@business Honestly the stuff AI is taking over is exactly how I learned the job 20 years ago. Pulling data, cleaning it, building the first draft. Not sure where freshers pick that up now. Something we think about a lot at InnoDexis.
Funny timing. Our September data at https://t.co/MTUrHaq2PN showed finance as pretty much the only sector where the AI research is ready AND companies are actually spending on it. And most of that spend is going to tools for the banks, not for the customers. So yeah, the adviser's job is changing well before the client notices.
Before you back a "first", ask what it's compared with. In September, only one in six research firsts answered that. Companies that put a number on their first drew twice the momentum.
#Innovation#Research#DeepTech#InnoDexis https://t.co/anr5Lrthnq
@sharran A year into building InnoDexis without funding, this hits. Most days the only proof anything's moving is what the team sees. You learn to be okay with that.
@deedydas Honestly the context-bound point is the whole thing for me. Models are smart enough already, they just don't see what's happening in science in real time. That's what we're working on at InnoDexis.
@alexrives Biology isn't short on prototypes, it's short on ways to get them past the bench. Our InnoDexis data shows life sciences with far more research in the pipeline than products reaching market. A virtual cell aimed at that gap would be a big deal.
@jimheskel This shows up in product launches too. In our InnoDexis tracking, launches that commit to one clear result outperform the "here's everything it does" ones by a wide margin.
Launches that state a number carried high momentum at 2.8x the rate of those that don't. Add a date and it climbs higher.
#ProductLaunch#DeepTech#Innovation#InnoDexis https://t.co/V1zO37egqT
Most research is years from use. The part that isn't can be found fast: read how it states its own impact. Where September's research is close to market, and who should move first.
#DeepTech#TechTransfer#Innovation#InnoDexis https://t.co/iUPDnE05Re
Physical AI is being sold as software and built as hardware. Our September read on robotics: where the money went, what research says is really holding robots back, and where the white space is.
#PhysicalAI#Robotics#Humanoids#InnoDexis https://t.co/ApUE2bGLLl
AI's power problem isn't being solved in labs. It's being solved in land deals, utility partnerships and cooling acquisitions.
The scarce asset isn't chips. It's a secured grid connection.
#DataCenters#AIInfrastructure#EnergyTransition#InnoDexis https://t.co/AJTPwPU7oO
Space science is studying black holes and Mars. Space business is selling connectivity and launch.
The opening sits between them: climate monitors already running, with users but no sellers.
#SpaceTech#EarthObservation#NewSpace#InnoDexis https://t.co/F8zZBf6sic
@PeterDiamandis The hard part is spotting the intersection before it's obvious. That's why we built InnoGraph at InnoDexis: to map where fields like computation and biology start converging, not after the breakthrough lands.
@theinformation Capital is flowing in, but the real question is which on-site power tech is ready to scale. Fuel cells and next-gen turbines are on very different timelines. That's the kind of shift we're watching closely at InnoDexis.
"Industrial base" is the right frame. The hard part is seeing where the building is actually happening: which labs, startups and supply chains are moving on advanced manufacturing, energy and defense tech, and who's funding them. That's what we're mapping at InnoDexis. Curious whether the next 250 gets built from the coasts or the heartland.
@nicomoel Lived this. We built InnoDexis pre-funding, and not having money to throw at problems forced every decision to be about what someone would actually use. It also forced us to sell before the product felt "ready." Hardest constraint, but the most useful one.