What Deeptech Founders can Learn About GTM & What I learned at Deeptech NYC Week:
I learned Deeptech Founders really struggle with getting nerd-snipped and commercialization - here are the ways to address these habitual problems as they come up.
It was kinda funny to me how deeply I was able to relate to one a major problem I heard deeptech founders talked about - despite not being one myself:
GTM and tech commercialization are hard to do on their own, AND being a technically-oriented person you really just want to build and tinker all day, not sell.
In hindsight this totally makes sense, I come from a software background rather than a sales one, so I lean towards the builder more.
It also why I feel, out of all the people I know in the GTM space, uniquely well-suited to share how I think about GTM for Deeptech, and how I got myself to shift from overindexing on tinkering and building to spending a more healthy split on GTM and building.
Two disclaimers:
there is nothing wrong with how sales-background based founders in the GTM space think/operate, it is just not how I do it and I think a bit more foreign to Deeptech founders, who are on average more definitely technical than I am.
Your mileage may vary. Once again I am not a Deeptech founder myself - I am really excited about solving problems in the space and am a technical (ik software isn’t mechanical engineering got it got it) founder myself. Figure out what works for you and your unique constraints.
As a bonus throughout this article I'll drop in some intelligence data points I pulled out from the dataset I built (I scraped the major events and enriched to get a ~900 person sample of the attendees). Keep in mind this is not based on an official exhaustive dataset.
I'll share more about about what I learned from this process soon in another post🙂
First intel point:
Intelligence Point: 32% of Deeptech Week attendees were VCs or investors, while 39% were deeptech founders - the remaining attendees were either classified as consultancies/agencies or enthusiasts/job-seekers.
Deeptech GTM Painpoints:
The two big takeaways around Deeptech GTM I walked away with:
Large majority of Deeptech Founders are nerds (I say this with loving kindness not derogatory) and want to tinker and build instead of sell, talk to customers and execute a GTM and commercialization. This is a fundamental problem on top of commercializing Deeptech being difficult in itself.
Most Deeptech startups should really focus on standing up a basic GTM motion that integrates into the difficulties of commercialization to build an effective high tempo GTM and commercialization motion. Then scale and automate it.
To clarify what I mean by GTM:
GTM (Go-to-Market) : Generating Pipeline and converting to deals, market intelligence gathering, developing and iterating on messaging, Total Addressable Market Mapping
Solving for message market fit: A/B testing messaging against the market and leads.
Think all of the different ways to say sales and marketing and this it this bucket
Commercialization: technical product/service development and iteration to build something that better fits existing market demand, solves a tangible pain point(s) effectively
Solving for: product market fit
All the ways you can say product management & development
Commercialization and GTM are different things, but should be in a continuous feedback loop informing one another.
You will never build anything innovative just listening to other people's opinions (ask 9 experts you'll get 10 opinions on what to build and why) - you must have taste.
But you will never achieve commercial success if don't listen customer feedback and the market and actively incorporate this into your product.
This is a problem I personally struggle with coming from a software background. I love to tinker, I love to build, I have "the a million mostly finished but ultimately abandoned side projects" syndrome.
To be frank, there was a period of time a while ago where this was major issue for me running a solo company to the point of causing serious problems in my life. I am grateful to have gone through this pain, since I learned from it. The three most important things I learned from this:
Be brutally honest with yourself about the purpose of the time you are spending. Every task I write down to do, or block out on my calendar, I bucket into these categories:
art: for its own sake. Does not mean "art" in popular reference to painting/statues. It means something for its own sake, experimentation, tinkering, the thing you do for its own sake.
fulfillment/building: executing on commercialization, building new features, iteration, developing specifically with the goal of revenue generation in mind.
selling/marketing: GTM, publishing, running out outbound, having conversations with leads or people in the market who have problems I am trying to understand better and solve.
Work with people who have complimentary ways of working and skillsets:
there is a reason the business + technical cofounder pair is so common - it works
If you're gonna go so solo, you must be even more brutally honest. You are your own forcing function.
But you can also partner with other solo companies or companies in general to compliment each other
Find a way to kill two birds with one stone:
This article is a perfect example. Me getting to create and write like this is art for me, and publishing it is selling/marketing.
The tricky part here is that you will be able to convince yourself of any sort of time split because you really just want to build - so don't start doing this until you've built the muscle and habit to sell/market.
For getting someone who is a builder/nerd like me, I’ve personally found reframing selling/marketing from a “necessary evil” to a fun puzzle solve works.
This reframe helps me tap into a similar mental muscle for building just redirected outwards towards a GTM motion (and making sure I still have plenty of time to tinker and build hah).
Transforming data into insight and actionable intelligence is fun, and most importantly, enables effective pipeline generation and feedback for product commercialization.
The faster speed-to-insight, the faster you iterate, the faster you hit message-market fit, and then product-market-fit.
Intelligence point: Out of the Deeptech startup categories in the dataset, Transportation & Mobility dominated with 46 startups (22% of founders), only 17% were in stealth mode. Contrast this with Computational Hardware where 25% of startups are flying under the radar, suggesting areas of intense competitive development.
This makes a ton of sense given there was an example of an quantum computing startup mentioned during the commercialization panel…
"I can give a specific case study where a deep tech startup was specifically building quantum computing in order to enhance stock trading. Now, what happened there is that they got a very, very, investment from a large investment bank but the condition was that they would be unable to use the specific logo and their theory for that is that they did not want to even tell their competitors that they were using this because that would give their competitors enough information on what strategy these quant traders were using…"
-Ayub Ansari
Takeaway: Computational Hardware startups probably do not need help with GTM but good to know about the market haha 🤷🏻♂️
Examples of What a Deeptech GTM Motion looks like:
Carbon Crusher Intelligence System Overview - Watch Video
When to think about Scaling & Getting Help:
If don't have the time, need help or need to go beyond the basics you should get in contact with a GTM Consultant (so me or somebody like me), but you shouldn't do it right off the bat.
You should be able to execute a basic manual motion, understand that process, and then add in the 100x leverage, not the other way around because this does not work (and I will happily advise you on where to start but I will tell the same thing I just wrote above)
Starter GTM Resources:
Once you've reframed GTM as a technical challenge rather than a dreaded chore, these tools become your test equipment and IDE.
Tech Stack Options:
Outbound Sequencing: Smartlead/Instantly, HeyReach/Phantombuster
You won't know what to do with the above tools without context and strategy, best place for are people sharing on LinkedIn. People to follow:
Jacob Dietle :) for content specifically on Deeptech GTM (I promise if I knew of anyone else in the space talking about the overlap of GTM and Deeptech I'd plug them as well lol)
Jordan Crawford for Relevance + Sending Permissionless Value
Commercialization Resources Mentioned at Conference:
Related intelligence point:
Intelligence point: Coming in at the highest, 71.4% of Manufacturing & Industrial deeptech founders have commercially-focused titles, compared to 36.7% in Healthcare & Life Sciences, which had the lowest.
My Raw Notes on What I Learned:
On Hardware Manufacturing & China:
there is much talk about how the US is behind China in regard to manufacturing, hardware and technical innovation. This is broadly true and known - but I think what is misunderstood is that this has been the case for YEARS, and is now only at the forefront since it is painfully obvious.
I spoke with an ev hardware founder who (paraphrasing a bit) said he had to go to China to start his first hardware startup and the Chinese government gave him tens of millions of dollars to start it while he struggled to get any funding in the US.
He also spoke about how all motorcycle manufacturers effectively stole IP from one another so regularly it was taken for granted - like it was a common understanding/forgone conclusion.
He also said the Chinese government made it effectively impossible for him to sell his bikes to the local market
China invites foreign companies to come take advantage of the abundance of (now increasingly less abundant) cheap labor, parts and insanely effective supply chain (this is still a key competitive advantage)
On Unique Issues Related to Deeptech Commercialization:
Surprising but makes sense having to protect your IP while having sales conversations and just how much of this an issue that this seems to be. When engaging with large companies as potential clients and partners
get them under contract for further due diligence
Licensing deep tech and focusing on commercialization rather than so much technical development was also a really interesting play
If you are partnering with larger orgs it is likely that there are people actively looking to gather that technical intel/understanding on how your tech works so they can clone it internally themselves and kill your partnership deal
On the People there and why they were there:
I was very impressed by everyone I spoke to (mentally I bucketed into founders and venture capitalists, consultants/agencies and enthusiasts/maybe job seekers) I spoke to, this is of course only my anecdotal experience, but the clear intelligence, insightful questions, thoughtful responses was not surprising given where I was, but the people along with the space/topics being discussed was really inspirational.
With that said, I was almost taken a back at how much of a second thought commercialization is for many Deeptech founders. This is a huge issue and there are many actual Deeptech founders who spoke deeply and effective on how to solve this problem. The panel on this was great. This also made think about how how basic most GTM motions or just non-existent these Deeptech startups have - which makes sense given where you need to spend time as a deeptech founder, developing innovative tech.
opposite problem for saas, extremely effective GTM but core product no moat and no deep innovation
overall really cool helpful and kind people
broadly people were either trying to meet investors to raise or deploy capital. Both in traditional equity-based and non-dilutive financing around hardware capex which was cool to learn about. Tech week/conferences have always been focused on this startup/vc matching so wasn't surprising just interesting to watch as someone not with those goals
On the term "Deeptech":
Deeptech is an amorphous term gigantic spread of startups from b2b, b2c, b2g, b2b2c, etc across a wide variety of verticals from paint that cools whatever you put it on 15 F below air temp, to AI agents for aerospace, to manufacture on demand for steel, to defense tech, to zinc-based electrical generators, to crop field scanners you can put on a quad bike. Really awesome.
I think "startups solving sci-fi problems" is both descriptive and aspirational enough to work.
but this spread does make it harder to establish shared context some times
makes me think some sort of conference intelligence tool (just "these are the 10 ppl you should look out for that have overlapping interest") would boost the value of the already valuable time spent.
in a non-exclusionary but serendipity boosting way
generally smaller group/conversation focus the easier it is to establish shared context
still need to be open to entirely unexpected conservations both out of self-interest, respecting others as subjects rather than objects and just for fun and curiosity
What's Next & Research Methods:
In the next few days, I'll dropping some intelligence on what I learned scraping all the big events at Deeptech Week and using AI Agents to Enrich & Analyze the Ecosystem.
Subscribe to see what I find. I’ll be trying to answer questions like these. If you want one of these questions researched specifically, (or access to the enriched dataset itself) please hit me up and let me know :)
Deeptech Founder Perspective:
Commercialization Strategy-Market Fit: Which verticals in the NYC Deeptech ecosystem (like 60% of attendees were from local area) demonstrate the shortest path to revenue, and how does this correlate with funding success and founder backgrounds?
Technology-Communication Alignment: How do successful deeptech startups in NYC align their technical capabilities with their messaging strategies, and which channels show highest effectiveness by industry vertical?
Strategic Partnership Navigation: Which types of corporate relationships are NYC deeptech startups pursuing, and how does this vary by technology maturity and vertical?
VC Perspective:
Investment Timeline-Technology Alignment: Which NYC deeptech verticals attract investors with appropriate timeline expectations, and what misalignments exist between startup development cycles and investor expectations?
Regulatory Advantage Creation: How do NYC deeptech startups strategically leverage regulatory requirements as competitive moats, and which verticals demonstrate the strongest regulatory advantages?
Staged Revenue Development: How do different technology domains approach the challenge of generating early revenue through tactical applications while preserving long-term vision?Intelligence Data Points Research Methodology:
I leverage a combination of data engineering + AI agents to scale research, this the high level research process executed for each fact using agents to analyze the dataset I built:
Fact 1: VC to Founder Ratio
Verified statistic: 32.1% of Deeptech Week attendees (in the enriched dataset) were VCs/investors, while 39.2% were deeptech founders—a ratio of 1:1.22.
Methodology:
Analysis based on 536 enriched entries (59.2% of total 905 attendees)
Identified by presence of 'Category' field with valid values
210 classified as 'deeptech_startup' and 172 as 'venture_capital'
Calculated percentage against enriched total (not full dataset)
Fact 2: Transportation & Stealth Mode
Verified statistic: Transportation & Mobility represents the largest vertical with 46 startups (21.9% of founders), with 17.4% in stealth mode. Computational Hardware startups show 25% in stealth mode.
Methodology:
Analyzed all 210 startups with 'deeptech_startup' classification
Identified verticals through 'Industry Vertical' field
Detected stealth status by searching for 'stealth' keyword in 'Org' field
Confirmed percentages: 8 of 46 Transportation startups (17.4%) in stealth mode
Confirmed 1 of 4 Computational Hardware startups (25%) in stealth mode
Fact 3: Commercial Titles by Vertical
Verified statistic: 71.4% of Manufacturing & Industrial deeptech founders have commercially-focused titles, compared to 36.7% in Healthcare & Life Sciences.
Methodology:
Filtered for startups with 5+ entries for statistical significance
Searched titles for commercial keywords (CEO, founder, revenue, sales, etc.)
Found highest commercial focus in Built Environment (76.9%) and Manufacturing (71.4%)
Lowest commercial focus in Healthcare (36.7%)
Focused only on verticals with sufficient sample sizes (5+)