TJC Invest

Box 3-update per 4 mei 2026: De nota naar aanleiding van het verslag en beantwoording van kamer vragen is uit. Het kabinet houdt vast aan 1 januari 2028 voor de nieuwe wet, ondanks alle kritische vragen vanuit de Kamer over de haalbaarheid. 1/11

Whether it’s existing consulting firms, new ones that emerge, FDEs from agent vendors, or new internal agent engineering roles, the amount of work that is going to be created to implement agents in enterprises will exceed anything we imagine today. The complexity of implementing agents in any existing organizations is very real. When I talk to large enterprises, as you move from a chat paradigm to agents that participate in meaningful workflows, there are a number of things they need to do. First, you have to get agents to be able to talk to your data securely across your systems. In many cases, enterprises have decades of legacy infrastructure that contain the valuable context for AI agents. That’s going to take a ton of work to go modernize and move to systems that work well with agents. Then, you need to ensure that you’ve implemented agents with the right access controls and entitlements, the right scopes to be safely used, and have ways of monitoring, logging, and securing the work that they do. Next, you need to actually document the processes in the organization in a way that agents can utilize for doing the work. You also need to figure out what the new workflow looks like when agents and people are working together on a process, and who steps in where. Just replicating the old workflow will mute the gains. Oh and you likely need to create evals for your top new end-state processes. Finally, you have to keep up with a rapidly changing set of best practices and architectural shifts happening in the agent space. While it’s fun for people to change their personal productivity tools on a dime, it’s 100X harder to do this in a business process. The speed of change is a blessing and a curse right now for anyone trying to keep a stable system design. All of this means that individuals and companies that develop expertise on the above set of components (and more) are going to be needed to help organizations actually implement agents at scale. This is also the rationale for vertical AI agents right now that can go in deep on a business domain and help bring automation to it. This is a huge opportunity right now whether you’re doing this internally or as an external business provider.

$hims revenue up 1,478% since IPO stock up 122%

ngl I've been mass-testing AI agents across all my products for the last 2 weeks tried to replace entire workflows. customer support, content generation, SEO audits, social scheduling here's what I found: agents are insanely good at tasks with clear inputs and outputs. content drafts, data extraction, competitor analysis. like 90% as good as a human, 50x faster but they still completely fall apart when context matters. when you need taste. when the answer is "it depends" I watched an agent confidently give a user the wrong Outrank plan recommendation 3 times in a row because it optimized for the metric instead of the actual need so painful to watch 😅 my take: the best AI-native products in 2026 won't be "fully automated" anything they'll be the ones that figure out the exact moment to hand control back to a human that handoff is the whole product

I talked to a buddy that is head of revenue over a large publicly traded company. He says they have tried Claude a number of times to automate some of their tasks, and it will continually do things they didn't want, make changes they didn't expect, and require someone to catch the mistakes it makes. The demos of Claude on X are cool, but don't be mistaken. It still requires humans to look over every aspect of what it's doing. The unemployment rate in the us is 4.3%, which is considered fully employed. Don't believe the doom and gloom.