Entrepreneurs
3 Curated Dialogues
林凡
Key Views & Dialogues
86. “Next year, tech companies will hire AI talent—and only AI talent!” Talking with Maimai CEO 林凡 about global AI talent trends, organizational change, and large-model quarterly updates
林凡 expects tech companies to hire essentially only AI talent next year, as Maimai’s AI roles reach 30%–40% and major-campus technical hiring narrows to three categories. OpenAI expanded Codex companywide in 9 months and Maimai expects 10–11 months; the catalyst is rapid workplace automation, while demand-side confidence and commercial proof remain unresolved.
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- 林凡’s most provocative claim is that next year, tech companies will essentially hire AI talent only—people who cannot use AI will be as unemployable as people today who cannot use PCs or smartphones. The basis for the claim is that OpenAI took 9 months to move from programmers using Codex to companywide adoption, with “legal, finance—everyone using it, and asking Codex to work for an average of one hour.” Maimai started about 5 months later than OpenAI and expects to complete the transition in 10–11 months. Maimai’s internal data shows AI talent growing 10x year on year from 2025, with the same growth rate continuing in 2026 and AI roles now accounting for 30%–40% of the total; technical campus hiring at major tech companies has converged on three categories: AI algorithms, AI full-stack, and AI Agent.
- The current US-China model gap is narrowing, while expensive tokens are a phase rather than a permanent condition. As the competitive center shifts from compute-intensive pre-training to RL and Agentic RL, resource requirements are lower than in pre-training and domestic and overseas SOTA models are “running close.” Distillation is also making small models increasingly capable—DeepSeek Flash was particularly impressive. Lin Fan expects premium SOTA tokens to remain expensive for another 6–12 months; once ordinary models reach the level of today’s Opus 5 or Fable 5, and demand settles at that level, tokens may become much cheaper. He explicitly did not promise that the gap will continue to narrow in the future.
- Lin Fan says RSI—AI self-improvement—is already taking hold: Anthropic and OpenAI “should” already be having AI optimize roughly 30% of their training work, with both expected to reach 70%–80% within a year. He sees this as the core reason model capabilities are improving so quickly. OpenAI was still targeting a top IOI finish last year—it came seventh—but this year it “doesn’t care” about IOI anymore; its central goal is to win the Fields Medal in mathematics within a year. Lin Fan also said he believes this generation may be the last Fields Medal era in which everyone can participate in the competition.
- Agents are moving from waiting for people to assign work to actively taking work away from them: inside Anthropic, Claude has the relevant context in Slack and will proactively say, “I can do this.” Lin Fan estimates that 70%–80% of human work is routine; as those tasks are absorbed, people will spend more time on non-routine work and remain outside their comfort zones. He has compressed his earlier forecast—“everyone a programmer in 3 years, a manager in 5, and a CEO in 10”—to 1 year, 3 years, and 5 years, acknowledging that he had “underestimated the speed.”
- AI talent falls into three categories, but the broadest dividing line is whether someone can make an Agent work autonomously for more than an hour. Base-model technical talent is judged by papers and open-source contributions; application developers by real Agent deployments; and business-productivity talent by whether they can remove themselves as the bottleneck in the loop. “The clearer your constraints and the better your initial architecture, the longer AI can autonomously complete the details.” Lin Fan says he once used architecture design and more than 200 test cases to have AI run continuously for 10 hours and execute more than 700 background tasks.
- The old rules of replacement have been overturned: the key variable is not how difficult a job is, but whether it has a clear, verifiable closed loop—mathematicians may be exposed while relationship-driven roles remain relatively safe. The consensus in a 500-person AI group at Tsinghua’s computer science department is that “AI intelligence has already surpassed ours”; what remains is to release more context. Programmers will not disappear, but will move outward carrying coding agents—“one person is a team”—allowing industries such as logistics, agriculture, and foreign trade, which previously could not afford a programming team, to use one for the first time.
- On the macroeconomic consequences, Lin Fan was explicit that he is “not as optimistic as you are”: AI will sharply raise productivity, but the problem is on the demand side, because more supply does not automatically create more consumption and the core issue is confidence. He predicts that 20 years from now “everyone will be born retired,” but says the decade before that will be “extremely turbulent and extremely panicked.” The transition will involve human resources moving “to the cloud”: hourly and daily employment, with an AI management platform assigning work centrally. Maimai’s envisioned end state is for the platform itself to employ candidates and directly match labor value—same customers, radically different methods.
Vol.200 Exclusive Interview with 林凡 of Maimai: What New Changes Can AI Bring to Social Networking and Recruitment
AI may move Chinese To B from process value to outcome delivery, with 林凡 pointing to services paid through performance sharing rather than fixed software fees. Maimai’s AI can screen 5,000 résumés in 3 minutes, but accuracy may fall from roughly 85% in one round to 20-30% after 10 rounds. Maimai has been profitable for 2 consecutive years yet may raise capital for a major opportunity, while its data advantage should matter for at least 1 to 1.5 years.
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- China never produced a LinkedIn because of supply and demand, not product shortcomings. During the decade of mobile internet, jobs were so easy to find that “Why would a boss spend time talking to a candidate? I only do it when candidates are hard to hire,” which gave Boss Zhipin’s rapid matching model the edge; now that a single opening can attract 1,000 applicants, the value of relationship-based endorsement is returning, with Maimai restarting professional social networking—the constraint is still the inescapable WeChat ecosystem, while the bet is on a generational shift summed up by “Gen Z saying, ‘Why can’t you add me on Maimai?’”
- 林凡’s read on the era: the internet only changed production relations; AI is a productivity revolution, and To B is China’s biggest hope for the next decade. In To C, “it’s all about foundation models” (Perplexity is struggling, and Grok has taken a large share of this kind of business), while AI lets Chinese To B companies move from “providing process value” to delivering outcomes: “If you improve performance by 10%, give me 5% of the upside—that’s easy to negotiate. If you don’t deliver, I don’t pay.”
- The restructuring of recruitment is already quantifiable: AI can handle 70%, even 90%, of a headhunter’s work. Every “Beike of headhunting” failed because it was a production-relations solution that had to wait for a productivity revolution; within five years, standardized roles will be filled “with one click,” payment will come only after the hire starts, recruitment and job-seeking will be “as simple as hailing a ride,” and “human resources in the cloud” will turn labor costs into variable costs.
- The cleanest technical definition is that AI has an extremely high ceiling and an extremely low floor. It won a gold medal at the IOI, ranking seventh globally, yet “you can tell it to fix a small bug until you’re blue in the face and it still won’t fix it”; accuracy of 85% in a single round can fall to 20-30% after 10 rounds. “The essence of AI to B is raising AI’s floor high enough”—high enough to hire AI employees directly.
- The AI talent market is the year’s most important talent-market event. The share of newly posted jobs containing AI keywords rose from 12% in 2024 to nearly 19% in January-October 2025; after the DeepSeek moment, September job volume was up 11x year on year. In Silicon Valley, discussing AI “entirely in Chinese is not theatrical at all”; ethnic Chinese make up more than half of the technical core, and talent flows run “mostly from China recruiting the US.”
- The China-US innovation gap is a multiplication of probabilities. Chinese founders pursue high-ROI projects and are risk-averse; Americans are willing to bet on projects with a 5-10% chance of success: “The US may have 100 people willing to do it; China may have only 10.” China is extremely strong in 80% of fields and moves faster in vertical applications; the other 20%—long-cycle, high-failure-rate innovation—belongs to the US. His judgment of 梁文锋 is unsparing: “That’s a game of luck. It’s hard to expect him to produce a big move on schedule.”
- AI-native organizations make a three-stage jump: everyone becomes a programmer, then everyone becomes a manager, then everyone becomes a CEO. AI handles 20-30% of work in the first stage, 60-70% in the second, and 95% in the third, with one-person companies everywhere; the end state is “born retired.” Maimai has already shifted 20-30% of its workload to AI (employees have been fired since 2023 if they do not use Copilot), while 林凡 himself spends 50-60% of his working time discussing problems with AI, including management’s “brutal truths” and verbatim communication scripts.
- Maimai has been profitable for 2 consecutive years and does not need external funding, but “when there is a truly big opportunity today, you need to make a big investment,” putting fundraising back at the top of the agenda. Its moat over startups is data—“it should matter for at least a year to a year and a half”—and “I have never worried about startups.” He also knows luck matters: Boss Zhipin becoming so large exceeded his expectations; ultimately, it was “desire happening to match social trends” plus luck.
133: Interns Paid RMB4,000 a Day and Programmers Turned into Gig Workers: Maima CEO Lin Fan Takes Stock of the 2025 AI Talent Market | Cross-appearance on 「职」无不言
Meta’s $10M AI pay packages can be justified by high operating leverage in compute costs and advertising revenue. China’s major tech companies are redirecting spending toward AI hiring and incentives, while shrinking junior roles and the prospect of labor moving to the cloud remain key risks to monitor.
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- Meta’s $10M annual packages for top AI talent make sense first and foremost on an ROI basis. A company spending $10B-plus a year on compute could save $1B if someone cut costs by 10%; a 0.1% improvement in Meta’s nearly $200B advertising revenue would add $200M. Against that backdrop, a $10M salary is hardly excessive. DeepSeek was cited as an example of dramatically lower costs, but Lin Fan then used the more conservative 10% savings case to make the same point.
- More important than ROI, Silicon Valley’s core teams still believe AGI could arrive in roughly 3 years—and see it as a matter of competition and survival. Lin Fan did not expect that before his trip, but conversations with core personnel at OpenAI and Google left him “80% convinced”: reinforcement learning has produced discontinuous gains in technical tasks such as coding; models can use “high-level judgment but low-level execution” to provide feedback for other models; and human-AI interaction continues to generate new, filterable data.
- China’s major tech companies have shifted from layoffs and cost-cutting to “spend more to become more efficient”—deciding where and on whom to spend in order to amplify productivity. According to the report, AI job postings have grown by the high teens to high 20s month over month since February, roughly 10,000 additional AI workers each month have marked themselves “open to opportunities,” and about 40% of employees at the six leading AI startups have done so since July. AI hiring at major tech companies is generally up more than 10x year on year. High-performer ratios, internal mobility within AI divisions and stock-vesting schedules are all being redesigned; the talent war now extends beyond pay to organizational structure and incentives.
- AI’s near-term impact on employment is not the disappearance of entire professions, but the contraction of junior roles and a higher capability bar—and Lin Fan believes China may lag the US by 1–2 years. Silicon Valley startups that once needed 8–10 programmers may now operate with 1–2; “1 core programmer plus AI” is roughly equivalent to the old 5-person team. In China, hiring for embedded, Python, C++, data development, frontend, testing, Android and iOS roles is down about 20%–30% year on year, while executive hiring is down roughly one-quarter. Lin calls the current state a short-term equilibrium: “No new batch of jobs has appeared out of thin air, and no entire job category has been replaced.”
- Scarcity pricing for talent has spread to papers, open-source code and interns. The compensation ceiling for elite AI talent is roughly $5M–$10M a year in the US and RMB5M–RMB10M in China; beyond that, “salary stops meaning anything.” A small number of top interns can earn RMB4,000–RMB5,000 a day, and interns now account for the high teens of AI hiring. AI talent is about 20% bachelor’s, 54% master’s and 25% PhD; PhDs even outnumber bachelor’s graduates. Citation counts and GitHub downloads have also become rough pricing signals.
- Lin Fan sees the next structural shift in work not as ordinary remote work, but as “labor moving to the cloud” after machines moved to the cloud. Over the next 2–3 years, hiring will shift from testing whether someone can work independently to whether “a person and AI can deliver results together.” Within 5–10 years, decomposable sales, operations, R&D and accounting tasks could be assigned through cloud-based human-machine platforms, turning white-collar work into gigs. Income may remain broadly flat—and rise for the most driven—but organizational belonging and career fulfillment will weaken; the top 5% or 10% may remain in traditional large companies.
- The most urgent personal strategy is to turn AI from an occasional tool into a work habit used at least 10 times a day. Lin’s passing grade is blunt: “If you only use it 10 times a week, you need to build the habit immediately.” The longer-term defenses are complex problems, cross-functional collaboration and experience that has not yet been digitized. He says 95% of people may eventually not need to work; the host summarized that endpoint as “retirement from birth.” Hobbies therefore matter—not as small talk, but as a source of meaning once work no longer supplies identity.