<p>a16z wrote an article about the difference between Individual AI and Institutional AI. The analogy is great: when factories replaced steam engines with electric ones in the 1890s, productivity didn’t increase for 30 years. This was because the new motor was placed in the old factory. It only took off when the factories themselves were rebuilt - conveyor belts, distributed motors, new processes.</p>
<p>With AI, it’s exactly the same. Each employee has become faster - their own prompts, their own chats, their own artifacts. But these artifacts are not connected into a single stream of decisions. There’s a lot of “content” and “activity,” but little verifiable business impact. Teams are drowning in AI-slop: generating has become easy, but filtering the signal is harder.</p>
<p>What distinguishes Institutional AI:</p>
<ul>
<li>coordination layer - a unified context and synchronization, rather than each having their own ChatGPT</li>
<li>focus on signal extraction - not “generate 20 options,” but find 1 correct move</li>
<li>AI optimized not to agree with the user, but to highlight risks and break weak logic</li>
<li>optimization for outcome and revenue, not for “time saved for the employee”</li>
</ul>
<p>In short: we already have “electricity.” Now we need to rebuild the factory.</p>
<p>#ai #a16z #strategy #management</p>
