# Andrew Ng

Andrew Ng works on AI education and software development, sharing practical views on AI skills, coding agents, and how AI is changing engineering teams. Ng's work also includes AI courses, a developer conference, and the open-source OpenWorker agent.

- Profile: https://b2binfluencer.marketing/creators/andrew-ng/
- LinkedIn: https://www.linkedin.com/in/andrewyng
- LinkedIn followers: 2,661,418
- Topics: artificial-intelligence, cybersecurity, product-management
- Based on 12 dated posts, latest 2026-09-21

## Summary

Useful for explaining how AI changes software teams, skills, hiring, and security.

- Best for: A B2B AI workforce-training company or expert-led webinar on AI-native engineering teams.
- Not for: Direct-to-consumer athletic apparel or short-form video lifestyle campaigns.
- Check first: Confirm commercial terms, audience composition, and comparable results before committing budget.

### Takeaways

- Building software is getting fast, so deciding what to build is now the bottleneck.
- AI is changing who gets hired more than it is eliminating jobs.
- Open-source, auditable agents give defenders the same leverage attackers already have.

## Main ideas

- **Building software is getting fast, so deciding what to build is now the bottleneck**: Andrew Ng argues that when coding agents accelerate building, the constraint shifts from writing code to choosing what to build. To adapt, some teams push engineer-to-product-manager ratios down from roughly 8:1 toward 1:1, but pairing one product manager with one engineer can replace one bottleneck with a communication choke point. The fastest teams they see have engineers who can do some product work and, optionally, product managers who can build. The same logic spreads outward: when coding speeds up ten or a hundred times, design, marketing and legal review become the slow parts, so generalists who span several specialties gain value in small teams.
- **AI is changing who gets hired more than it is eliminating jobs**: Andrew Ng argues that fears of AI-caused job loss have so far been overblown. In this account, recent tech layoffs mostly correct pandemic-era overhiring or reflect general cost-cutting, and few roles have actually been automated, partly because AI does not work well enough yet. The exceptions named are call-centre operators, translators and voice actors, who are described as likely to struggle or see pay fall. The pattern highlighted instead is replacement by adaptation: employers part with staff who do not adopt AI tools and hire people who do. AI-native teams are sometimes smaller than the ones they replace, while many businesses have more work than people.
- **AI-safety hype is a setback, and cybersecurity is the part worth taking seriously**: Andrew Ng writes that fear about AI dangers has been amplified by what appears to be a well-orchestrated publicity campaign, calling it a setback for the field. They see no increase in human-extinction risk, describing those theories as fantastical science-fiction scenarios, and identify cybersecurity capability as the change that deserves attention. On the incident where an OpenAI team's agents broke into Hugging Face, they separate the technical fact from the press framing, attribute the breach to buggy sandboxing and monitoring rather than a new class of threat, and argue those are the right things to fix instead of pausing AI. In this account, the long-term advantage lies with defenders, because they hold more information about the bugs they need to close, while agents mainly bring relentlessness and patience in chaining vulnerabilities.
- **Open-source, auditable agents give defenders the same leverage attackers already have**: Andrew Ng presents OpenWorker, an open-source agent that completes tasks on a laptop rather than only chatting, and frames the goal as giving security teams the leverage attackers already get from AI. Running an agent takes two pieces: a model and a harness. Because the OpenWorker harness is fully open source, security teams can audit it for backdoors that might exfiltrate code or data. Model choice is open: users can run open-weight models locally so sensitive code never leaves the machine, use a ChatGPT subscription, or connect any model by API key.
- **Five settled trends, plus an open-question list as the working agenda**: Announcing the AI Developer Conference in San Francisco under the theme 'The Future of Software Engineering,' Andrew Ng lays out five developments treated as already visible: more people will write code, writing and reading raw syntax will matter less, custom applications will proliferate because software for small audiences is now economical, deciding what to build is the bottleneck, and paying down technical debt is getting cheaper. They then list what remains open, including what senior and junior engineers should learn, where competitive advantage comes from when everyone can build features, how coding agents should be organized, what a software team should look like, and how agents change machine-learning and data-science work. Publishing the questions as an explicit list doubles as an event agenda.

## Work to read

- [AI-native engineering teams and the product-management bottleneck](https://www.linkedin.com/posts/andrewyng_meta-pivots-from-open-weights-big-pharma-activity-7454559322900123648-zdsF)
- [The five trends and open questions shaping software engineering](https://www.linkedin.com/posts/andrewyng_ai-dev-26-x-sf-ai-developer-conference-activity-7449507592210391040-CZBu)
- [Where AI is actually moving jobs, and which skills are paying off](https://www.linkedin.com/posts/andrewyng_openclaw-runs-amok-kimis-open-model-ministral-activity-7427025592774062080-m17n)
- [Why Andrew Ng thinks AI-risk hype is a setback for the field](https://www.linkedin.com/posts/andrewyng_metas-agent-security-the-navier-stokes-activity-7507906165196984320-99sj)
- [OpenWorker, an open-source agent built for security work](https://www.linkedin.com/posts/andrewyng_github-andrewyngopenworker-activity-7498080864258617344-Xv9b)

## Brand relationships

- **DeepLearning.AI** (owned_business)
- **OpenWorker** (owned_business)

## Partnerships

Andrew Ng's posts do not describe a sponsorship or paid-partnership offering. Across five LinkedIn posts, the closest commercial signals are the AI Developer Conference (AI Dev, April 28–29 in San Francisco), which invites readers to attend and hear about the future of software engineering, and the open-source OpenWorker agent, which credits collaborator Rohit Prasad and points readers to a project site and code repository. Neither post describes a purchasable sponsorship slot, rate card, media kit, advertiser audience demographics, or bookable terms. Before budgeting for a partnership, ask directly about pricing, deliverables, exclusivity, and lead times; the posts don't provide them.

## Alternatives

- Kashif Aziz: Choose Kashif Aziz when the partner's training offer is about workforce change at large — freelancing, remote work, and how AI shifts what knowledge workers are paid for — rather than engineering team design. Aziz's two posts tackle those questions directly. One closely reads a Pakistan Software Houses Association budget proposal that would split freelancers from full-time remote employees for tax purposes and raise rates on the second group from 5% to 20%. It argues that penalising independent earners pushes money offshore and speeds up emigration. The other walks through OpenAI's GDPval benchmark of 1,320 tasks across 44 occupations and predicts AI running multi-step workflows by 2026 and industries reorganised around AI by 2027. A webinar or guide on AI and the future of work, global remote talent, or pay and policy for distributed teams draws more from this material than from engineering-org design.
- Dan Arias: Choose Dan Arias when the partner wants a counterweight voice for a debate, a panel, or a “what AI still cannot do” module aimed at engineering managers. Arias argues that an LLM cannot make anyone a software engineer: designing and shipping software requires experience with distributed systems, architecture, continuous delivery, accessibility, performance at scale, data integrity, and security. A model can produce jargon-heavy answers that look right until a human with real experience checks them. One post compares the claim to saying AI lets anyone perform surgery, and both posts make a human-centric case for technology. A session on reviewing AI-generated code, or on where human judgment remains mandatory, fits this position.
- Ido Geffen: Choose Ido Geffen when the buyer is a security leader or the training topic is AI inside security operations. Geffen argues that AI writes more code faster with more vulnerabilities while also getting better at finding and exploiting them. Hiring more security staff is rational but not sufficient: a team of twenty cannot manually review what AI assistants ship across a thousand engineers in an afternoon. Their posts also describe AI agents sharing discoveries through a memory system and chaining exploits to find zero-days. They point to agent-based pentesting as the defenders' equivalent step-change. A session on securing AI-accelerated engineering, or on where security review belongs in a faster build cycle, matches these points.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/