The opportunity extends across devices, memory systems, data centers and communications networks. Every repeated copy, transfer and retrieval consumes some combination of capacity, time and energy. An efficient implementation licensed into the platforms that perform those operations can earn revenue across successive product generations.

Spending or distribution indicator	Verified scale	Commercial meaning
Worldwide IT spending, 2026 forecast	$6.317T	The broad economic environment for data infrastructure 33
Data-center systems, 2026 forecast	$787.99B	Equipment purchases exposed to memory, storage and data movement constraints 33
Memory semiconductor revenue, 2026 forecast	$633.3B	The scale of the component market; price inflation contributes to growth 34
Global data-center investment, 2024	About $500B	Capital committed to physical infrastructure 51

Size the paid product inside the spending

These figures describe overlapping markets at different supply-chain levels. They establish the scale of the problem. The licensing model then prices a defined product: a covered chip, an active device, an accelerator runtime, a controller, an enterprise deployment or a funded national program. This creates a route from broad industry demand to an auditable royalty statement.

Four pools of customer value

Storage: more useful data per physical byte and fewer replicated bytes. Transmission: fewer eligible bytes crossing a paid or constrained link. Inference and hardware memory: less eligible data movement and a smaller working set where the decoder can sustain the required rate. Persistent knowledge: more relevant evidence per request, less repeated context assembly and fewer stale or unsupported answers.

The same efficiency can support lower unit costs or more output from an existing budget. Procurement should measure both; capacity released does not automatically appear as a reduced invoice.

The strategic ambition is an IP position inside the infrastructure that stores, moves and uses the world’s information.

The buyers are already investing at scale

The target accounts own the distribution channels and infrastructure budgets that can turn a successful implementation into a large recurring licensing business.

Buyer evidence	Reported amount or deployment	Why it matters
Amazon / AWS	AWS revenue $128.7B in 2025; Amazon announced about $200B companywide 2026 capex 41	Sell measurable capacity and operating efficiency into custom silicon and cloud infrastructure
Microsoft	$115.948B FY2026 cash additions to property and equipment 40	Azure efficiency can affect an enormous installed and expanding infrastructure estate
NVIDIA	$193.7B FY2026 data-center revenue 37	Accelerator platforms can distribute a qualified implementation across cloud and enterprise customers
AWS / NVIDIA	Two million additional GPUs planned for 2027–2028 55	A concrete example of deployment at millions-of-units scale

Amazon’s capital plan covers more than AWS, and NVIDIA’s revenue is supplier revenue. These measures are not summed as customer savings or a single addressable budget. Their relevance is the purchasing power and distribution reach of the proposed licensees.

Turn infrastructure scale into a purchasing decision

The first proposal to a hyperscaler should specify an eligible fleet, a measured reduction in cost per successful workload and a license whose price leaves the buyer a substantial share of that improvement. A $600 annual license across one million covered accelerators produces $600M recurring revenue. That is a proposed commercial illustration, not a published contract price.

At an illustrative $20,000 avoidable annual cost per accelerator, a verified 25% efficiency benefit creates $5,000 of annual capacity value. A $600 license leaves $4,400 per accelerator before any additional unmodeled integration expense. The inference example later in this plan shows the performance assumptions required to generate that benefit.

The scale comes from the platform agreement

One design win can reach multiple products, regions and refresh cycles. The agreement must identify deployment entitlements, royalty reporting, permitted subsidiaries and the next generation’s rights. Broader distribution then grows revenue without requiring a direct contract with every end user.

A global infrastructure market.

A billion-dollar licensing thesis.

NEOMORPHIC™ SSI · MARKET & COMPETITION

Phoenix targets the byte-related cost of storing and transmitting data. NEOMORPHIC Memory targets the cost of unreliable access to enterprise knowledge. Enterprise, operator and OEM licensing create routes into these budgets.

Market / source	2025 $B	2030 $B	CAGR / period
Telecom services 2	1,900.00	2,460.00	5.23% / 2025–30
Video streaming 3	811.37	1,816.85†	17% / 2026–34
Satellite communication 4	66.19	113.04	11.4% / 2026–30
Satellite internet 5	14.26	32.43†	17.85% / 2026–31
5G satellite communication 6	6.80	20.59	24.8% / 2026–30
Content delivery networks 7	29.94	77.94	21.1% / 2026–30
Mobile CDN 8	45.54	116.94	20% / 2026–30
Cloud storage 10	161.28	400.67†	19.3% / 2026–34
Enterprise data management 11	123.10	222.28†	12.8% / 2026–33

All values are nominal USD billions. † Derived 2030 values; other 2030 figures are published. CAGR periods are source-specific. These sectors overlap and are not summed. Mobile CDN and general CDN come from differently scoped studies and cannot form a single hierarchy.

Demand is real; serviceability needs a customer-level model

Ericsson’s 515 EB/month forecast applies to 2031, including fixed wireless access. 1 Actual codec serviceability depends on the customer’s data, baseline compression, endpoint access and variable bill. The next page makes the revenue bridge explicit.

Companion: 3sky.ai/deck

Small capture.

Billion-dollar annual revenue.

Management’s $143B codec-addressable 2030 pool frames the Phoenix opportunity. The percentages below represent annual vendor revenue capture from that reference pool.

Phoenix revenue capture	Annual revenue / 2030 pool
0.1%	$143M
0.5%	$715M
1.0%	$1.43B
2.0%	$2.86B
5.0%	$7.15B

The commercial thesis

A 0.5–1% capture range represents $715M–$1.43B annual revenue. At 5%, the opportunity is $7.15B annually. The $143B denominator is a management sizing assumption, separate from the cited third-party sector forecasts.

The lead operating plan starts in Year 1

Phoenix targets $115.5M in the first 12 commercial months, rising to $473.1M in Year 2 and $1.262B in Year 3. Company revenue, including SSI/Memory and implementation, reaches $155M, $611.7M and $1.605B.

This contract model combines annual enterprise scope, eligible operator traffic and OEM platform/covered-unit fees. Savings-share pricing is included within operator contract economics. The capture table measures vendor revenue as a share of the reference pool.

Distribution supports reach

An enterprise license monetizes a large data estate. An operator agreement reaches multiple eligible flows. A supported OEM implementation can reach millions of covered units. An open format or reference implementation can support interoperability, while optional enterprise capabilities, optimized implementations and explicit commercial agreements generate paid revenue.

The capture table and operating plan are complementary views, not additive revenue streams. The 2030 market reference is distinct from the launch-year timeline.

Billion-dollar IP businesses already exist

Arm reported $4.92B of FY2026 revenue, including $2.61B of royalties and $2.31B of license revenue. That is an operating precedent for monetizing technology through other companies’ hardware distribution. It supports the business-model category, while NEOMORPHIC’s own design wins and prices remain to be secured. 50

Groq and NVIDIA announced a non-exclusive inference-technology licensing agreement in December 2025. The announcement confirms that a leading accelerator company can license external inference IP; it does not disclose a price to use as a NEOMORPHIC valuation or fee benchmark. 39

A repeatable commercial structure

Paid evaluation and integration. Deliver a workload benchmark, supported SDK and customer-specific integration under a fixed scope. Development fees fund engineering and establish the performance baseline.

Platform rights. License the optimized implementation, firmware or qualified hardware block for defined products, architectures and fields of use. Annual minimums support multiyear planning and reserve engineering capacity.

Running royalties. Charge per covered shipped component for embedded IP, or per active deployed unit per year for a supported runtime. Usage reports, audit rights and agreed exclusions make the entitlement measurable.

Generation expansion. Negotiate rights for additional chip families, operating systems, workloads and regions. A renewal or new generation can expand the paid scope without repeating the entire customer-acquisition process.

Preserve the IP while licensing its use

The proposed model licenses specified usage rights while retaining ownership of the underlying technology. Contracts separate background IP, customer-funded modifications, maintenance, source escrow and any exclusivity. Exclusivity should have a narrow field, a defined term and minimum commercial commitments.

An open format or reference implementation can support adoption. The paid value must reside in enforceable rights and differentiated implementation, performance, support or operational control. Patent filings, granted claims and freedom to operate require an IP register; this plan does not infer worldwide exclusivity from a portfolio description.

Win the workload,

then expand the distribution.

Phoenix must beat credible existing codecs economically. NEOMORPHIC Memory must improve a customer’s knowledge workflow against established retrieval and memory systems.

Competitive set	What the customer already gets	What NEOMORPHIC must prove
Zstd / LZ4 / Brotli 22, 23, 24	Capable lossless codecs and established tooling	Better net economics on matched data
nvCOMP 25	GPU-based compression pipelines	Useful ratio and speed on matched hardware
Video standards / VVC 26	Quality, device support and working implementations	Separate media qualification; no raw-to-video shortcut
Mem0 / Zep / Letta 27, 28, 29	Persistent or stateful agent-memory approaches	Accurate knowledge updates and complete-task value
Pinecone / retrieval stacks 30	Persistent indexing and retrieval	Relevant, verified evidence at competitive cost
Inference optimizers 19, 20, 21	Efficient attention, KV-cache or numeric representation	Complementary value; distinguish working memory from knowledge

Proposed differentiation

Phoenix: lattice-native lossless encoding integrated into qualified storage and transfer paths. Memory: fast associative retrieval combined with canonical records, version history and controlled access. Reliability: evaluate whether the final answer uses the correct current evidence, not just whether a record can be recalled.

Adoption plan

Launch paid enterprise, operator and OEM scopes on supported implementations. Use design partners and matched evaluations to strengthen deployment economics while the wider qualified pipeline advances. Scale the stable decoder, SDK and support model across contracted distribution. Proprietary licensing requires a documented IP and freedom-to-operate review; absence from a patent pool is not itself a proven competitive advantage.

Company presentation: 3sky.ai/deck

The customer savings that fund the license

Phoenix creates commercial value by reducing eligible physical bytes. The contract measures savings against the customer’s deployed baseline, including all encoded state and processing costs. The examples below are editable planning cases in Value Economics, not published product benchmarks.

Storage: $30M retained customer benefit

Consider 100 PB of logical information with three physical copies at an avoidable owned-capacity cost of $0.02 per GB-month. The annual capacity cost is $72M. A measured 50% incremental reduction would avoid $36M of that cost. After $3M of annual encode/decode operations and a $3M license, the buyer retains $30M per year.

This example uses physical replicas and a physical capacity cost. A managed storage price that already includes redundancy should not be multiplied by the same replica factor again. Requests, transfers and other service charges remain separate; S3’s pricing structure illustrates these distinct charge categories. 52

Transmission: $175M of value before the license

At 100 eligible EB per year, $0.01 avoidable cost per GB, 25% incremental byte reduction and 80% cash realization, gross realized savings are $200M. Subtracting $0.00025 of codec cost per eligible GB leaves $175M. A 25% share of that benefit produces $43.75M annual Phoenix revenue, leaving the buyer $131.25M.

The same unit economics underpin the original lead model’s 40 EB Year 1 operator scope and $17.5M operator revenue. More contracted, eligible traffic scales revenue; the entire network’s traffic is not automatically licensed or compressible.

What the 130:1 result contributes

Management’s reported raw-data ratio corresponds to a 99.23% payload reduction on that workload. It is the high-upside technical signal motivating customer qualification. The commercial ratio must be measured on the selected workload relative to its existing representation. A blended estate’s benefit depends on the eligible share, achieved reduction and actual cost realization.

The world-scale opportunity is repeated deployment of a favorable unit economic result. It does not require assuming that every global data bill falls by the raw-data compression ratio.

Memory efficiency becomes inference capacity

AI inference spends time computing and moving information through memory. Reducing the right data movement can improve throughput, while persistent knowledge can reduce repeated prompt construction and the amount of irrelevant history supplied to the model.

A transparent hardware-performance example

Suppose memory movement accounts for 60% of elapsed time on a measured workload. If an implementation halves that eligible movement and adds overhead equal to 5% of original elapsed time, the new elapsed-time ratio is:

1 − (60% × 50%) + 5% = 75% of baseline time.

That represents a 25% time reduction and approximately 1.33× throughput under unchanged bottlenecks. These are planning calculations, not NEOMORPHIC benchmark results. If compute, interconnects or decoding becomes the limit, the measured outcome will differ.

At one million covered accelerators and $20,000 of avoidable annual cost per accelerator, a 25% useful efficiency improvement represents $5B of annual capacity value. A $600 annual runtime license generates $600M revenue and leaves $4.4B of customer value before other integration expense. A procurement case must establish the avoidable cost and how freed capacity is monetized.

Distinguish the working sets

For a simplified transformer, KV-cache bytes scale with two tensors × layers × KV heads × head dimension × bytes per element × cached tokens × concurrent sequences. A 32-layer example with eight KV heads, head dimension 128, two-byte elements and 32,768 tokens uses about 4 GiB per sequence for KV tensors alone. At 16 concurrent sequences that is 64 GiB before weights and other overhead.

PagedAttention, FlashAttention and quantization address different parts of this problem. NEOMORPHIC must compare against optimized baselines and preserve quality; lossless raw-data compression is not an established KV-cache ratio. 19, 20, 21

Persistent memory reduces repeated work

Retrieve authoritative evidence, enforce access and version rules, and send the relevant subset to the model. Measure retrieval overhead, prompt tokens, time to first token, output latency and cost per correct task. Titans provides another research example of learned long-term memory; it is a comparator, not evidence that NEOMORPHIC has already surpassed it. 58

Research notes and works cited

Calculation notes

Derived 2030 values use: streaming $969.56B × 1.17^4; satellite internet $38.22B ÷ 1.1785; cloud storage $197.8B × 1.193^4; enterprise data management $137.3B × 1.128^4. Sources were accessed on 8 September 2026. These are interpolations, not independently published 2030 figures.

1 Ericsson. Mobile network data traffic forecast. Current forecast, accessed 8 September 2026.

2 Mordor Intelligence. Telecom Services Market. 27 June 2025; 2025–2030 forecast.

3 Fortune Business Insights. Video Streaming Market. 24 August 2026; 2026–2034 forecast.

4 The Business Research Company. Satellite Communication Global Market Report. 2026 edition; accessed 8 September 2026.

5 Mordor Intelligence. Satellite Internet Market. 2026–2031 forecast; accessed 8 September 2026.

6 The Business Research Company. 5G Satellite Communication Global Market Report. 2026 edition; accessed 8 September 2026.

7 The Business Research Company. Content Delivery Network Global Market Report. September 2026; accessed 8 September 2026.

8 The Business Research Company. Mobile Content Delivery Network Global Market Report. 2026 edition; accessed 8 September 2026.

9 MarketsandMarkets. Content Delivery Network Market. 2025–2030 forecast; accessed 8 September 2026.

10 Fortune Business Insights. Cloud Storage Market. 2026–2034 forecast; accessed 8 September 2026.

11 Grand View Research. Enterprise Data Management Market. 2026–2033 forecast; accessed 8 September 2026.

Primary technical references 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 are linked at their point of use and listed in the accompanying business plan. Market values are forecasts with differing definitions; operating revenue uses customer cohorts rather than a sum of these markets.