Trace CohenEstimated Reading Time: 4 minutes
iRobot much?
Did you ever see the movie Hackers (1995) with Angelina Jolie? That’s kind of how I feel right now installing random stuff using my computer Terminal based on what some random Ai startup is telling me to do - watching random characters, words and I dont even know what type out and move across my screen as things get installed and configured…
If you’ve been on Twitter or read the tech news over the last week, the biggest thing right now is Ai coding assistants, specifically Cursor. There are many out there like Microsoft CoPilot, but Ive never used it and honestly dont even know where to start, which is why Cursor was so easy. I literally downloaded it, prompted their chatbot with ideas, it printed the code, which confused me and made no sense, so I asked more questions until eventually I learned and figured it out. It’s very apologetic and empathetic too lol.
I haven’t really coded (if you can even call it that) since I failed AP Comp Sci in high school (2005 yes I’m old) and more seriously with my first startup out of my dorm-room building personal websites for friends to get jobs using Dreamweaver, CSS and HTML. The most basic coding possible really, but it worked as a start to something larger (startup got acquired!), which is exactly where we are again now 20years later. The problem is kind of that 99% of things will be crap but that’s OK because iteration is the new innovation.
So for those looking to tinker with ideas, test out concepts, or even develop early-stage prototypes, Cursor provides the perfect environment. It strips away many of the complexities associated with traditional coding environments (which I dont know much about), making it accessible to a broader audience, including those who may not have formal training in software development like me.
However, while Cursor is excellent for getting ideas off the ground, it's important to recognize its limitations and why it wont replace experienced developers just yet. Building real, scalable platforms or companies requires more than just a basic understanding of coding. Deep technical knowledge of systems architecture, scaling strategies, and integrations is essential to create robust, secure, and scalable applications. The simplicity of Cursor, while beneficial for initial ideation, can mask the complexities that arise when trying to take an app from a prototype to a production-level platform.
Hence my really bad and impossible game below I made in a few hours because I had to do lots of Ai prompting, learning Github repositories then how to host/launch it using Netlify.
gentle-eclair-66d998.netlify.app
I also was hanging with a VC friend last week who was talking about their do diligence process and how they rate startups. So I prompted a few times, make a few edits, changes etc and created a simple rating system for 3 partners to give feedback on a potential startup they’re looking at. This literally took 20 minutes to do right in front of them and while it’s very simple, just the process of a non-technical person watching it happen in real time, definitely blew their mind while I played it cool, literally making it up as I went along.
dodiligencescoring.netlify.app
The AI Landscape: Current Platforms and Future Prospects
The broader AI landscape though is evolving at a break-neck pace, with platforms like Cursor leading the charge in democratizing access to seemingly complex Ai thanks to the help of actually smart chatbots. These tools are lowering barriers to entry, enabling more people to engage in coding, data analysis, and even machine learning. The AI ecosystem is now more diverse than ever, with applications ranging from natural language processing (NLP) to computer vision, robotics, and beyond available to basically anyone.
Benefits for Hobbyists and Prototyping:
Lowered barrier to entry for coding
Accelerated ideation and prototyping
Assistance with syntax and common coding patterns
Valuable learning tool for beginners
Limitations for Production-Ready Applications:
May not adhere to best practices or company-specific coding standards
Limited understanding of complex system architectures
Potential security vulnerabilities if not properly vetted
Lack of deep domain knowledge for specialized industries
The Broader AI Ecosystem is Massive
AI-assisted coding is just one facet of the rapidly evolving AI landscape though. Let's explore other key areas where AI will make a significant impact:
1. Natural Language Processing
Large Language Models
Chatbots and virtual assistants
Speech recognition and synthesis
Machine translation
2. Computer Vision
Image and video recognition
Object detection and tracking
Facial recognition
Augmented and virtual reality
3. Robotics and Automation
Industrial automation
Autonomous vehicles
Drones and unmanned systems
Robotic process automation
4. Machine Learning Platforms
TensorFlow
PyTorch
Amazon SageMaker
Google Cloud AI Platform
5. AI in Business Intelligence
Predictive analytics
Customer segmentation
Fraud detection
Recommendation systems
What else…?
Future Applications of AI
As AI continues to advance, we can expect to see innovative applications across various industries - there is still so much green space opportunity as it will take years to reach, teach, convince and integrate.
Healthcare
Personalized medicine
Drug discovery
Medical imaging analysis
Predictive diagnostics
Education
Adaptive learning systems
Automated grading and feedback
Personalized curriculum development
Environmental Sustainability
Climate modeling and prediction
Optimizing renewable energy systems
Smart grid management
Wildlife conservation
Finance
Algorithmic trading
Risk assessment
Personalized financial planning
Fraud detection and prevention
Agriculture
Precision farming
Crop yield prediction
Automated pest and disease detection
Creative Industries
AI-generated art, music, and literature
Virtual influencers and digital humans
Personalized content creation
Cybersecurity
Threat detection and prevention
Automated incident response
Behavioral analysis for anomaly detection
What else…?
Investing in Broad and General VS Niche and Specific?
As AI continues to advance literally everyday, we can expect to see more sophisticated tools that bridge the gap between simple prototyping and production-ready applications. However, the need for deep technical understanding, especially in areas like system architecture, scalability, and integration, will remain crucial for building robust, enterprise-level solutions.
As an early stage investor investing in the broad LLMs and hardware is basically out of reach because most of those startups begin with $10M+ raises at $50M+ valuations which sadly doesn’t work for Emerging Managers economics. That means we need to find niche and specific startups/founders with a moat in industries where their expertise and network are their unfair advantage initially.
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