The Capture Rate, Anchored

How TAM and Product Market Fit Should Be Sequenced. A Companion to Step Two of How to Launch a Token

TAM is Diagnostic

A total addressable market figure becomes useful once it converts into a required capture rate against current revenue. GEODNET's positioning materials cite a $30 billion core market for centimeter level positioning data (see case study here). Against approximately $11 million in annualized revenue, that market implies a capture rate near 0.04% (DefiLlama). A number that small signals real headroom for growth. Cited on its own, a $30 billion or $100 billion market is narrative. Run through a capture rate and checked against growth trajectory and customer concentration, it becomes a diagnostic tool.

The distinction matters beyond the math. A market size on its own asks the market to take a projection on faith; a capture rate against real revenue provides a tangible anchor.

Product Market Fit as Two Curves

In DePIN, Product market fit runs on two curves. The contributor curve tracks whether the people supplying the network, node operators, drivers, GPU owners, keep getting paid enough to stay online. The customer curve tracks whether the people buying what the network produces choose it over the incumbent, at a price or quality the incumbent can't match. These two curves can and often move independently. A network can show contributor growth for years while the customer curve stays flat, and the contributor chart alone won't show that gap.

Proof, Then Projection, in Five Protocols

The sequence a protocol follows matters as much as its current numbers. A protocol that confirms both curves before sizing its TAM gets a capture rate calculation that means something. A protocol that sizes TAM first spends the following stage correcting the product instead of scaling it.

GEODNET - got it right the first time

GEODNET confirmed both curves before its TAM figure carried weight. Contributor growth came from a working reference station network producing positioning data used by real customers. The customer curve came from measurable revenue, not a projection. Once both curves were established, the $30 billion core market for centimeter level positioning data became a number worth converting. The sequence is what makes this capture rate trustworthy. GEODNET proved its PMF first and then worked on scaling it. (See case study)

Helium - building the plane while flying

Helium's 2019 whitepaper cited the Internet of Things as an $800 billion industry, with spending predicted to reach $1.4 trillion by 2021. That figure described the entire IoT industry, not IoT connectivity spend specifically, and the whitepaper never separated the two. The IoT connectivity market on its own was estimated at $10.16 billion in 2024, with a 20 percent compound annual growth rate projected through 2033 (imarc), nearly two orders of magnitude smaller than the whitepaper's framing. Helium priced its opportunity against the broader figure before the narrower one existed to check it against. (See case study)

Contributor growth moved first after that. Hotspot deployment scaled into thousands of cities ahead of enough paying customers to use that coverage. The customer curve took years to catch up to the pricing the network had already set. HIP 143 gave Nova Labs standing authority to negotiate carrier rates on the network's behalf, cutting the payer rate from $0.50 per gigabyte to roughly $0.10, an 80 percent reduction after the original pricing proved too generous to sustain. HIP 149 added a floor and a cap on top of that correction. By the second quarter of 2026, network volume was growing 20 percent while revenue declined 14 percent over the same period. Both HIPs are the cost of pricing against a market size before the customer curve was proven, paid in governance cycles instead of paid upfront. (See further analysis here)

HIP-150 followed in late August 2026, raising the deployer earnings target minimum and adding reward multipliers at the network's highest-traffic venues. It extends the same pattern as HIP-143 and HIP-149, a third adjustment to the customer curve rather than a one-time fix. HNT found a low of $0.167 in mid-August 2026, after HIP-149 was already live. The token is trading above that low. Whether that recovery reflects the corrections taking hold or ordinary volatility in a small-cap token is a question the market is still pricing in.

Render - the anatomy of a successful horizontal expansion

OTOY was founded in 2008 by Jules Urbach, building OctaneRender, a GPU-accelerated rendering engine. OctaneRender was adopted by visual effects studios, animators, and content creators over the following years earning an Academy Award for the underlying technology (OTOY). By 2017, OTOY had production credits with major studios and had done rendering work connected to Facebook's VR camera pipeline (Forbes). That customer base predated both the token and it’s later AI compute TAM story.

The move to a token was a response to a supply constraint. Urbach had seen as early as 2009 that rendering demand could eventually outgrow what OTOY's own infrastructure could keep up with. He patented a peer-to-peer model where outside GPU owners could contribute compute and get paid for it (Forbes). That model only became practical once blockchain-based token payments existed, which is why the token launched in 2017, eight years after Urbach first conceived it (Render). The customer curve came first. The token solved for supply once demand had already outrun it.

In 2025, network governance approved RNP-019, after an initial rejection, extending the same GPU marketplace into AI compute and inference work (Messari). By 2026, AI workloads accounted for roughly 35 to 40 percent of the network's activity (hoge.gg), with trailing revenue under the network's burn and mint model near $2.04 million annualized (DefiLlama). The TAM conversion into AI compute inherits a customer curve that was already proven, rather than starting the sequence over in a new vertical.

Wingbits - too early to prove but moving in the right direction

Wingbits entered a market with an established structure. Commercial flight tracking networks including Flightradar24, FlightAware, and ADS-B Exchange already depend on globally distributed ADS-B receivers to source their data. The large majority of that network is volunteer-operated, individuals who install and run receivers for free, without compensation for the position data they contribute (Wikipedia, FlightAware, Aviospace). Wingbits enters that same base of existing activity and pays contributors for data collection that has historically gone uncompensated.

Wingbits confirmed both curves early. Contributor deployment and customer usage on the aviation tracking network both show real activity, not just hardware rollout. Flight tracking data has a uniform, bounded customer base: airlines, air traffic control, logistics and insurance firms, and existing aggregators, a finite and enumerable set rather than a broad consumer market. That structure is what makes bottom-up extrapolation reliable here. Once realized pricing and the addressable customer list are known, the TAM can be built directly from Wingbits' own data rather than borrowed from a third-party estimate. Third-party research on flight tracking as a category currently ranges from roughly $500 million to $16 billion depending on the firm and what each one counts, a spread wide enough that none of them would be worth citing here.

GridEcon has advised Wingbits on tokenomics design. This section describes Wingbits' current stage in the sequence. It does not offer an investment view on the protocol.

Hivemapper - impressive contributor curve, still figuring out demand

Hivemapper's contributor curve was strong from early on. Dashcam contributors mapped tens of millions of road kilometers, and the network signed named enterprise customers, including Lyft, Volkswagen's autonomous vehicle unit, NBCUniversal, Mapbox, and HERE (Blockworks). The weaker curve is the customer side, and the reason isn't low adoption. Mapping data tends to get bundled into a larger platform rather than sold as its own product, the way Google folds Maps into a broader ecosystem instead of pricing it independently. Hivemapper's early numbers showed contributors would participate. They never confirmed customers would pay for unbundled mapping data on its own. Protocol revenue tracked by DefiLlama fell from roughly $294,000 in the fourth quarter of 2025 to $130,000 in the first quarter of 2026, then to nearly $25,000 in the second quarter. Named customers exist. The revenue pattern is what an unconfirmed customer curve looks like after initial pilots stop expanding. Hivemapper’s PMF, not the TAM conversion, is still the open question. (See case study)

The Order Behind TAM

The sequence matters more than the current snapshot. GEODNET and Render both confirmed a customer curve before converting a market size into a capture rate, and the conversion holds up because of that order. Helium sized its market first. Two governance cycles then corrected the price against a customer curve that wasn't there yet. Wingbits confirmed both curves early, and the market it sells into is uniform and bounded enough to extrapolate a TAM directly from its own pricing rather than a third-party estimate. Hivemapper never confirmed the customer curve would hold up outside a bundle. That gap is the one a capture rate calculation can't fix, regardless of how the market size gets sized.

Protocols preparing a token launch should check their own sequence before citing a TAM figure. Confirm the customer curve exists as a standalone reason someone pays, separate from the incumbent's bundle. Only then convert the market size into a capture rate and check it against growth trajectory and customer concentration. Where no reliable third-party market size exists, a protocol with confirmed demand can extrapolate its own from realized data rather than borrow a number from a source that doesn't hold up.

A protocol that follows this sequence ends up with something more useful than a defensible number. It has a story anchored in something any stakeholder verify and hold onto, not a projection asking to be taken on faith. A market size cited before that confirmation is a bet on a curve that hasn't been drawn yet.

Disclosure

GridEcon advises a commercial Helium deployer and separately has advised Wingbits on tokenomics design. Both protocols are used in this piece as examples within a broader analytical framework, not as endorsements of investment merit. Where Helium is discussed, GridEcon's advisory relationship is with a deployer operating on the network, not with Nova Labs or the Helium Foundation directly. Where Wingbits is discussed, GridEcon's advisory relationship was limited to tokenomics design and does not extend to an investment view on the protocol. This piece reflects GridEcon's independent analysis and is not investment advice.