Assessment 2 - Step 3 - BrainChips Products - Contribution Margins and Resource Constraints
- Feb 7
- 4 min read
For this step, I’m using creative licence, in assuming my company is profitable. In reality, they are currently sinking around $20M per annum into Research and Development, while revenue is in $100,000’s.
Edge AI Box
Sale price: US$995.00
Contribution Margin = Sales Price less Variable Cost
I’m assuming a variable cost of 70% = $696.50
$995.00 - (70% X $995.00) =
$995.00 - $696.50 = $298.50
BrainChip, Inc. (n.d.). Akida™ Edge AI box [Image].
M.2 Card B+M Key
Sale Price US$249.00
I’m assuming a variable cost of 65%, as lower value products often require a higher contribution margin to make the sale worthwhile = $161.85
$249.00 - (65% X $249.85) =
$249.00 - $161.85 = $87.15
BrainChip, Inc. (n.d.). M.2 card (M-key) [Image].
1 Week Cloud Access
Sale price: US$995.00
80% variable cost assumption due to the easy scalability of this software.
$995.00 – (80% X $796.00) =
$995.00 - $796.50 = $199.00
BrainChip, Inc. (n.d.). One-week cloud access [Image].
Why the contribution margins differ across the products and service.
A 30% contribution margin, for the Edge AI Box is a reasonable assumption, because it is a full hardware system, which includes a general‑purpose processor, Two Akida AKD1000 neuromorphic processors, memory and storage chips, Wi‑Fi, a circuit board, enclosure, heat management, assembly, and packaging according to (BrainChip, 2024). These components and manufacturing steps increase variable costs, which limits how much of the sale price remains as contribution margin. Because the Box is a complete computer, its margin is likely lower than more simple products.
I believe the Akida M.2 Card (B+M Key) has a slightly higher contribution margin at 35% as it is a far simpler construction, with only one Akida AKD1000 chip and a small processor (BrainChip, 2025). Compared with the Edge AI Box, it has fewer components and fewer production steps, so its variable costs are likely lower and a higher portion of the selling price remains as contribution margin.
Even though cloud‑based products offer high scalability, BrainChip’s 20% contribution margin, reflects that supporting a specialised AI cloud platform requires highly paid engineering and support staff. BrainChip’s cloud access offers not only remote access to Akida hardware for model testing, but support for model evaluation and direct communication with engineers and technical specialists to assist with troubleshooting and performance tuning (BrainChip, 2026). These experts are well paid, and support costs rise as more customers use the service.
Why BrainChip produces products with different contribution margins.
To support the customer journey from testing, to prototyping and finally deployment. BrainChip’s products each serve a different purpose in the AI adoption process building a complete ecosystem.
Akida Cloud is the rent before you buy product, lower the barrier for new users by removing the need for hardware and installation.
M.2 Card is the build and test on your own device product, used for experimenting and developing early prototypes.
Edge AI Box is the full adoption and deployment solution, used in real world applications across retail, healthcare, automotive, and industry.
Each stage requires a different pricing structure, resulting in different contribution margins.
Why not only produce the product/service with the highest contribution margin?
Whilst it may initially seem logical for a company to focus only on the product with the highest contribution margin, this approach would not support BrainChip’s overall strategy or the needs of its customers. Although the Akida M.2 Card has the highest contribution margin at 35%, relying solely on this product would limit BrainChip’s ability to guide customers through the full development and deployment process. The M.2 Card enables users to build and test prototypes for their Edge AI solutions, but customers would then be forced to design their own deployment hardware. For many organisations, this would introduce too much friction, technical difficulty and additional cost, potentially discouraging them from adopting the Akida platform completely.
BrainChip’s objective is not simply to sell development cards but to drive widespread adoption of the Akida Edge AI Box. This is the product intended for large‑scale deployment across numerous devices and applications. In this ecosystem, Akida Cloud access and the M.2 Card serve as stepping stones that help customers explore, test, and refine their models before transitioning to the high‑volume deployment hardware. Producing only the product with the highest contribution margin would undermine BrainChip’s growth strategy, limit customer success and reduce the overall scalability of the Akida technology platform.
The contribution margin is a valuable metric as it shows how much each product/service contributes towards covering fixed costs and subsequently how profitability may increase as BrainChip reaches scale. Contribution margins help management evaluate whether each product is priced appropriately relative to its cost structure. For example, the M.2 Card’s higher CM indicates its pricing is favourable and may allow room for discounted promotions. In contrast, cloud access has a comparatively lower CM, informing management that excessive discounting would erode profitability.
Resource Constraints
A significant resource constraint that BrainChip may face is the inability to recruit and retain suitably skilled staff. The company relies heavily on specialised engineers and AI experts to develop new products and provide technical support. BrainChip risks being unable to hire or keep these employees, due to well-funded competitors competing for them. This limits BrainChip’s ability to scale its cloud service, complete product development and support new customers effectively. This constraint can directly impact product quality, slow down innovation and delay market entry for new products.
Limited availability of highly skilled engineers, caps how fast BrainChip can scale both its Akida Cloud service and its hardware customer support. Because Cloud access depends on specialist staff to tune models and resolve complex issues, headcount shortages could slow the onboarding of new users and delay new feature rollouts. The same talent pool is also needed to support M.2 Card and Edge AI Box customers with integration and application engineering. When specialists are scarce, post‑sale support can become a bottleneck that forces BrainChip to pace sales and production to what the team can reliably support. Even if demand finally takes off, growth must be aligned with engineering capacity, making talent availability a limiter on how much Cloud usage and hardware volume, BrainChip can sustainably deliver.
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