MBA Training AI

Does running twenty AI agents in parallel on the same problem produce better answers? A developer on OpenAI's Codex project ran the experiment and found swarms burn 10 to 20 times more tokens while landing in roughly the same place. The argument here is that the agents are not independent thinkers: they share one model, one training set and one reasoning pattern, so temperature changes give different wording rather than different intelligence.

You come away with a test you can run Monday morning, a way to read vendor claims from Google AI and other cloud providers against your own token bill, and a clear line between specialization, which works, and redundancy, which does not.

Key takeaways

• Run one real task through a single agent and through your swarm, then compare answer quality against token cost using actual numbers rather than impressions.
• If swarm quality matches single agent quality, shut the swarm down and keep the token savings.
• Give each parallel agent a genuinely different job and prompt, such as write the code, write the tests, check the security.
• Treat vendor claims about multi-agent orchestration with caution, since cloud providers bill for every extra agent you run.
• Remember that raising temperature varies wording, not reasoning, so extra clones repeat the same misreading of the problem.

Chapters
0:00 OpenAI Codex data on agent swarms
1:04 Why 20 clones repeat the same mistakes
1:47 Vendor incentives behind multi-agent hype
2:35 Specialization works, redundancy does not
3:22 Token cost math of a 20-agent swarm
3:46 The single agent versus swarm test

Go deeper, free lessons

• Multi-agent systems: orchestrator, workers, and handoffs
• Agents vs workflows vs automations: choosing the right level of autonomy
• Cost, latency, and reliability: shipping agents to production
• What an AI agent really is: the perceive, plan, act, observe loop
• Codex: agentic coding in your environment

Full article and transcript: https://www.mba-training.com/blog/ai-agent-swarms-waste-tokens

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Per-seat SaaS pricing assumes a human logs in and does the work. Agentic AI does not log in, so one user can run forty background agents that never touch the interface. This episode argues the seat is already dead as a billing unit, and that charging agents a seat fails in both directions: too cheap and you give away a thousand humans of work, too expensive and the customer builds it in-house.

You come away with a way to pick a new unit. The discussion covers outcome pricing, Intercom's roughly 99 cents per resolution, Salesforce Agentforce at about $2 per conversation, efficiency deflation as inference costs fall, and why credible attribution decides who keeps pricing power.

Key takeaways

• Stop counting seats and count value events per account: tickets resolved, reports shipped, money moved.
• If your value-event count is rising while seat count stays flat, reprice against that event before a competitor does.
• Meter a business result the customer would have paid a human far more for, not tokens, tasks or invoices scanned.
• Treat vendor figures like Intercom's 99 cents per resolution, Salesforce's $2 per conversation and OpenAI's tenfold annual inference cost drop as marketing, and cross-check them.
• Build the ability to prove your agent caused the outcome, because whoever measures it credibly owns the pricing conversation.

Chapters
0:00 Why per-seat pricing stopped working
1:12 Outcome pricing and Intercom's per-resolution model
1:53 Efficiency deflation and falling inference costs
2:34 Metering value customers feel, not work
3:04 Attribution as the new moat: Agentforce
3:53 Monday morning repricing steps

Go deeper, free lessons

• What agentic AI breaks in the SaaS business model
• Pricing and packaging AI in a per-seat world
• Embedding AI features that customers actually pay for
• Retention is the engine, not the afterthought
• Reading a SaaS company through its unit economics

Full article and transcript: https://www.mba-training.com/blog/agentic-ai-saas-pricing-disruption

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AI credit models score applications in seconds and can approve borrowers a human officer would skip, but they can also rebuild race from proxies like zip code, college name or phone carrier. This episode takes the position that the risk is not the automation itself, it is the failure to test and document. A negligent model is worse than a biased human; a disciplined one is better and provable.

You will learn the difference between disparate treatment and disparate impact, how a less discriminatory alternative search creates a legal defense, what belongs in a model risk file to meet the business necessity standard, and what CFPB, OCC and DOJ enforcement has cost, including Trustmark's roughly $9 million redlining settlement and over $150 million from the DOJ redlining initiative.

Key takeaways

• Run a less discriminatory alternative search on every credit model, and document what you found and adopted.
• Assume removing race does not remove bias, because zip code, college and phone carrier act as proxies.
• Keep a model risk file listing every variable, its business justification, and proof it predicts default.
• Ask your data science team to show the less discriminatory alternatives they tested and rejected; silence is a red flag.
• Cross-check vendor fairness claims, including Google's fairness libraries, against independent research.

Chapters
0:00 Why banks automate credit decisions
0:41 Disparate treatment versus disparate impact
1:39 Less discriminatory alternative search explained
2:50 Model risk files and business necessity
3:23 Redlining settlement costs and DOJ actions
3:44 Human loan officers versus audited models

Go deeper, free lessons

• Governing black-box models through SR 11-7 and explainability mandates
• Spotting model risk before it becomes a loss event
• Bias in AI: where it comes from and why it matters
• Protecting the customer: conduct and fair treatment
• Running the pre-deployment gauntlet: checks that catch problems early

Full article and transcript: https://www.mba-training.com/blog/credit-decisioning-models-fair-lending

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Everyone says they want an agent running their inbox, and almost nobody hands one real control. The episode argues the gap is not a technology problem but a trust problem, and that Fyxer treats trust as the product rather than email sorting. The position: automation is checked work, delegation is unchecked work, and most agent companies fail at the second step.

You come away with the three parts Fyxer uses, fine-tuning on your own writing, persistent memory across sessions, and structured human feedback where every edit becomes a training signal. You also get a way to read adoption claims from OpenAI and Google AI against MIT Technology Review and O'Reilly Radar, plus a draft-mode rollout pattern with accuracy thresholds set in advance.

Key takeaways

• Design the feedback loop before you build the automation, so every human correction retrains the agent.
• Ship agents in draft mode where the user approves each reply, then widen authority per task once accuracy passes a threshold you set in advance.
• Treat memory without correction as a confidently wrong colleague, and keep a human in the loop early before pulling them out gradually.
• Discount adoption figures from OpenAI and Google AI, who sell the underlying models, and cross-check against MIT Technology Review and O'Reilly Radar.
• Assume trust is asymmetric: one embarrassing autonomous email to a board member can get the whole tool banned.

Chapters
0:00 Why people won't delegate their inbox
1:04 Fine-tuning, persistent memory, structured feedback
1:58 Agent adoption claims versus pilot reality
2:58 Probation periods for AI agents
3:47 Visible reasoning and cheap error catching
4:15 Ship in draft mode first

Go deeper, free lessons

• Guardrails, permissions, and human-in-the-loop
• Meetings, email, and admin: reclaiming hours each week
• Agents vs workflows vs automations: choosing the right level of autonomy
• Evaluating and debugging agents: traces, evals, and failure modes
• Memory and state: short-term context and long-term recall

Full article and transcript: https://www.mba-training.com/blog/fyxer-ai-executive-assistant-trust

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Anthropic released the Model Context Protocol as an open standard in December 2025. Was that generosity or strategy? The position here is that it was strategy that worked: Anthropic set the terms the ecosystem now builds on, and MCP solved the N times M integration problem by replacing bespoke connectors with one protocol. But fixing the plumbing exposed the reasoning failures that broken pipes used to hide.

You get a way to read vendor numbers, including OpenAI's 70 percent task completion claim and Google's tool use accuracy figures, against MIT Technology Review's independent evaluations closer to half. You also get Block's approach of exposing a narrow, well documented tool surface, plus a rule for scoping what an agent may do without a human.

Key takeaways

• Before connecting an agent, write down every action it may take without a human and cut anything irreversible.
• Treat vendor benchmarks like OpenAI's 70 percent completion rate as directional only, and check independent evaluations such as MIT Technology Review's.
• Expose a limited, well documented set of internal tools rather than everything at once, as Block did with its MCP agents.
• Let agents draft refunds, pull data and format reports, but keep a human on the trigger for irreversible steps.
• Expect composability: you describe the goal and the agent assembles the path, so you give up knowing the steps in advance.

Chapters
0:00 Why Anthropic gave MCP away
0:53 The N times M integration problem
1:23 Plumbing was the bottleneck, not intelligence
2:10 Vendor benchmarks versus independent evaluations
2:56 Scoping agents and tight fences
3:53 How Block limited its MCP tools

Go deeper, free lessons

• MCP explained: the USB-c for AI tools
• Connecting and using MCP servers
• Building your own MCP server
• Tools and function calling: giving your agent hands
• Connector safety, permissions, and governance

Full article and transcript: https://www.mba-training.com/blog/mcp-ai-agents-tool-use

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Price elasticity has been calculated in consumer goods since the 1980s, so why does it matter again now? The answer given here is cadence and granularity: quarterly, category-level estimates are historical artifacts, while pooled models that borrow signal across similar products and stores can produce weekly estimates across roughly 40,000 SKUs per category. Vendor throughput claims from OpenAI and Google AI are worth scrutiny, since O'Reilly Radar engineering write-ups put real production costs higher once data plumbing is included.

You leave knowing how to separate price cuts from display promotions in point of sale data, model the post-promotion dip that MIT Technology Review flagged, validate against holdout stores, and bring SKU-level numbers into trade negotiation with retailers.

Key takeaways

• Pool data across similar products and stores so slow-moving SKUs inherit patterns instead of producing noise.
• Move elasticity refreshes from every 13 weeks to weekly so numbers survive contact with a live trade negotiation.
• Clean your point of sale feeds so a price cut can be separated from a display promotion sitting on top of it.
• Model the post-promotion dip explicitly and validate against holdout stores where you deliberately skip the price change.
• Treat a wide confidence range as the model admitting it does not know, and override it with human judgment.

Chapters
0:00 Why elasticity models resurfaced in FMCG
0:54 Pooling signal across sparse SKU data
1:27 Weekly refresh across 40,000 SKUs
2:22 POS data plumbing and promotion logging
2:49 Pantry loading inflates elasticity estimates
3:36 Using elasticity in retailer trade negotiation

Go deeper, free lessons

• Forecasting demand for fast-moving SKUs across channels
• Pre-deployment checks for pricing, promotion and supply-chain AI
• Marketing mix modeling for trade and media spend
• Reading and shaping category dynamics
• The four model risks that break FMCG AI systems in production

Full article and transcript: https://www.mba-training.com/blog/ai-elasticity-models-fmcg-price-optimization

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Why do clinical decision support tools with strong validation scores get ignored on the ward? The position here is that the blocker is calibration and explainability, not model accuracy. A model that is right 94 percent of the time gives the clinician a lifetime average, not a way to judge the single alert in front of them. Epic's sepsis prediction model, and the external Michigan study that found missed cases and false alarms, is the worked example of how that ends in alert fatigue.

You come away with two design rules for clinical AI screens, a way to read OpenAI's medical benchmark claims against O'Reilly Radar adoption data, and a one-question test to run before any go-live.

Key takeaways

• Show the evidence behind an alert, such as rising lactate and six hours of elevated heart rate, rather than the model's internal reasoning.
• Display uncertainty as confidence bands so clinicians can see when the model is guessing.
• Design the tool so the clinician stays the decision maker and the AI behaves like a well-read intern that can be overruled.
• Treat vendor-published accuracy numbers, including OpenAI's medical benchmarks, as marketing until an independent study checks them.
• Before approving any clinical AI, sit a frontline clinician in front of it and ask whether they can tell when it is wrong.

Chapters
0:00 Why clinical AI tools fail on the ward
0:40 Why 94% accuracy is useless at the bedside
1:24 Epic's sepsis model and alert fatigue
2:07 What explainability looks like on screen
2:59 OpenAI benchmarks versus O'Reilly adoption data
4:11 Running a trust review before go-live

Go deeper, free lessons

• Clearing the safety, regulatory, and liability bar for clinical AI
• Diagnosing model risk in clinical AI
• Measuring outcomes and running post-deployment evaluation
• Running the pre-deployment guardrail checklist
• HIPAA and the price of a data breach

Full article and transcript: https://www.mba-training.com/blog/clinical-ai-clinician-trust-diagnostic

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Should a public body fight AI agents that file claims for citizens, or give them a proper entrance? The UK's Department for Work and Pensions is the first large organisation to say out loud that a big share of its benefits claims now come from software agents, and the position here is that pretending they are not there is the expensive mistake. Detection and rate limiting buy time; a machine-readable submission channel with verified identity fixes the noise.

You walk away able to instrument your own intake, spot agent traffic by identical timing, inhuman speed and repeated retries, and weigh the two failure modes: locking out disabled claimants or letting fraud agents drain the budget. Includes what OpenAI, Google AI, O'Reilly and MIT Technology Review actually claim about agent reliability.

Key takeaways

• Measure what share of your submissions already comes from agents before designing any controls.
• Spot agent traffic through patterns: identical timing, inhuman speed and repeated retries after rejection.
• Publish a machine-readable submission channel so you control the format and can verify identity up front.
• Treat vendor adoption figures from OpenAI and Google AI as self-serving and cross-check with O'Reilly developer surveys.
• Tune access rules continuously: blocking too hard risks discrimination claims, blocking too soft invites fraud.

Chapters
0:00 AI agents filing DWP benefits claims
1:09 Genuine access or carpet bombing claims
1:49 Vendor hype versus real agent reliability
2:35 Building an official door for agents
3:19 Blocking too hard versus too soft
3:59 Instrument your intake and count agents

Go deeper, free lessons

• Guardrails, permissions, and human-in-the-loop
• Agents vs workflows vs automations: choosing the right level of autonomy
• The ChatGPT agent: browsing and taking actions
• Evaluating and debugging agents: traces, evals, and failure modes
• Governance and the EU AI Act: the topline

Full article and transcript: https://www.mba-training.com/blog/ai-agents-public-services-human-oversight

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Enterprise AI spending per employee dropped at top firms in August 2026. Does that mean companies got efficient, or that adoption quietly stalled? The position here is that the ratio itself is the problem: token prices at the frontier labs have fallen by more than half since early 2025, and headcount grew at the heaviest adopters, so the per head figure falls even when absolute budgets rise.

You get a way to tell a real efficiency gain from a dying pilot, using weekly active users divided by paid licenses per team, the Google AI 30 percent shipping claim checked against O'Reilly Radar survey data, and the Klarna customer service reversal as a warning about cutting cost before fixing the workflow.

Key takeaways

• Split spend per employee into its parts before reading it: falling token prices and rising headcount both push the ratio down.
• Track weekly active users divided by paid licenses per team over time instead of dollars per head.
• Treat unused licenses as a stall, not a saving: 120 weekly logins against 500 seats means 380 people abandoned the tool.
• Read a healthy drop as rising usage with flat or rising total spend and falling unit cost.
• Redesign the workflow before cutting cost, as Klarna's customer service rehiring showed.

Chapters
0:00 Why spend per employee fell in August 2026
0:41 Token price deflation and headcount growth
1:22 Google AI claims versus O'Reilly Radar data
2:16 Active seats versus paid licenses
3:04 Klarna and the cost cut that reversed
4:08 The one usage ratio to track weekly

Go deeper, free lessons

• Cost, latency, and model selection tradeoffs
• Tokens, training, and inference: what happens when you hit enter
• Evaluating outputs: how do you know it works?
• Choosing the right approach: prompt, RAG, fine-tune, or agent
• Thinking before building: framing an AI problem

Full article and transcript: https://www.mba-training.com/blog/ai-spend-per-employee-falling

MBA Training, mba-training.com

How does an advanced driver assistance system get from raw sensor data to a brake command in about 100 milliseconds, and which stage of that chain actually breaks? The position here is that object classification gets the attention while sensor fusion and training data carry the real risk. A system can label a scene beautifully and still arbitrate a camera and radar conflict badly, which is how a car reads a white truck against a bright sky as open road.

You come away able to describe each stage of the pipeline, explain why cameras, radar and lidar are blind in different ways, read vendor accuracy claims such as OpenAI's 99 percent benchmark figure against independent coverage from MIT Technology Review and O'Reilly Radar, and press vendors on failure behaviour instead of accuracy scores.

Key takeaways

• Treat ADAS as a relay race of sensing, fusion, classification and decision logic that must finish in roughly 100 milliseconds.
• Expect conflicts between sensors and ask exactly how fusion arbitrates them, since bad arbitration is where fatal failures cluster.
• Read classification outputs as probabilities, not certainties, and plan for the gap between 91 percent and 100 percent confidence.
• Discount vendor benchmark accuracy figures, including OpenAI's, and cross check against independent road and long tail coverage.
• Ask vendors for failure data and untrained edge cases, because the missing 1 percent is your liability.

Chapters
0:00 How ADAS decides to brake before you do
0:47 Cameras, radar and lidar tradeoffs
1:11 Sensor fusion and bad arbitration crashes
2:06 Object classification and the edge case problem
3:35 Decision logic and the 100 millisecond budget
4:18 What to ask ADAS vendors for

Go deeper, free lessons

• Defining the autonomy ladder and where AI actually earns its keep
• Clearing the safety and validation bar for AI you ship
• Recalls, defects, and the machinery of NHTSA
• Building a model risk framework for safety-critical AI
• The regulatory map every automotive AI leader must navigate

Full article and transcript: https://www.mba-training.com/blog/adas-perception-stack-real-time-decisions

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