Summary: This post breaks down Fidji Simo’s plan to make ChatGPT far more useful—and to get users to pay for that usefulness. I’ll explain the split leadership at OpenAI, Simo’s remit and tactics, the product moves already under way (Pulse, jobs, mental-health work, Sora), the compute and privacy constraints that shape decisions, and the ethical trade-offs between ads, commerce, and user safety. I’ll close with practical questions for product leaders, business people, and policy makers to consider as they weigh access, value, and risk.
Interrupt and engage — quick read, then a slow think
Stop skimming. Read this as both a market brief and a checklist. Ask yourself: what would you pay for an AI that acts like your personal shopper, travel agent, financial adviser, and health coach rolled into one? What can you afford to give up for that convenience? Those two questions frame Simo’s work at OpenAI: making high-value helpers and finding a business model that scales without destroying trust.
Why the split leadership matters
OpenAI now runs on a dual-leader model: Sam Altman runs research and compute; Fidji Simo runs Applications—the consumer and revenue side. That separation isn’t cosmetic. It sets two priorities in clear relief: push model capability fast (Altman) and turn capability into widely used products (Simo). The company needs both. Simo says the job is to close this gap between capability and adoption. Close this gap between capability and adoption—repeat that. That line is the compass for every product decision she makes.
Who is Fidji Simo — credibility and constraints
Simo is a product executive who scaled large consumer apps at Meta and took Instacart public. That track record gives her authority: she knows distribution, monetization, and the messy reality of consumer trust. She’s also running Applications remotely from Los Angeles because of POTS—a chronic condition that makes standing risky. Yet she’s visible: available from 8 AM to midnight, responding in minutes on Slack, showing that remote leadership can still be operationally intense and culturally present. That visibility matters: authority plus accessibility builds internal momentum and external trust.
Strategy: Fight for less scope, not more scope
Her stated focus is surprising in its discipline: “battle for less scope rather than more.” In a company that can build anything, the hard task is prioritizing. The product play is straightforward: pick a few high-value helper experiences, make them much better than current options, and then charge for them. She believes people will pay substantial sums if the helpers truly save time, money, or risk. Here’s the implied bet: people will trade money for reliable, private, high-trust assistance. Do you agree? How much would you pay for a helper that reliably reduced your friction on the tasks that cost you the most time or stress?
Products launched and the “close the gap” examples
Simo has shipped several focused initiatives that illustrate the approach:
- Pulse — connects to your calendar, your chat, and your feedback to surface timely briefings and health signals. It’s a utility aimed at both work and personal needs. She uses it to spot new research on her condition and to stay on top of AI news.
- Jobs and certification platform — trains people, certifies AI skills, and links them to roles. It’s both a social-good play and a demand-generation mechanism for enterprise services.
- Mental-health improvements — targeted work to reduce harmful responses and to add parental controls and age protections. This is safety plus product-market fit for sensitive use cases.
- Sora — a new video application with parental controls and protections for likeness use. Launched early, refined with feedback. Think of it as a medium that will evolve beyond replication to unique creative formats.
Monetization: make it must-have, then charge
Simo’s commercial thesis is clear: make the helpers indispensable, then price them. She imagines giving each person access to a team of helpers—shopping, travel, finance, health. When that helper saves time, reduces risk, or increases income, people will pay. She’s candid about OpenAI’s losses: the company is still burning cash at scale. The remedy is not merely raising prices—it’s creating services with clear ROI. Will users pay for a better set of recommendations? Will enterprises build on OpenAI’s platform to create industry-specific agents? Those are the revenue levers she’s pulling.
Compute deals: why OpenAI pushed hard
OpenAI has locked in enormous compute capacity—deals running into the hundreds of billions, according to reporting. Simo argues these deals are necessary: product pipelines require heavy compute, and product goals are constrained without it. She points to Pulse as one of at least ten examples that need massive compute to scale. The trade-off is concentration: big deals can centralize power and raise competition concerns. How should a company balance securing capacity and preventing an unhealthy concentration of economic power? If you were designing policy, how would you answer that?
Ads and commerce: cautious, but candid
Advertising is on the table because ChatGPT already sees commerce intent—people asking for shopping advice, product comparisons, travel planning. Simo’s stance is measured: ads only work if commerce intent is real and the recommender experience is excellent. She also repeats a lesson: objections to ads usually come from how data is used, not ads themselves. She promises privacy-forward design as a condition. That’s a commitment to the long game: don’t monetize by default; monetize after the experience proves its value and privacy guardrails are in place. Does that pass your smell test?
Data privacy and the advertiser temptation
OpenAI holds sensitive signals: calendars, chat histories, personal concerns. That data is valuable to advertisers. Simo says the company will be “extremely respectful” of privacy before it moves on ads. That’s responsible rhetoric. Yet words meet incentives. The tension is structural: when you have a product with 800 million weekly users, the temptation—and the commercial case—for targeted revenue is strong. What guardrails would you insist on to prevent mission drift? Repeat after me: privacy rules must be productized, not just promised.
Mental health: practical detection and tough trade-offs
OpenAI has reduced negative mental-health responses through model changes and policy. A tough example: mania detection. Clinical signals—no sleep for two days and feeling unstoppable—can look like positive energy to a model. Simo stresses collaboration with psychologists to catch subtle signals and intervene appropriately. But interventions risk false positives that harm autonomy. This is where value of No matters: saying “No, we won’t claim clinical diagnosis” is a boundary; saying “No, we can’t ignore cries for help” is another. Which No do you prioritize? Where do you draw the line between helpful intervention and overreach?
Sora and early-medium criticism
Critics call some early AI-generated video “slop.” Simo compares this to early cinema—new media often starts by imitating old forms before finding its own grammar. She highlights that some users find real value—even entertainment—in early outputs, and some creators gain from increased reach. Copyright holders reportedly see both promise and need for clear value exchange. The practical approach: iterate quickly, add parental controls and IP controls, gather real-world usage signals, and then raise the quality bar. What counts as acceptable experimental output in your view? Who decides?
Jobs: disruption and a mitigation plan
Simo expects job creation alongside disruption. Yet some roles will be deeply altered. Her response: train 10 million workers via certifications and connect them to jobs through a marketplace. That’s both public-minded and self-reinforcing: certifying talent grows the ecosystem and creates demand for enterprise features. It’s a practical application of reciprocity: give people skills, and they provide labor for the new market. Is this enough? It’s a start, but it needs public partners and employer commitments to scale.
Human advantage: creativity, speed, and children as case study
Simo rejects doomsday framing. Her bet: humans are creative and will use AI as superpowers. She points to her daughter using AI to build businesses and creative projects. That anecdote supports a broader claim: lowering the floor for creation increases participation. But that also raises inequality questions: who gets early access to effective helpers, and who is left behind? What policies ensure broad access to these productivity multipliers?
Leadership limits: why she won’t replace Altman
When asked if she’d become CEO of the entire company, Simo says no. She sees Sam Altman’s role as distinct and vital. She also says there’s more than a decade of work inside her Applications remit. That’s a useful display of commitment and consistency: pick a mission, do it well, and resist vanity moves that dilute focus.
Ethics, trust, and the business model test
At the core, Simo faces three linked tests:
- Can you make helpers that people truly need—helpers that save time, reduce risk, or increase income?
- Can you monetize those helpers without eroding trust or privacy?
- Can you scale ethically under massive compute constraints and concentrated vendor relationships?
If the answer to each is yes, OpenAI has a play. If not, the company risks being a high-cost utility that users love but won’t pay for at scale.
Practical steps for product leaders and policy makers
If you build or regulate these systems, consider these moves:
- Design privacy as a product feature. Make opt-in monetization explicit, not hidden. People say no to surprise data use; honor that No and watch trust rise.
- Price on delivered value, not attention. Test paid tiers that reduce friction in high-ROI tasks (travel bookings, tax prep, medical triage summaries).
- Create third-party certification for mental-health interventions so responsibility is shared across vendors and clinicians.
- Push compute transparency: publish aggregate capacity use and third-party audits to reduce concentration fears.
- Support worker transition programs that combine certification with employer commitments to hire certified talent.
Questions to open a conversation
I want to hear your take. Which helper would you pay for today? Which privacy trade-offs would you accept—and which No would you refuse? How should compute be governed to prevent undue concentration while allowing rapid product-scale experiments? Tell me: which of Simo’s moves feels right, and which worries you?
Repeat: close this gap between capability and adoption. Close this gap between capability and adoption. Say No when a design violates trust, and say Yes when a paid helper delivers measurable value. Pause. Think. Then answer.
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