, the things that we talked about. It's easier to be excited by the new toy than by trying to use it. And so yes, I think this is why there is a bit of a gold rush of trying to figure out where is it going to end. We don't know." - Sophie DionnetProfile: Sophie Dionnet, Senior Vice President of Product and Business Solutions at DataikuLinkedIn: https://www.linkedin.com/in/sophie-dionnet-a176894/Episode Highlights [00:00] Quote of the Day by Sophie Dionnet from Dataiku[01:00] Three angles: domain knowledge, orchestration, governance[01:59] What has not changed: data still decides everything[02:31] Data consciousness accelerated over the past twelve months[03:05] The LLM explosion and the raw-power question[03:51] Why Sophie pushed governance before the market asked[05:30] What Dataiku is, and where the name comes from[06:15] Three ingredients: people, orchestration, controls[07:22] Roche: a patent lawyer builds his own agents[08:51] Change management, not technology, is the gap[09:41] Decision takes an hour, implementation takes two years[09:58] Why domain knowledge beats model performance[11:30] Most changes do not require the latest models[11:58] The scaling belief the industry gets wrong[12:50] Centralisation risk and the rise of shadow AI[14:02] Where leaders still quietly choose to do nothing[15:22] Vibe coding, conflicting outputs, and lost consensus[16:43] The GDPR lesson on ex-post compliance cost[18:42] Why the agent question starts at the board[19:38] Agents are simply a new kind of API[20:28] Is agent sprawl technology or organisational design[21:28] What separates AI scalers from pilot purgatory[22:58] The bear case: foundation labs absorb the middle[23:45] Why every leading technology becomes self-centred[24:57] Vibe coding your own Salesforce, and why not[25:23] The pet store analogy for build versus buy[26:22] Systems of record and the real switching cost[28:30] Dataiku in Asia Pacific over the next three years[29:59] ClosingPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.

Fresh out of the studio, Dr. Rafael Frankel, Head of Public Policy for Asia Pacific at Meta, joins us to unpack how Meta is stepping up on the fight against scam syndicates in Southeast Asia along with a coalition of partners. He explains why today's syndicates operate with state-like capabilities and forced labor at scale and how AI has become both the scammers' sword and Meta's shield, powering the removal of hundred million scam accounts in 2025. Frankel walks through the disruption-sprint model that took down the million accounts in a single week alongside the FBI, Microsoft and Starlink, and argues that beating scams demands a coalition matched to the criminal supply chain. Closing out, he reminds us that crime is never zero — but through sustained, collective pressure, it can be cut by orders of magnitude."I think that people underestimate the criminals that are the drivers of the scams. Mostly what we're talking about at this point is extremely well-financed, highly sophisticated transnational criminal networks that have turnover in the billions, tens of billions, and have the finances, the technological know-how, the sophistication to really be trying to scam people 24/7, 365 days a year. That's a tough adversary to be up against." - Dr Rafael FrankelProfile: Profile: Dr Rafael Frankel, Head of Asia-Pacific Public Policy at Meta LinkedIn: https://www.linkedin.com/in/rafael-frankel/Episode Highlights:[00:00] Quote of the Day by Rafael Frankel from Meta[02:38] From foreign correspondent to tech policy[03:39] Career lessons: gut instinct and taking risks[04:33] Why the Meta APAC policy role, and why now[07:26] The one thing people misunderstand about scams[09:13] What changed: industrial scale scam centers[11:35] AI as Meta's shield, deployed at scale[13:07] Genesis: the Bangkok 2024 convening[14:14] First disruption: 59,000 accounts, six warrants[15:45] June haul: 1.5M accounts, Microsoft, Starlink, 63 arrests[16:52] Matching the coalition to the criminal supply chain[18:52] Building trust: results create the feedback loop[20:05] The Global Signal Exchange and the FIRE program[22:50] Counter-intuitive lesson: Meta doesn't see everything[24:13] Asia Pacific as front line of the solution[26:20] Pulling on the same rope across governments[27:30] POGO centers and pinpointed police action[29:02] Is sanctioning a kingpin just theatre?[30:52] Crime is never zero, but reducible by orders of magnitude[31:02] The question Rafael wishes people asked: how can I help?[32:52] What success looks like three years out[35:08] ClosingPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.

Fresh out of the studio, Jing Yang, Asia Bureau Chief at The Information, joins us to explore how China is building frontier AI under chip constraints, state capital, and open source ambition. Jing breaks down her scoop on DeepSeek's $7.4 billion round at a $50 billion valuation, unpacking a deal structure in which the founder wrote two-fifths of the check and outside investors got no voting rights. She explains why Chinese AI valuations trail US labs, why seven to eight language model players refuse to consolidate, and why DeepSeek chose Huawei chips before Huawei knew about it. Last but not least, Jing shares the indicators she is watching from now to 2027."This is one of the things that really surprised me when I was working on this story: DeepSeek had spent a lot of time last year retrofitting their models and software with Huawei chips. I think a lot of people assumed it's because the government ordered DeepSeek to work with Huawei to embrace the domestic ecosystem, but it's actually the opposite. It was DeepSeek that voluntarily started using Huawei, experimenting with the Huawei chips, and Huawei actually only found out after. Then they started sending people to DeepSeek to help..." - Jing Yang, Asia Bureau Chief, The InformationEpisode Highlights:[00:00] Quote of the Day by Jing Yang from The Information[01:30] What changed since September: the ByteDance mystery resolved[03:00] Why China's AI must be read on its own terms[04:10] Breaking the story of DeepSeek on their 7.4B fundraise[05:40] How the valuation went from $10 to $50 billion[08:30] The lab that became famous for rejecting scaling laws[10:15] Unpacking the DeepSeek deal structure: four types of investors[12:40] Five-year lock-up and no secondary market trading[14:20] Reverse due diligence: DeepSeek vetting its own investors[16:40] Balancing open source, AGI research and IPO pressure[17:45] The irony: inclusive vision, exclusive deal structure[20:35] How Anthropic's Mythos preview changed Liang's mind[21:30] Why Chinese AI valuations look tiny next to US labs[23:00] Why Chinese founders go consumer when they go global[26:45] "The worst of customers" — price sensitivity and zero loyalty[27:45] The compute constraint that caps every Chinese lab[28:40] New labs in China: weaker infrastructure, harder exit[30:30] Can China leapfrog on hardware the way it did on models?[32:50] Stricter compliance, not political muscle[34:30] Founder power when your company becomes strategic[36:40] Junyang Lin's new lab and the scarcity premium[38:00] Open source influence versus sustainable commercial models[39:30] Zhipu versus MiniMax: how sentiment diverged after IPO[42:30] What is the right mental map for China's AI ecosystem?[43:20] The consolidation that still hasn't happened[45:00] Seven to eight serious players and nobody giving up[46:40] The one thing Jing wishes people would ask[47:20] Nobody ordered DeepSeek to use Huawei chips[50:00] Indicators to watch from now to 2027[55:30] ClosingProfile: Jing Yang, Asia Bureau Chief from The InformationThe Information Profile: https://www.theinformation.com/u/JingYangLinkedIn: https://www.linkedin.com/in/jing-yang-33548123/X: https://x.com/jingyanghkPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.

Fresh out of the studio, Aneesh Raman, Chief Economic Opportunity Officer at LinkedIn and co-author of Open to Work: How to Get Ahead in the Age of AI, joins us to dismantle the flattened narrative that AI is simply taking jobs. His counter-thesis: work is changing, not ending — and we never truly left the industrial age, only traded the factory floor for the office floor. Aneesh walks through his three-bucket framework for auditing your week, the move from the career ladder to the career wall, and why the org chart is giving way to the work chart. He closes by reframing the current AI moment as a battle of belief, urging leaders and workers to move from anxiety to agency and bet on themselves. "We are in a battle of belief right now more than anything else. So stories matter a lot to humans. It's not just the tools we create, it's the stories we tell that have allowed us to become everything we've become. But whether it leads to better or worse... depends on the story we tell now. If we tell ourselves a story that it's going to lead to worse, it's more likely going to lead to worse. Because we have to unwind a lot of what the industrial age has told us to think about ourselves. A lot of industrial age work was about deficit management. I don't have the degree yet, I need to get it. I don't have the job title yet, I need to get it. We all started from a place of what we don't have, that we needed to get, in order to feel of value and succeed. This world..." - Aneesh RamanEpisode Highlights:[00:00] Quote of the Day by Aneesh Raman[01:55] Aneesh's origin story[04:00] The labour market: least efficient, transparent, dynamic market ever built[05:45] Career advice: worry about what you can control[07:19] Open to Work and the misaligned labour market[09:00] We never left the industrial age[10:36] Jobs are tasks, not titles — the software engineer example[11:50] The three buckets: automated, augmented, uniquely human[13:34] Gen Z: highest AI confidence, entrepreneurial by survival[15:49] The real gap: mid- and late-career professionals[16:13] Using the tool while rejecting the fatalism[18:06] Auditing your week: list twelve tasks, sort three buckets[20:22] Is there a cognitive ceiling? Bucket two for everyone[21:00] Human capability is a fraction; it's human with AI[23:34] Why the org chart emerged from the industrial age[25:30] Work-chart principles: capabilities, not categories[26:30] Managers as coaches and the rise of the super IC[27:37] Asia Pacific: a responsible AI-era workforce strategy[31:00] Anxiety to agency: we are in a battle of belief[32:10] Bet on yourself: from deficit management to asset inventory[34:07] How long is the window? Five years of experimentation[36:04] The question no one asks: what's possible?[36:23] The innovation explosion and the Lost Einsteins[38:02] Fifty years to outdo the last five hundred[38:17] What this all means for LinkedIn[40:30] RecommendationsProfile: Aneesh Raman, Chief Economic Opportunity Officer, LinkedIn and Co-Author of "Open to Work" LinkedIn: https://www.linkedin.com/in/aneeshraman/Open To Work: https://www.linkedin.com/opentowork/Podcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format. Here are the links to watch or listen to our podcast.Analyse Podcast Main Site: https://analysepodcast.com

Fresh out of the studio, Benedict Evans, independent technology analyst and author of AI Eats the World, returns to explore whether the AI model layer is becoming commodity infrastructure. Benedict argues there is no winner-takes-all effect in models yet, drawing parallels to telecoms, cloud, chips and the fiber bubble to ask where durable value actually accrues when everyone runs similar infrastructure on similar tokens. He unpacks why the chatbot remains a poor interface, introduces the "blank screen" and "jagged frontier" problems that keep software companies alive, and explains why large language models inherently give you "the average." Closing the conversation, Benedict reflects on the indicators that would show AI has truly eaten the world — and why the answer is better products, not better models."When you automate away work, you can always see the jobs that are going away because they're right there. And you don't know what the new jobs are going to be. Human needs are infinite. How many people are earning a living from making podcasts now? Imagine predicting that 10 years ago. There's a stage in the evolution of the market where like if you're still arguing about that, you're an idiot. But there's a stage at the beginning where you might have opinions about some of these questions, you're probably not even asking the right questions. That, I think, is where we are with this stuff today." — Benedict EvansEpisode Highlights: [00:00] Quote of the Day by Benedict Evans from AI Eats the World[01:16] The public market test: what are investors buying?[04:21] How far up the stack can models go?[05:30] Models can't build all the apps themselves[06:00] The thesis: models as commodity infrastructure[07:52] "All the value went up the stack"[08:24] Chips and Rock's Law: down to three players[11:23] The 1999 reseller story: one-time sales[13:28] The S-curve framing of technology[16:38] You're probably not asking the right questions on AI[18:02] "If this works, we're competing with a Mac"[20:25] Incumbents make it a feature[22:14] Big tech "killing startups" is overstated[24:39] Cowork as the new spreadsheet[26:01] The blank-screen and jagged-frontier problems[29:00] The hard part isn't writing the code[31:25] "What a good answer would probably look like"[33:38] The job displacement debate[37:38] Jevons paradox and the lump-of-labour fallacy[40:30] LLMs inherently give you the average[42:36] Why you really hire McKinsey[45:33] Punk versus prog rock: outside the training data[49:00] Automating ever-higher human functions[49:55] Why this is unanswerable: no theory of scaling[51:30] Indicators that AI has eaten the world[54:53] The solution isn't a better model[56:39] Where to find Benedict EvansProfile: Benedict Evans, Independent Technology AnalystLinkedIn: https://www.linkedin.com/in/benedictevans/Website: https://www.ben-evans.com/newsletterPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.Here are the links to watch or listen to our podcast.Analyse Podcast Main Site: https://analysepodcast.comAnalyse Podcast Spotify: https://open.spotify.com/show/1kkRwzRZa4JCICr2vm0vGl Analyse Podcast Apple Podcasts: https://podcasts.apple.com/us/podcast/analyse-asia-with-bernard-leong/id914868245 Analyse Podcast LinkedIn: https://www.linkedin.com/company/analyse-podcast/Sign Up for Our This Week in Asia Newsletter: https://www.analysepodcast.com/#/portal/signup Subscribe Newsletter on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7149559878934540288

Fresh out of the studio, Eyck Freymann, Hoover fellow at Stanford and author of Defending Taiwan: A Strategy to Prevent War with China, joins us to explore why the Taiwan question will be decided by economics and coercion, not by invasion. Eyck unpacks the Thucydides Trap as a warning, not a prophecy, traces how Xi Jinping's Belt and Road statecraft shapes his approach to Taiwan, and contrasts a kinetic invasion with the "quarantine" scenario he fears most. He reframes 2027 as a capability milestone, recasts TSMC as a "silicon magnet" binding America to Taiwan, and flags Taiwan's 2028 election as the real flashpoint. Last but not least, Eyck argues the real task is to deter the crisis, not the war."For Beijing, I hope they will say: the United States actually does have a strategy to use every element of its national power to preserve peace and stability without provoking us, and we should not assume the United States is incapable of an effective response. In Taiwan, I think the lesson is: the United States trusts the people of Taiwan to choose the best future for themselves, and ultimately Taiwan's fate is up to the people of Taiwan to choose. That is the heart of what the American One China policy is about and must be about. The people of Taiwan must choose, and the United States will respect their choices. That is a profound insight that doesn't get said often enough." - Eyck FreymannEpisode Highlights:[00:00] Quote of the Day by Eyck Freymann from the Hoover Institution at Stanford[01:18] Eyck's origin story[04:02] When Taiwan deterrence pulled the threads together[06:33] Why the CCP embraces the Thucydides Trap[07:36] Belt and Road as decentralized statecraft[10:18] How Belt and Road consolidated Xi's power[11:39] Xi's legacy project: why Taiwan comes next[12:17] What gets lost without untranslated Chinese sources[14:12] China's unexplained nuclear breakout[16:23] Applied history: lessons from three mentors[19:50] Reframing the timeline: 2027 vs 2049[22:49] Declassifying the Davidson window[24:27] Is 2049 bound by Xi's resolution?[27:34] Cross-strait history and the counterintuitive lesson[29:28] Two scenarios: kinetic invasion vs customs quarantine[34:00] The TSMC financial-shock trigger[36:48] Strategic ambiguity vs structured ambiguity[42:39] The one thing few understand: it's all economic[44:39] The right and wrong asks of Southeast Asian neutrals[47:17] The silicon shield paradox and chip onshoring[50:19] Why the CHIPS Act won't replace Hsinchu[53:49] The January 2028 Taiwan election as a flashpoint[55:24] Meta-question: the neglected domestic politics of Taiwan[58:07] What success looks like for the book[60:14] ClosingProfile: Eyck Freymann, author of "Defending Taiwan" and Hoover FellowLinkedIn: https://www.linkedin.com/in/eyck-freymann/Personal Site: https://www.eyckfreymann.com/Podcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format. /Here are the links to watch or listen to our podcast.Analyse Podcast Main Site: https://analysepodcast.comAnalyse Podcast Spotify: https://open.spotify.com/show/1kkRwzRZa4JCICr2vm0vGl Analyse Podcast Apple Podcasts: https://podcasts.apple.com/us/podcast/analyse-asia-with-bernard-leong/id914868245 Analyse Podcast LinkedIn: https://www.linkedin.com/company/analyse-podcast/Sign Up for Our This Week in Asia Newsletter: https://www.analysepodcast.com/#/portal/signup Subscribe Newsletter on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7149559878934540288