Happy Canada Day! Hope everyone is having a good week. This week, we explore how Mythos revealed what happens when AI finds more problems than people can fix, why faster output creates new bottlenecks, and where human judgment becomes more valuable.

AI in Action

The Problem With Finding Too Many Problems [Cybersecurity/AI Models]

Claude Mythos arrived in April with the kind of mystery usually reserved for unreleased films and government projects. Anthropic gave a small group of organizations access through Project Glasswing, expanded the programme in June, released a more capable Mythos 5, then watched the US government suspend access over national-security concerns. Two weeks later, the model returned for more than 100 approved American organizations under tighter controls. Previewed, expanded, blocked, restored. Mythos has already had a busier release schedule than most streaming shows.

The caution comes from what the model can do. Mythos can inspect large codebases, investigate suspicious sections, and test whether weaknesses could be exploited. Earlier versions reportedly uncovered serious flaws in widely used software and sensitive government systems. That helps defenders find problems sooner. It also gives attackers a faster way to search for the same weak points. Anthropic has kept access limited to vetted technology companies, security firms, government bodies, infrastructure operators, and some open-source maintainers. Smaller organizations may face the same vulnerabilities without the same tools, staff, or repair budgets.

Then comes the awkward part: finding the flaw. Every result still needs to be verified, ranked, assigned, repaired, tested, and released. Weak alerts create noise. Real vulnerabilities compete for scarce engineering time. A payment-system flaw deserves a different response from an obscure bug buried in old software. Discovery becomes abundant. The advantage shifts to teams that can separate urgent risks from impressive-looking noise, then get the right fix out quickly. Faster detection also raises expectations. Once a company knows about a serious weakness, customers, boards, and regulators will have questions about how long it sat in the queue.

Cybersecurity offers a sharp preview of a broader workplace problem. AI can generate more code than developers can review, more leads than sales teams can pursue, and more analysis than managers can turn into decisions. The slow point shifts farther along the process. Useful adoption requires a plan for who checks the output, who decides what deserves attention, and who owns the next step. Mythos can find problems at machine speed. Companies still have to fix them at company speed, which often involves three meetings and someone being on vacation.

Bits of Brilliance

The Moving Bottleneck [AI Adoption/Concept]

AI can make your team faster and your company slower. Mythos can help security teams discover ten times more software flaws while the number of fixes shipped barely moves. Coding tools generate features, review queues fill up. Sales tools surface leads, follow-up starts wheezing. One queue disappears and another quietly forms down the hall.

The harder job is deciding what deserves attention. More output creates more noise, and every alert carries a different level of risk. Some bottlenecks can be staffed or automated. Others live inside budgets, approvals, judgment, and the slightly terrifying moment when someone has to own the decision. Work usually piles up where tasks wait, bounce between teams, or depend on one person who is somehow always in another meeting.

Throughput shows how much useful work reaches the finish line. A team can produce more reports, ideas, alerts, and code while completing the same number of decisions, fixes, and launches. Strong AI workflows protect attention with filters, thresholds, and clear ownership. Sometimes the smartest system produces less because less is what the team can actually absorb.

A useful question for any AI tool is: What becomes scarce once this becomes abundant? When code becomes abundant, review gains value. When analysis becomes abundant, judgment gains value. When warnings multiply, prioritization gains value. The new bottleneck points to the skill, investment, or decision that will determine whether faster work creates progress or simply a larger pile.

Curiosity in Clicks

The Abundance Test [Experiment/Workflow]

Pick one task where AI has made you faster. Writing, research, coding, sales, meeting prep. Any workflow where the machine has started handing you more than your calendar can comfortably digest.

Complete this sentence: AI helps me produce more __________, but I still lack the capacity to __________.

For example: AI helps me produce more meeting summaries, but I still lack the capacity to follow up on every action item.

Paste your answer into your chatbot and ask: “Identify the bottleneck in this workflow. Explain why work is piling up there, and suggest one way to reduce the queue without creating a new one somewhere else.”

The second blank usually gives away the real problem. It may be attention, review time, ownership, budget, or the one decision everyone keeps circling without making. AI is very good at filling the top of the funnel. The bottom of the funnel still has three approvals and someone on vacation.

_{Byte-Sized Intelligence is a personal newsletter created for educational and informational purposes only. The content reflects the personal views of the author and does not represent the opinions of any employer or affiliated organization. This publication does not offer financial, investment, legal, or professional advice. Any references to tools, technologies, or companies are for illustrative purposes only and do not constitute endorsements. Readers should independently verify any information before acting on it. All AI-generated content or tool usage should be approached critically. Always apply human judgment and discretion when using or interpreting AI outputs.}