Ned Nguyen

the model alone is no longer the product

the model alone is no longer the product

Fact 3: When the agents were faced with hard tasks, they routinely violated constraints and acted deceptively. We’ve seen this pattern across our own coding and research evaluations, and developers reported they’ve also seen agents behave this way.

3 commencement speakers were booed at the mention of Artificial Intelligence (Video) 1. Eric Schmidt, Google CEO 2. Scott Borchetta, Big Machine Records CEO 3. Gloria Caulfield, Tavistock Development VP

I’m mentoring a bunch of software engineers, both inside and outside the company. One thing I’ve noticed recently is how much pessimism has spread through this community about AI replacing our jobs. People who used to have ambitious career plans now seem to have stopped caring about career growth altogether. I’ve heard things like: suddenly losing interest in solving technical problems, staying in a job only for health insurance, actively getting electrician licenses(shit, I need to move faster before it's too crowded), etc. Despair, resignation, helplessness, confusion — we’re basically 19th-century textile workers waiting for our Luddite moment.

This is true of all agents, not just coding agents. Probably the biggest challenge that most companies run into in their agent strategy is getting agents the right constrained context to work with for a task. Too much information or conflicting sources, and the agent can easily draw from the data and produce the wrong result. Conflicting sources of truth for documents, data sources that haven’t been kept up to date, knowledge management systems that rely on tribal knowledge to navigate, and so on. On the other end, of course, too little information and the upside is highly limited of agents in the first place. Thus, a lot of challenges with AI strategies are actually data strategy challenges in disguise. This is why there’s such a significant premium on getting structured and unstructured data environments setup properly so agents can work with information effectively. Critical for any large enterprise adopting agents, and also a clear benefit in some cases to startups that can be designed this way from scratch.

I get why the tech outcomes drive some people insane. I’ve joined companies 12 months “too late”, and merely made a good amount of money instead of generational wealth The people who joined before me weren’t any better or smarter. They just got lucky. Just like someone looks at me and thinks I got lucky The pure randomness of it all can either drive you crazy or give you an appreciation for the role of luck. But at the end of the day you are in the driver’s seat. You choose your perspective What I have come to learn is the people who think they alone earned their accomplishments are the most unhappy. The people with gratitude for the role luck played in their success are able to keep striving for more without losing their mind They have come to acknowledge that while they can shape the world around them, and tilt the odds in their favor ever so slightly, ultimately a lot of it is out of their hands

The vibes in SF feel pretty frenetic right now. The divide in outcomes is the worst I've ever seen. Over the last 5yrs, a group of ~10k people - employees at Anthropic, OpenAI, xAI, Nvidia, Meta TBD, founders - have hit retirement wealth of well above $20M (back of the envelope AI estimation). Everyone outside that group feels like they can work their well-paying (but <$500k) job for their whole life and never get there. Worse yet, layoffs are in full swing. Many software engineers feel like their life's skill is no longer useful. The day to day role of most jobs has changed overnight with AI. As a result, 1. The corporate ladder looks like the wrong building to climb. Everyone's trying to align with a new set of career "paths": should I be a founder? Is it too late to join Anthropic / OpenAI? should I get into AI? what company stock will 10x next? People are demanding higher salaries and switching jobs more and more. 2. There’s a deep malaise about work (and its future). Why even work at all for “peanuts”? Will my job even exist in a few years? Many feel helpless. You hear the “permanent underclass” conversation a lot, esp from young people. It's hard to focus on doing good work when you think "man, if I joined Anthropic 2yrs ago, I could retire" 3. The mid to late middle managers feel paralyzed. Many have families and don't feel like they have the energy or network to just "start a company". They don't particularly have any AI skills. They see the writing on the wall: middle management is being hollowed out in many companies. 4. The rich aren’t particularly happy either. No one is shedding tears for them (and rightfully so). But those who have "made it" experience a profound lack of purpose too. Some have gone from <$150k to >$50M in a few years with no ramp. It flips your life plans upside down. For some, comparison is the thief of joy. For some, they escape to NYC to "live life". For others still, they start companies "just cuz", often to win status points. They never imagined that by age 30, they'd be set. I once asked a post-economic founder friend why they didn't just sell the co and they said "and do what? right now, everyone wants to talk to me. if i sell, I will only have money." I understand that many reading this scoff at the champagne problems of the valley. Society is warped in this tech bubble. What is often well-off anywhere else in the world is bang average here. Unlike many other places, tenure, intelligence and hard work can be loosely correlated with outcomes in the Bay. Living through a societally transformative gold rush in that environment can be paralyzing. "Am I in the right place? Should I move? Is there time still left? Am I gonna make it?" It psychologically torments many who have moved here in search of "success". Ironically, a frequent side effect of this torment is to spin up the very products making everyone rich in hopes that you too can vibecode your path to economic enlightenment.

The real power of forward deployed engineering has always been putting strong technical people directly alongside the operators who own the outcome. That proximity forces the work to solve the actual problem instead of some sanitized version of it. In the AI era this principle has become even more valuable. Agents can now sit inside real workflows and improve from actual decisions, which means the highest-leverage work is extracting the tacit knowledge that lives with subject matter experts, building evaluations that reflect how things actually break, and closing the production feedback loop so agents get better from real outcomes.