Chamath Palihapitiya (@chamath)

Here is my AI investing guide. Sitting here August 2026, my current best thoughts are as follows: 1. LPS (Land Power Shell) is still the most obvious and fastest path to cash on cash returns. Lots of value can be assembled and traded quickly at this layer. And as data centers get more pushback, energized land can explode in value. Very bullish here. I’ve stepped into this layer very aggressively. My partner @anitavlallian and I have acquired almost 6GW coming online in a ramp from today thru 2029 of grid power and behind the meter. 2. Silicon - I helped get @GroqInc off the ground in 2015 and we licensed it to @nvidia for $20B Dec2025. I won’t invest or incubate anything in this layer now. The perf demands of the chips are too high, manufacturing precision is too complex and supply chain influence to get adjacent components like memory isn’t possible for a startup anymore. Lots of capital will be wasted here chasing Groq and Cerebras’ success. Note that both startups made sense a decade ago when these constraints were much more modest. 3. Clouds - Clouds are very very lucrative but very hard to build and very expensive and technically complicated to maintain. And as alignment becomes a more important issue, I expect the clouds will be asked to build robust KYC and attest to it. This makes the risk:reward ratio skewed. I don’t want to be responsible when the USG says a cloud allowed a bad actor to do something bad because of poor KYC. 4. Models are complicated. The big open question is how much of the revenue being generated by them today is because of tokenmaxxing and poor model behavior. If it’s a lot, then the annualized revenues will diminish meaningfully even as token consumption inflects upwards. This is the big economic question at this layer. 5. Harnesses are where the action is and why I started @8090solutions two years ago. In a nutshell, the harness helps enterprises owns their proprietary context (what Alex Karp calls their ‘alpha’). This is an enterprise’s data, workflows, evals, and business rules. A harness that gives this to an enterprise is what creates very low model-agnostic switching costs, which further reinforces my views of #4# above. 6. Applications will be another long term winner along with harnesses. This is where the differentiation between “off the shelf” and “custom time and materials” melts away. Every company, with the right harness, can now imbue their alpha into the software that runs their company. I expect this to mean that “off the shelf” is largely replaced with custom software creating a huge opportunity to write these solutions for companies. Build once and sell repeatedly is a laggard GTM motion for a SaaS world that isn’t needed here. Think custom by design, alpha embedded, proprietary by nature. Fin. Good luck to all the players!

An interesting trend in forecasting where the world is going is by looking at SEC filings for the overuse of buzzwords. When buzzwords peak, the value of those terms usually deflate. It is one of the cleanest examples of how corporate America rapidly changes its language in response to shifts in technology, culture and regulation. Right now everyone is glomming onto AI like it’s a life raft. But these same folks have not yet shown repeatable, audited, verifiable ROI even as their CapEx and OpEx are increasing with token costs on all things AI. This has happened before. DEI was the defining investment and disclosure trend from 2020-2022, then declined rapidly. “AI” began a sustained climb after late 2022 and is now nearly ubiquitous in large-company filings. “Agentic” is now following an even steeper adoption curve, albeit from an almost nonexistent base. It appears to be becoming the newest “must-have” AI descriptor. Now, to be clear, AI is real and will be. It is the defining change of our lifetime. But the path to spending money to see value is still largely uncharted. We are in the early phase where a few companies are making all the money from our largesse. This needs to be reset for everyone to win.

In an early meeting at Facebook (c. 2007), when I was describing the goals of Facebook Platform (an area I oversaw) Bill Gates yelled at me/us. His quote has stuck with me to this day: “This isn’t a platform. A platform is where the collective sum of revenues of the participants exceeds those of the platform itself.” Ladies and gentlemen, I present to you the tokenmaxxing circle jerk.

Why would you let a competitor use the same software you do? That is at the crux of SaaS - it’s hard to differentiate yourself if your tools are the exact same as your competitors. We are flipping this on its head. AI allows every company to build and use custom software - software that helps you amplify your edge and increases the distance between you and your competitors. Ping us if you want our help: sales@8090.ai