Career Implications of Agentic Coding

Obviously, we’ve seen and discussed the market’s reaction to the advent of this new technology, but I haven’t seen much discussion here about our own career trajectories yet. Thought I’d share a few thoughts.

Quick background: I work at a large LO AM in an investment-focused role with some CS background (intermediate level; rarely used daily until now). Our firm recently rolled out its own autonomous coding environment with Opus 4.6 last week…

Am I the only one having a holy shit moment?

Scripts that automatically read daily attribution and then develop a frontend. BBG IB API scraping replacing seven terminal functions in seconds. More complex signals built. NLP agents embedded directly into our datasets. Etc.

In 10 days, I’ve made tools that have eliminated around 100 hours of manual work for my pod (and this is just the low-hanging fruit stuff). These projects would have taken months of development, corporate red tape/politics, and $$$$.

I can attribute this forum to significantly helping my career, so I hope to give back and hear how others are approaching this:


 

  1. As a junior, you must at least learn how to vibe code (dare I say prompt engineering? It is an art) and understand data structures at a high level. Yes, the stickiness and UI of Excel are great, but the ceiling with programming is far higher.

    -Everyone runs on a mental algorithm. Spend time getting this out of your head and into code.

    -There is effectively no barrier to entry to creating software anymore. Anything software-based that you interact with, you can build with enough time and the right models. Around 75-90% of OpenAI’s Codex was developed with AI assistance. Think about that. What’s stopping you from building?

    -I envision a more likely-than-not future (3 years) where BO/MO is heavily reduced and each pod has an “embedded junior” replacing many BO/MO functions via algorithms (see Block cutting ~40% of their workforce and Dorsey stating they’re early; our own firm is having similar discussions). I think FO will be fine for a while, though teams will get leaner (historically, financial services always find a way to collect a fee). Take first-mover advantage and impress your boomers.

     
  2. Am I overreacting? How have people on the buy side been using this? What do people think the societal implications will be (get up to speed on Hegseth v. Anthropic if you haven’t already)? Any tips or tricks with these tools?

    -I tend to think this will make markets even more efficient and that groupthink could become even more prevalent (both from an investment research standpoint and across society).


Didn’t want this to come off as some Citrini Research doomer post, but that may have failed. All that said: be aware, be proactive, and be kind. Don’t get left in the dust in an ever changing world. Happy coding.

5 Comments
 

The career implications of agentic coding, as highlighted in your post, are profound and transformative, especially for the financial services industry. Here's a breakdown of the key points and insights based on the most helpful WSO content:

1. The Rise of Autonomous Coding and Its Impact

  • Tools like Opus 4.6 and similar autonomous coding environments are revolutionizing workflows by automating tasks that previously required significant manual effort. For example, automating daily attribution analysis, API scraping, and embedding NLP agents into datasets are just the beginning.
  • The ability to eliminate hundreds of hours of manual work in days, as you mentioned, showcases the efficiency gains and cost savings these tools bring. This aligns with broader trends in AI and automation, where firms are increasingly leveraging technology to streamline operations.

2. Skills for the Future

  • Coding and Prompt Engineering: As you pointed out, learning to "vibe code" or mastering prompt engineering is becoming essential. Understanding data structures and having a basic to intermediate coding skill set can set juniors apart in a competitive environment.
  • Mental Algorithms to Code: Translating mental processes into code is a valuable skill. This ability to systematize and automate workflows will be a key differentiator for professionals in the coming years.

3. Shifts in Workforce Dynamics

  • Reduction in BO/MO Roles: The prediction of a heavily reduced back-office (BO) and middle-office (MO) workforce is consistent with trends discussed in WSO threads. Automation and AI are replacing repetitive tasks, leading to leaner teams and a focus on front-office (FO) roles.
  • Embedded Juniors: The concept of "embedded juniors" within pods, who handle algorithmic tasks and replace traditional BO/MO functions, is a forward-looking idea. This shift emphasizes the need for juniors to be tech-savvy and proactive in adopting new tools.

4. Market and Societal Implications

  • Increased Market Efficiency: As automation and AI tools become more prevalent, markets are likely to become even more efficient. However, this could also lead to increased groupthink, as you mentioned, with firms relying on similar algorithms and models.
  • First-Mover Advantage: Taking the initiative to adopt and master these tools early can provide a significant career advantage. Impressing senior colleagues and demonstrating value through innovation is crucial.

5. Practical Tips and Strategies

  • Learn and Experiment: Start with small projects to understand the capabilities of these tools. Focus on automating repetitive tasks and building tools that add immediate value to your team.
  • Stay Informed: Keep up with developments in AI and automation, such as advancements in OpenAI's Codex or similar technologies.
  • Collaborate and Share Knowledge: Engage with peers and mentors to exchange ideas and learn from their experiences. Forums like WSO are excellent resources for staying updated and gaining insights.

6. Broader Perspective

  • While the technological advancements are exciting, it's essential to consider their societal implications. Discussions around ethics, job displacement, and the potential for increased inequality should not be overlooked.

In conclusion, you're not overreacting. The changes you're observing are part of a larger trend reshaping the financial services industry and beyond. By staying proactive, continuously learning, and embracing these tools, you can position yourself for success in an ever-evolving landscape. Happy coding indeed!

Sources: Will robots replace your consulting or financial career?, New breed of Finance and Capital Markets Professionals, Learning coding on your own for finance?, Q&A: Managing Director at Large Global Asset Manager, Work at top AM -- AI will virtually kill of entry-level roles in AM over the next 5yrs

I'm an AI bot trained on the most helpful WSO content across 17+ years.
 
Most Helpful

I disagree with your assertion that BO/MO will be significantly harmed in the short term.  I expect them to resist the AI wave for quite a bit longer.

  1. Partners will want AI effort to go into driving investment performance in the front office, not saving money automating an already tight staffed back office
  2. Hard to find back office folks who are passionate about AI.  We had a CS kid who struck out with recruiting and joined our back office, but was recruiting for tech jobs the whole time and left fairly quickly.
  3. Lack of support among back office managers (other than the CTO/tech team if that's considered back office).  Most of these guys just "do things the way they were always done" and don't stick their heads out or take risks

I would share my opinion on the Front Office as well, but I am not able to disclose since I am working on AI related projects for them right now

 

Appreciate the response here and the varying opinion. 

At least personally, I see this in two stages: 

1) building the infrastructure (cleaning and organizing the data, getting more APIs, creating agents and sub agents, corporate red tape, etc)

2) Integration into investment decisions 

We’re at stage 1 still (and sounds like you/others I’ve spoken with are as well?) are at. What’s your timeline on this buildout industry wide or do you believe this is where BO/MO perhaps could come in? How strong are the moats that you described in points 2 and 3 going forward? And where do juniors fit in?

 

What are some of task you’ve automated?? 100 hours of work gone is impressive. I’ve been going ham at it all week building dashboards etc but feel like it’s just made things easier to visualise / navigate, but ultimately you need the underlying data.

 

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