As a Business Development Specialist at Conversation Design Institute (CDI), I help organizations around the world create human-centric AI Assistants, such as chatbots and voice assistants. 

At CDI, I work closely with tech partners, agencies, and other key players in the industry to offer high-quality e-learning programs and live sessions for teams and individuals. I have trained hundreds of professionals in strategy, design, and development for AI Assistants, using our proven workflow and best practices.

My goal is to empower more people to design, deploy, and manage valuable conversational experiences.

Benjamin Taylor

Hi everyone,

If you would like to connect with me and we don't know each other PLEASE reach out to contact@thiswayglobal.com and share what you would like to talk about so you can be connected to the right person on my team. You will have far greater success than reaching out to me here in LinkedIn.

My team will accept public speaking requests, customer invites and investor communications here.

Otherwise, feel free to "follow" me 

Hope you have a great day!
Angela

I am the Founder/CEO of ThisWay Global, an international, VC-backed HR tech company that was incubated at ideaSpace – University of Cambridge, UK, with new offices now in the US. ThisWay's team is comprised of experienced entrepreneurs, global PhD’s in the areas of machine learning and AI, as well as talent from across the globe. 

Over the last twenty years, I have founded and built three companies, in sectors that range from architecture to the licensing industry. The common core in each has been my ability to build consensus amongst stakeholders, whether they be individuals, businesses or international governments.

I am a recognized thought leader on the topics of "Diversity and ROI" and "AI in the new world of work."

Without knowing my history, it would seem that the leap from land development to tech development is a big one. However, ThisWay is the manifestation of my drive to solve big problems. As early as 1992, when I founded the Texas A&M College of Architecture Career Fair, through my award-winning land development plan in the early 2000s, I have always sought to find meaningful solutions to big challenges.  

How we got to where we are today:
IBM Silver Business Partner
Google Accelerated Founder and Startup
Business Journal Finalist - Women of the Year 2023
ideaSpace-University of Cambridge: Alumni Founder
Institute of Enterprise and Entrepreneurs: Lifetime Fellow
Cambridge Wireless: Founder Member
Enterprise Nation: Entrepreneurship Mentor for both Growth and Hiring

Get in touch with me here or via either of the channels below:

Angela Hood

A genome-wide association study that normally takes months 

Finished in 20 minutes with Claude.

That's not a typo. 

Researchers at Stanford built Biomni, an AI agent that connects Claude to hundreds of databases and scientific tools. 

One test: data cleaning, analysis, statistical modeling, results. 

Twenty minutes.

Another project that would take a human expert three weeks finished in 35 minutes.

The bottleneck in scientific research has never been thinking. 
It's been data. Months spent cleaning messy datasets. Weeks parsing through literature. Grad students arguing over spreadsheets about which genes matter.

Claude is collapsing that part of the pipeline.

MIT's Cheeseman Lab uses Claude to interpret patterns from millions of CRISPR gene knockouts. The lab director can recall about 5,000 gene functions from memory, but Claude still catches connections he misses. Stanford's Lundberg Lab is using it to predict which genes matter based on molecular properties instead of guesswork.

Anthropic's AI for Science program gives free API credits to researchers working on high-impact projects. 

Claude for Life Sciences now connects directly to Benchling, PubMed, and 10x Genomics. 

They're basically removing every excuse to stay stuck on busywork.

It's only a matter of time before we see an AI-assisted breakthrough.  Fingers crossed for this year!

What do you think it will be?

AI for B2B expert/ Forbes 50/50 List / INC Magazine Founder / Google Accelerated / IBM Think Keynote / Outstanding Alum@TAMU & Founder/Alum Uni of Cambridge: ideaSpace Founder/Alumni

Microsoft is giving 12 months of Copilot and LinkedIn Premium Free 

To every college student.

The headline: "Democratizing AI literacy and preparing the workforce."

aka whoever owns the tool you learn on becomes the tool you lobby for at work.

Google did something similar last year with Gemini.  Plus think back to their 15-year campus strategy. Free Gmail, Docs, Drive for students. Now Google Workspace is the default in 90% of schools and a good chunk of enterprises. 

That didn't happen by accident.

Microsoft is not just handing out Copilot licenses. They're bundling LinkedIn Premium, launching global educator communities, creating new credentials, building AI-powered lesson planning tools. 

They're structuring the entire learning experience around their stack.

The free part is real. The value is real. Students and educators genuinely benefit.

Microsoft's betting that a $150 lifetime value of free subscriptions converts to years of enterprise spending down the road. 

Given their track record with enterprise bundling, that math probably works.

Google vs Microsoft continues on the college campus front.  Which stack is your school currently using?

ThisWay Global, maker of Amalgamy.ai, is now an approved OEM vendor with TD SYNNEX.

Amalgamy.ai is built for anyone selling to companies that operate data centers. 

This proprietary data and compute orchestration platform is platform-neutral and designed specifically for heterogeneous data center clusters. 

It removes the bottlenecks associated with data movement and scheduling, enabling more efficient AI, HPC and data center operations while also maintaining security. 

Because it's agnostic, it works across all data center environments, regardless of processor brand or type.

The ugly secret out there is the amount of wasted capacity and its impact on ROI.  How do you actually run that hardware at full capacity?  With Amalgamy.ai.

TD SYNNEX partners can now leverage the Amalgamy.ai software suite and appliance for all CPU/GPU/TPU, storage, and accelerator deployments. By perfecting the fusion of data and metal, data orchestration and scheduling bottlenecks will be solved in one motion.

If you're a TD SYNNEX reseller, Systems Integrator, or services partner working with customers investing in GPUs, accelerators, or high-performance storage,let’s deliver a high value solution together this quarter.

We're looking to collaborate on opportunities to fuse data and infrastructure together.

www.thiswayglobal.com

#TDSYNNEXUS #ThisWayGlobal #AmalgamyAI

ThisWay Global has had an incredible start in 2026 and we are hiring!
We will start posting the jobs here next week and on our company LI page.
As we are an AI and infrastructure acceleration company, all jobs are technical in nature. Expect open roles in these catagories:
#sales engineers
#software engineers
#sales execs with experience in software, data center, infrastructure and construction industries

We know there are a lot of people being layed off from the companies featured in this Crunchbase announcement. and we are fully remote, hiring US based employees.  So comment here to be sure you see all the jobs as they open up over the coming weeks.

Open article: Tech Layoffs: US Companies With Job Cuts In 2024 And 2025 by news.crunchbase.com

Tech Layoffs: US Companies With Job Cuts In 2024 And 2025

OpenAI just turned coding into management.

You're now the PM of your robot employees.

The Codex macOS app lets you spin up multiple AI agents working on 
different parts of your codebase at once.

Each agent gets its own git worktree, so they work in parallel without colliding. You switch between them, review changes, and merge when ready. It's more about coordination than writing code faster.

Skills are bundled instructions that connect Codex to external tools and complete full workflows.

- Deploy to Vercel.
- Triage bugs in Linear.
- Generate images for game assets.
- Create documents.

OpenAI built an entire Mario Kart clone using a single prompt and 7 million tokens, costing $175 in API fees.

Backing up claims that the unit of work may change.

Then there are Automations, which run tasks on a schedule. Daily issue triage. Finding CI failures. Generating release notes. The work your team forgets until it becomes a crisis is now delegated to a background agent.

Some early access users claim, "I feel more like a manager than ever, but I also feel way more productive than ever."

Managing an agent squad has been the dream for a while now.

This gets us one step closer. 

Is anyone making the switch from Codex / Claude Code?

OpenAI just opened a request for proposals worth billions 

In domestic manufacturing contracts.

The deadline is June 2026. 

They're looking for US-based suppliers to build data center equipment, consumer electronics components, and robotics parts. 

They'll review rolling basis and fund the winners..

When the largest AI company in the world publicly commits to building supply chains domestically, other companies watch. Competitors follow. 

Investors fund the manufacturing startups that can compete. That's how momentum shifts.

OpenAI's Stargate initiative promised 10 gigawatts of AI compute capacity. 

They're already halfway there with announced projects creating "good jobs and local economic growth" across the country. 

Now they're formalizing that preference for domestic manufacturing across the entire supply chain. Not just chips, but racks, cooling systems, power electronics, testing and assembly.

Large-scale AI infrastructure doesn't have to be built offshore. It can be built here, faster, more resilient, and with domestic leverage.

Whether that actually happens at scale depends on whether US manufacturers can compete on cost and speed. 

Do you think domestic manufacturing can hit the price points needed to win against incumbent overseas suppliers, or will cost pressure force most contracts overseas anyway?

AWS turns 20 next month, and the party's getting expensive.

For years, cloud pricing felt inevitable,

Just another rising line on the spreadsheet. 

But 2026 flips that script. 

Amazon announced GPU pricing cuts up to 45% last year. Don't expect that again.

Energy costs are climbing. Data centers are power-hungry. Every major cloud provider (Amazon, Microsoft, Google) is dumping billions into AI infrastructure and training. 

They need revenue to fund it.

So who wins in 2026?

Organizations that double down on FinOps. 

The ones treating cloud spend like a business problem. Accurate tagging, reserved instances, spot pricing, renegotiating contracts. Those companies will carve out 10-20% savings and keep capacity while competitors absorb price hikes.

Everyone else gets squeezed.

The flip side: companies scrambling to justify cloud bills might also realize some workloads don't need premium cloud anymore. 

Neocloud providers (specialized AI-optimized environments) are undercutting conventional cloud on certain jobs. 

That's pressure AWS, Azure, and Google didn't have before.

The 20-year cloud story started with cheap compute changing everything. 

This chapter is about who can optimize when the easy savings are gone.

Are you in a position to absorb cloud cost increases, or are you already hunting for optimization levers?

New skills can get you a 15% raise. 

But companies are cutting entry-level jobs where people learn those skills. 

So who's actually going to get them?

Jobs that need new skills pay about 3% more. 

Stack four or more of those skills and you're at 15% premiums in the UK, 8.5% in the US. 

That extra money doesn't just sit there, it cycles through local economies when people spend it.

AI skills pay well, but occupations exposed to AI have seen 3.6% lower employment over five years. 

Companies using generative AI are making a clear choice: automate what entry-level workers used to do, hire fewer of them.

So now we've got a labor market that's splitting. High-skill workers are doing fine. Everyone in the middle doing routine work? That's where the squeeze is happening. 

And if you're young trying to get your foot in the door, there are fewer doors.

Finland and Denmark are putting real money into training programs. Other countries have mismatches, either tons of demand with nobody to fill it, or plenty of talent with nowhere to go.

It comes down to decisions about who gets access to training, how we handle competition, what safety nets look like, and whether education systems adapt.

Plus what are companies actually doing with the money they save from automation?

AI labs are basically grading their own homework. 

Miles Brundage spent seven years at OpenAI working on policy for AGI.

Now he's launching AVERI 

(AI Verification and Evaluation Research Institute) with $7.5M in funding and a blunt argument: 

Companies testing their own safety is insane. It's like your vacuum cleaner testing itself and promising it won't catch fire.

Here's how it works today. Companies conduct safety evaluations in-house. They publish whatever results they want. Nobody checks. Each lab decides what tests to run, how rigorous they should be, and what counts as safe.

Brundage and 30+ AI safety researchers published a research paper proposing "AI Assurance Levels," Which range from basic third-party testing all the way to "treaty grade" assurance for international agreements. 

They don't want to do audits themselves. They want to catalyze an industry of auditors from existing firms and new startups.

Who forces this? Insurance companies requiring audits before underwriting policies. 

Investors demanding verification before billion-dollar checks. 

The EU AI Act already hints at this direction for high-risk deployments.

But it's tricky to find auditors.  AI safety auditing requires rare skills and technical AI expertise plus governance knowledge. 

Everyone with that combination is getting lucrative offers from the companies they'd be auditing. 

Brundage's bet is building teams from audit firms, cybersecurity, AI safety nonprofits, and academia.

Dario Amodei says AI will replace every software developer within a year.

Demis Hassabis, who just won a Nobel Prize for his AI work, 

says current systems are "nowhere near" human-level intelligence.

Yann LeCun, the Turing Award winner who pioneered neural networks, goes further. 

He says LLMs will never achieve human-level intelligence. Not in 10 years, not in 50. Never.

At Davos this week, AI leaders couldn't agree on whether we're one year or one decade away from AGI.

Amodei claims we'll have Nobel-level AI scientists in two years and 50% of white-collar jobs will vanish in five. 

Sam Altman says we're already slipping past AGI toward superintelligence.

Meanwhile, Hassabis says we need "one or two more breakthroughs" and puts the timeline at 5 to 10 years. 

LeCun says the entire industry is "digging the same trench" with LLMs when we actually need completely different architectures.

LeCun's critique hits hard. If LLMs can't build world models or predict consequences, we won't get domestic robots or self-driving cars, let alone AGI. He left Meta over this exact disagreement.

The business world doesn't care. 

Cognizant estimates current AI could unlock $4.5 trillion in U.S. productivity. 

Trillions are flowing in regardless of who's right about AGI.

Does the actual timeline matter, or is the debate itself what makes the funding possible?

OpenAI is now predicting who's under 18 on ChatGPT. 

Based on behavior, account age, activity patterns, usage times. 

If the model thinks you're a teen, safeguards kick in automatically.

The safeguards make sense: no graphic violence, self-harm content, or material promoting extreme beauty standards. 

Parents get quiet hours, memory controls, distress alerts.

But here's the operational tension.

The model will get it wrong. Some adults will get flagged as teens. Some teens won't. When misclassification happens, you need a fast way to correct it. 

OpenAI built that, a selfie verification through Persona. 

Simple enough on paper.

But at scale, this becomes a support and trust problem.

If you're an adult locked into the teen experience, how fast can you actually get verified? What's the friction? Do you abandon the account or jump through hoops? If the system over-classifies, you're building resentment with users who have nothing to hide.

And if teens figure out the signals the model watches, activity times, account patterns. They'll just game it. 

The system becomes an arms race between OpenAI's refinements and user behavior modification.

The harder question for platforms: How do you manage this at millions of accounts? 

What's the cost of handling appeals and misclassifications? Does the support burden outweigh the safety benefit?

Age verification online is hard. Building the tech is one thing. Running it operationally is another.

How would you handle this if you were OpenAI's head of trust and safety?

Nvidia's $100 billion OpenAI investment was never actually promised.

Not quite. 

Jensen's walking back the September announcement by emphasizing it was "never a commitment." 

He's now saying Nvidia will invest "one step at a time" and that the current round won't approach $100 billion, but OpenAI invited them to invest up to that amount. 
 
Which makes sense we would immediately jump at the large figure and react. 

The Journal reported Huang had privately expressed doubts about OpenAI's spending discipline and questioned their business approach. 

"That's nonsense." In his own words.  

Nvidia needs OpenAI as a customer. OpenAI needs Nvidia's chips to exist. 

This strategic partnership will continue and I'm sure the actual investment numbers will still be substantial. 

With reported Q3 revenue of $57B, there's money to throw around here.

Gartner: 75% of firms will need a digital sovereignty plan.

But it's not about where your data lives. 

It's about control.

AI workloads are bringing this issue to a head. 

Storing data in a specific country was the first-order problem. Running proprietary models on that data, under a specific jurisdiction's laws, is a much harder one. 

For auditors and customers, simple data residency is no longer a sufficient answer.

We're seeing a clear shift in architecture. Sovereignty is moving from a bolted-on security control to a core design principle, embedded directly into the software stack. 

This looks like a customer-operated control plane, where identity, access, and encryption keys are kept strictly within the organization's boundary, even when running on a hyperscaler's hardware. 

The boundary becomes cryptographic and operational, not just geographic.

This changes the infrastructure discussion from choosing a cloud region to building a verifiable environment that you operate. F

or companies deploying proprietary AI against sensitive data, this kind of control is fast becoming a competitive requirement, not just a compliance checkbox.

What happens when AI stops waiting for your question, 

And starts investigating on its own?

OpenAI Prism embeds GPT-5.2 directly into your research paper. 

It reads your entire manuscript: structure, equations, citations, figures. Like a co-author who's actually read the whole thing. Then it pulls relevant papers, converts your whiteboard sketches into LaTeX, and reasons through your equations in context. 

Kevin Weil at OpenAI said: "2026 will be for AI and science what 2025 was for AI and software engineering."

That's basically Claude Code for scientists.

Google's Agentic Vision does something different. When AI looks at a photo now, it doesn't just glance and guess. It actually investigates. Zooms in, crops sections, checks details. Accuracy jumps 5-10%. 

A startup using it for architectural reviews got 5% gains on code compliance checking. That's real money.

Then there's Moonshot's Kimi K2.5. One prompt generated a 100MB Excel storyboard with 55 consistent scenes for a film. 

The model figured out what needed to happen, split the work between specialist agents, and stitched it back together. 

Their video-to-code feature is wild: record your screen while browsing, upload the video, it clones the entire site with all the interactions.

As AI applications advance, it will stop waiting for you to ask the right question. 

It's now asking what it needs to know, what tools to use, how to check its own work.

Investigates, coordinates, verifies.

We're about to see more and more workflows change to human in the loop management of AI agent outputs. 

How would that affect your job?

ChatGPT started as a research preview. 

Three years later, it's the infrastructure layer for how people think.

That's OpenAI's framing in their new strategy note.

People didn't just download ChatGPT to experiment. They folded it into their lives. Students used it to untangle homework at midnight. 

Parents used it to plan trips and manage budgets. Writers broke through blank pages. Then they brought it to work.

At first in small ways. A draft refined before a meeting. A spreadsheet checked one more time. A customer email rewritten to land the right tone. 

Then it became part of daily workflows. Engineers reasoned through code faster. Marketers shaped campaigns with sharper insight. Managers prepared for hard conversations with better context.

What started as a curiosity tool became infrastructure.

Here's the architecture OpenAI is describing: 
compute scales at 3x per year 
(0.2 GW in 2023, 0.6 GW in 2024, 1.9 GW in 2025). 

Revenue scales at the exact same curve 
(10x in three years, $2B to $20B). 

Stronger models unlock better products. Better products drive adoption. 

Adoption funds the next wave of compute. The flywheel compounds.

The business model is deliberate too. Consumer subscriptions for individuals. 

Usage-based pricing for teams where costs scale with real work. APIs for developers. Free tiers for adoption. Commerce and advertising for decision-making moments. 

Every monetization path is calibrated to the value delivered.

Compute is still the scarcest resource. Three years ago OpenAI relied on a single provider. Now they're diversified across an ecosystem, training frontier models on premium hardware for capability, serving workloads on cheaper infrastructure for efficiency.

The vision for 2026 is "practical adoption." 

Close the gap between what's possible and what's actually being used. Health, science, enterprise. Places where better intelligence becomes better outcomes.

Does OpenAI continue to grow the adoption curve it needs to sustain and thrive?

Neoclouds are tracking toward $180 billion in revenue by 2030.

You probably haven't heard of them. 

But Nvidia's channel chief calls them the "next phase of opportunity" for the AI data center boom.

So what are neoclouds?

They're smaller, AI-specialized cloud providers built specifically for training and inference workloads. Think CoreWeave and Nebius. 

They compete with AWS, Azure, and Google Cloud, but they're laser-focused on AI infrastructure instead of general-purpose computing.

Right now they're pulling in $23 billion annually. If the projections hold, that becomes $180 billion by 2030.

That's not niche territory. That's a legitimate market segment carving out space from the hyperscalers.

Nvidia's Craig Weinstein says this is an "ever-growing and important segment" for the broader ecosystem. 

World Wide Technology, ranked #9 in the Solution Provider 500, is already generating substantial revenue from hardware resale, data center design, and managed services for neoclouds.

If you're training massive models, you don't need AWS's 200+ services. You need optimized GPU clusters, fast interconnects, and teams that understand AI workloads.

We've spent 15 years watching everything consolidate into three hyperscalers. 

Now AI workloads are specialized enough to fragment the market again.

If you're building or optimizing data center infrastructure for AI workloads, let's talk about solving orchestration bottlenecks together.

#AmalgamyAI

Google just funded the guy who co-authored "Attention Is All You Need."

Llion Jones helped invent transformers. 

Now he's "absolutely sick of them."

Google announced a partnership with Sakana AI, backing the Tokyo startup with capital and access to Gemini and Gemma models. 

Sakana isn't building bigger transformers. 
They're exploring fundamentally different architectures inspired by biology.

Their Continuous Thought Machines incorporate timing at the neuron level. Something transformers completely ignore. When solving mazes, the model's attention literally traces the path like a human thinking step-by-step.

Their AI Scientist automatically generated a research paper that passed peer review at ICLR. The first fully AI-authored paper to do it.

Last month, their ALE-Agent became the first AI to win a major programming optimization contest, beating 804 human competitors.

Why does Google care? 

Because Deepmind CEO Demis Hassabis just said at Davos that AGI probably needs "one or two more breakthroughs," specifically around continual learning and memory. 

Those are exactly what Sakana researches.

Of the eight transformer co-authors, only two still work at major frontier labs. 

The rest scattered to startups and other labs. Jones is the most vocal about moving beyond what they built.

Sakana will actually still use Gemini extensively.

But is this Google admitting the transformer paradigm might be incomplete?  

Hassabis said language models could be "a key component" of AGI rather than "the only component." 

Google is paying to keep a seat at the table if something else wins.

Goodbye text to video. 

Hello world model? 

Google releases Project Genie, which is being labeled as a world model. You describe an environment in text, upload a sketch, pick how you move through it, and the AI generates the entire world in real time as you explore it. 

Walking, flying, driving. Doesn't matter. The world builds itself around your input.

Right now it's limited to 60-second sequences. Grainy visuals. 

But that's not the point.

For decades, 3D world building has required artists, level designers, physics programmers. 

You want a procedurally generated dungeon? You need someone to code it. You want a city layout? An artist draws it. A game environment? A whole team builds it.

Genie flips that. You describe what you want. It generates it. Then it simulates it. In real time.
The implications split two ways.

For game devs and architects, this is a productivity tool. Rapid prototyping. 
Sketch an idea, iterate instantly, move to polish. That's useful.

But for the people whose job is "turn a description into an interactive space," this is a different conversation.

Google's being cautious. It's locked behind their Ultra subscription, $250 a month. Gradual rollout. Not exactly screaming from the rooftops about it.

The shift from "humans build worlds" to "AI generates worlds on demand" doesn't feel like it's coming in 5 years. It feels like it's here. 

Which industry adopts Genie first: games, architecture, education, or something else?

Cisco surveyed 5,200 security professionals across 12 markets. 

90% expanded their privacy programs because of AI. 

93% plan to spend more this year.

Privacy spending has tripled. 

38% now drop at least $5 million annually on it, up from 14% last year.

These privacy frameworks are unlocking faster AI deployment. 
And more importantly, better customer trust, an actual competitive advantage.

But a lot of companies are stuck in the middle.
65% can't access quality data efficiently. 
85% say data localization requirements are adding cost and complexity. 

They're spending millions on governance while still fighting basic data hygiene.

Jen Yokoyama at Cisco put it this way: "Organizations must deeply understand and structure their data to ensure every automated decision is explainable. It's not just for compliance, but a necessary scaling engine for AI innovation."

Companies with mature governance can move fast. 

The others are paying what I'd call the AI tax. 

Higher costs, slower rollouts, fragmented systems.

Would love to hear what you think of this technology!

Board & Executive Advisor on AI Accountability and Human Impact Former Fortune 500 finance executive helping leaders make responsible decisions before risk, regulation or reputation force the issue

A college student with quadriplegia types math equations with her tongue.

No surgery. No headgear. Just a retainer.

She’s studying math and computer science. She takes notes in class, texts friends, and researches assignments—often in places where voice control isn’t an option. Libraries. Lectures. Group study rooms.

The device is MouthPad^, built by MIT spin-off Augmental. It’s a custom 3D-printed retainer that sits on the roof of your mouth and turns tongue movement into cursor control.

Think about what assistive tech usually asks people to accept:
 ↳ Surgical implants, recovery time, and real risk
 ↳ Visible headgear that turns you into “the person with the device”
 ↳ Voice commands that fail the moment the room needs to be quiet
 ↳ Price tags that put independence out of reach

What Augmental built instead:
 ↳ A touchpad for the tongue: swipe to move, press to click
 ↳ Head-tracking for broad motion, tongue reserved for precise control
 ↳ Bluetooth pairing like a normal mouse—no special software
 ↳ $1,500 during early access
 ↳ Users wearing it up to 9 hours a day with low fatigue

Here’s the part that stopped me.

Her mother called receiving the MouthPad^ “the most significant moment since her injury.” 

Not the diagnosis. 

Not the rehab milestones. 

A dental retainer that gave her daughter control back—without making her life louder, more visible, or more medical.

In a lecture hall, no one knows she’s controlling her laptop. 
That’s not a side benefit. 
That’s the design.

For boards and executive teams, this isn’t a feel-good edge case. It’s governance. When non-invasive, low-cost accessibility exists and organisations still choose systems that shut people out, the issue stops being “technology limits” and becomes leadership. Procurement standards, inclusion policy, and digital decisions determine who gets full access—and who is left working around choices made for them. That’s when accessibility stops being a design preference and becomes an accountability issue.

And it raises a blunt question:

Why did we spend decades trying to restore digital access by opening skulls when a workable interface might already be inside the mouth?

♻️ Share if you think accessibility shouldn’t require surgery—just better design.

This is one of the stories from my keynote on Designing Accessible Futures: AI, Ethics and Inclusion in Practice for boards and leadership teams.

Source: MIT News and Augmental (MouthPad^ assistive technology).

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