It is built for the earliest stage. Every session targets a blocker pre-seed and seed founders are hitting right now.

Speakers don’t just speak on a big stage and disappear. They stick around for off-the-record roundtable discussions, giving attendees a chance to actually meet them.

Every speaker knows the early stage. Each of them has founded, funded, operated, or advised a pre-seed or seed startup, so the advice fits where you are now.

Successfully raised pre-seed, seed and series A rounds from top-tier investors (including a16z, Sequoia and Y Combinator), met through DECODE roundtables and dinners.

​Identified advisors and mentors (including experts from Apple, Google and Microsoft) who’ve helped them 10x their startup’s growth.

​Found co-founders and early hires for their startups through DECODE community members, like the founding teams of Databricks ($62bn valuation), Agora (IPO) and FlowGPT (Series A).

We all now know that AI enables founders to build in hours what used to take months. However, the latest data shows top startups are scaling revenue exponentially faster, too.

According to the Pacesetter Index, the top quartile of startups generating under $10M in revenue is growing ARR by an astounding 26x year-over-year (meaning a startup generating $10M today would hit $260M this time next year).

A prime example of this speed: Typesafe AI's Jev — an advanced decision-making model that’s great at multiple choice — scaled from $0 to $100M in just 7 days.

Per the latest data from Ramp, ~80% of OpenAI and Anthropic’s enterprise revenue comes from just 1% of customers with a heavy concentration in tech and AI.

Anthropic’s leaked IPO prospectus confirms this datapoint, with ~25% of Anthropic’s revenue coming from just 2 unnamed customers.

Bottom line: Yes, enterprise AI spend is surging, but this spend is coming from a small handful of players.

In the past, when startups raised money, they’d approach investors and raise as big of a round as they can in one go. That process is now being split into two steps:

We haven’t just seen this process play out repeatedly across our portfolio. It is also validated by data from Sequoia which suggests that founders are raising at an average post-money valuation of $110M in step 1 and then raising at an average post-money valuation of $3.4B post-money in step 2.

With so much money flooding AI startups, the bottleneck to startup growth is no longer monetary but physical: electricity access, permits, and supply chains — and how to get access in the most capital- and time-efficient manner possible.

Therefore, it will no longer be enough to say that you’ve got the most powerful model; you’ll need to be the most powerful on a per-megawatt basis.

When determining how to price their product, startups typically take 1 of 3 paths:

Currently, over 1/3 of AI startups default to competitor-based pricing, whether or not it makes sense.

However, as more companies implement AI (and as we have more data to prove the ROI of AI investments), we expect more founders to lean into value-based pricing to fully capture the upside.

This was one of the top topics we discussed at the Jeffersonian dinner we recently hosted with Megh Gautam, Head of Product (Apps) at Box and Vinay Seshadri, Senior Director of Product at Chargebee. Find other key takeaways from the session here.

Sophia, a former conference lead at DECODE, just launched Blinkko, a wearable AI companion. She worked at Apple before getting funded by YC China to focus on AI x hardware at Blinkko.

Learn more about their recent launch in SF here.