AI Agent Governance

Posted by Marcus Bourke on Saturday, October 25. 2025

I wrote about AI agents back in February 2025. If you're still not sure what agents are all about, go back and read that article now. Since then, agents have evolved and become a lot more prevalent. Actually, agentic AI is all thre rage.

Agents are very powerful, but as the saying goes, with great power comes great responsibility. If you're deploying an AI agent, you must ensure that you have proper controls in place. That means implementing strong governance. Let's dig into that today.

Governing AI Agents in Production: How to Monitor, Audit & Correct Autonomous Behavior

So, we know that AI agents can act, plan, and take multi-step actions on behalf of users and systems. It doesn't take much imagination to see the potential risk that poses. Let's break down some practical ways to monitor, audit, and safeguard the agents that you deploy.

Why agents require different governance than static models

Traditional models respond to prompts. Agents act on their own. They call APIs, send emails, create records, move money (sometimes), and take multi-step actions that can really streamline business operations. Because these actions can have irreversible consequences, governance must move from “model QA” to product-grade operational controls:

Agents can compound errors over multiple steps.

Agents may act with delegated permissions that require careful boundaries.

Agent failures can create downstream business, legal, financial or safety incidents.

Rule of thumb: Treat every agent as if it were a small autonomous system. Design it for observability, implement safe defaults, and ensure fast undo/stop controls. Basically, treat it as a junior-level employee and follow the trust but verify model.

Core principles for agent governance

Design for observability. If you can't see what an agent did and why, you can't fix it.

Prefer constrained autonomy. Start with narrow, reversible actions and expand the agent's scope of control in a cautious, controlled manner.

Human-in-the-loop (HITL) by default for risky tasks. Humans should review important or irreversible actions until the agent proves itself. Then, humans should perform a random audit function.

Fail-safe first. Default to “do nothing” or “ask a human” when confidence is low in the agent's ability to complete the task successfully.

Auditability and explainability. Preserve decision trails that can be reconstructed later.

Monitoring: what to log and watch

Good monitoring is more than uptime. For agents, you need to monitor three key categories: actions, decisions, and effects. Below is a checklist of things to be able to monitor before a wide rollout

Essential logs

Action log: Record every API call, external interaction, message sent, or resource changed (timestamp, actor, context, target).

Decision trace: Save the reasoning or chain-of-thought summary used to choose the action (hashed or summarized for privacy where needed).

Inputs & outputs: Retain the prompt/state before the action and the response after the action (store this securely).

Confidence & provenance: Capture the confidence score, model version, data sources cited.

Rollbacks/compensating actions: Record when and why a rollback occurred.

Here's a possible JSON log format to get you started

{"timestamp": "2025-09-29T14:32:10Z",

"agent_id": "invoice_agent_v1",

"session_id": "sess-abc123",

"action": "create_invoice",

"target": { "account_id": "acct-789", "invoice_id": "inv-20250929-01" },

"decision_summary": "Extracted line items -> grouped by client -> generated invoice draft",

"confidence": 0.87,

"model_version": "gpt-xyz-1.2",

"sources": ["document_123", "contract_456"],

"outcome": "success",

"rollback": false}

Key metrics to tack and report

Task success rate (per-agent, per-task)

Rollback frequency (how often actions were reverted)

Escalation ratio (percent of actions flagged to humans)

Latency & cost per action

Anomaly rate (unexpected/unauthorized actions)

Auditing & explainability

How do you answer the question “why did it do that?” If you guessed audits, you'd be correct. Audits need to reconstruct the decision path. You won’t (and shouldn’t) log full chain-of-thought text in production for privacy and data volume reasons, but you must retain a compressed decision trail:

Decision IDs: Comprised of hashable references linking inputs to intermediate steps to the final action.

Source citations: Track the source for each claim or data point the agent used.

Snapshot storage: Keep snapshots of state for high-risk actions (e.g., financial transfers) for a defined retention window.

Periodic audits

Schedule recurring audits: weekly for high-risk agents, monthly for medium-risk, quarterly for low-risk. Use a combination of automated checks (pattern detection) and human review (sampled cases) to verify that the agent is in compliance.

Corrective mechanisms & safe defaults

Below are some practical controls to limit potential harm from a malfunctioning AI agent.

Global Kill Switch: Build a kill switch, or an immediate stop for all agent activity with a single command. Test it monthly.

Scoped kill Switches: Build in the ability to disable a specific agent or a class of actions (e.g., “no outbound emails”).

Permission Gates: Institute the requirement that the AI agent must request more privileged actions, which require human approval.

Sandbox mode: Create and environment to allow agents to simulate actions and produce “what would happen” reports before doing the real thing.

Compensating transactions: For reversible domains, create automated rollback flows, such as the ability to cancel an invoice, process a refund, or reverse updates.

Critical Step: Implement a two-step commit process for irreversible actions. For example, the agent posts a proposed change and a human, or a timed automatic condition, confirms it.

Governance structures & organizational roles

Who's accountable for AI agent safety? Effective governance requires a defined governance model, named roles and responsibilities (ideally in the form of a RACI diagram), and an incident lifecycle.

Suggested roles

Safety Owner: Product or engineering lead accountable for day-to-day safety and incident triage.

Agent Review Board: Committee of cross-functional reviewers (product, engineering, legal, security) for major agent launches, permission upgrades and audit reviews/approvals.

Compliance Liaison: Owns audit readiness, reporting to the Agent Review Board and any required external reporting.

On-call Incident Responder: First responder responsible for handling immediate mitigation (activating kill switches, rollbacks, etc.).

Incident Resolution lifecycle (high-level)

Incident Detection (automated monitoring or customer reported)

Triage the incident to assess the incident and impact (Safety Owner + on-call first responder)

Mitigate this incident (activate kill switch, revoke permissions, execute a rollback, etc.)

Lessons Learned session to ensure it doesn't happen again (post-mortem and root cause analysis)

Remediate the root cause & document appropriately (bugs fixed, controls updated, etc.)

Communicate transparently about the issue, impact and resolution (customers, internal stakeholders, regulators as required)

Deployment strategies: How to roll agents out safely

Plan for a phased rollout to minimize collateral damage.

Parallel mode

Run an agent in parallel (observation-only) to compare proposed actions against business rules without actually executing them. This is critical for validating agent behavior under production-like conditions before going live.

Canary or pilot releases

Allow the agent to operate for a small group of users or accounts. Monitor metrics closely and expand only if the results are as expected and the agent is operating safely.

Phase in elevated permissions

Start with read-only access, then assign incremental permissions (write drafts, submit for approval, execute) as the agent proves itself. Each permission increase must be reviewed and approved by the Agent Review Board, monitored and periodically audited.

Examples of a rollout schedule

Phase	Duration	Allowable Actions
Parallel	2–4 weeks	Observe & log only
Pilot	1–2 weeks	Execute low-risk actions for a sample of customer/accounts
Pilot	2–6 weeks	Execute broader actions with human approval
Production Rollout	Ongoing	Full permissions with monitoring & periodic audits

A hypothetical case study

A short fictitious example to illustrate governance in practice.

Imagine an invoice agent that drafts invoices from contracts and submits them to customers. In production it mistakenly billed a test account because a flag in the sandbox environment was unset. With governance in place the team:

Detected unusual billing via anomaly monitors (surge in invoices for test accounts).

Triggered the scoped kill switch to stop additional invoice generation.

Rolled back the erroneous invoices using automated compensating actions.

Ran a post-mortem and determined that the root cause was environment misconfiguration. Remediation called for an additional gate check and guardrails in the agent planner.

Published a customer-facing incident report and updated the risk register.

The end result: quick remediation, minimal customer impact, and improvements that made the agent safer.

Potential operational checklist for agent governance

Agent Governance Checklist

1. Observability

   - Action logs enabled

   - Decision traces linked to actions

   - Confidence & model-version metadata

2. Monitoring

   - Task success rate dashboard

   - Rollback & escalation metrics

   - Anomaly detection on actions

3. Safeguards

   - Global kill switch tested

   - Scoped kill switches available

   - Permission gates for privileged actions

4. Auditing

   - Weekly sample audits (high-risk agents)

   - Quarterly full audits

5. Roles & governance

   - Named Safety Owner

   - Agent Review Board charter

   - Incident runbook + post-mortem template

6. Rollout

   - Parallel -> Canary -> Pilot -> Full Production plan

7. Communication

   - Customer incident template ready

   - Internal escalation contacts documented

Abbreviated post-mortem template

Incident Post-Mortem

1. Title & date

2. Incident summary (1-2 sentences)

3. Timeline of events (concise)

4. Root cause

5. Impact Assessment (users, data, financial)

6. Immediate mitigation steps

7. Root cause fixes & owners (with deadlines)

8. Preventive measures & monitoring updates

9. Customer communications & compensation (if any)

10. Lessons learned

Final thoughts & next steps

Running AI agents in production raises the bar on the need for governance, but it’s also solvable with engineering discipline, thoughtful product design, and clear organizational ownership. Start small, expand in a controlled manner, and treat safety as a critical function, the same way you treat performance and reliability.

Immediate actions you can take today: enable action logging, define a Safety Owner, and add a “parallel mode” for your highest-risk agent(s). Those three moves drastically reduce collateral damage and buy you the time needed to build a robust goverenance model and implement associated controls.

Interested in working with us? Check out FailingCompany.com to learn more. Go sign up for an account or log in to your existing account.

#FailingCompany.com #SaveMyFailingCompany #ArtificialIntelligence #AIAgentGovernance #SaveMyBusiness #GetBusinessHelp

AI Startup Myths

Posted by Marcus Bourke on Saturday, October 18. 2025

Hopefully you're well on your way with your AI start up by now. Last week's post should have helped you down the right path to gain some real traction in your business. But what other issues do you need to know about? Are there any land mines to watch our for that could sink your business? With something as hot as AI, you already know the answer to that. Let's check it out today.

AI Startup Myths That Could Sink Your Business (And What to Focus on Instead)

It seems like you can't go anywhere without hearing about AI...in the news, on social media, in the boardroom, and in just about every other corner of the planet. Unfortunately, that means that there are plenty of myths floating around as well. If you’re building an AI startup, buying into these myths can torpedo your business. Let’s tackle some of the biggest myths and talk about what you should focus on instead.

Myth #1: “If You Build Amazing AI, Customers Will Come”

This is the classic “Field of Dreams” trap. Founders assume that if they train the most advanced model, customers will line up at the door. The truth is that most customers don’t care about your algorithm. They care about what problem you solve for them and how it impacts their bottom line.

Reality: Successful AI startups like Gong and Jasper thrived not because they had the “best” models, but because they solved urgent pain points (sales insights, content creation) and packaged them in easy-to-use products.

Focus instead: Don't deviate from solid business fundamentals. So, always lead with customer value. Translate your AI solution into clear outcomes like time savings, increased revenue, operating cost reduction or risk reduction. Let the tech stay behind the scenes, and let business outcomes be the trailer for the feature film.

Myth #2: “More Data Automatically Means Better AI”

It’s easy to assume that adding in more data will magically make your AI smarter...and more competitive. But data without quality, diversity, or proper labeling can backfire on you. It can end up producing biased, noisy, or even dangerous outputs. That will be of no benefit to your business.

Reality: Startups like Scale AI built their business not around “more data,” but around better data. They invested in clean, structured, and high-quality inputs that made their AI systems usable and beneficial in the real world.

Focus instead: Curate data ruthlessly. Spend energy on quality datasets, feedback loops, and continuous improvement rather than training your models on terabytes of potentially junk data.

Myth #3: “Big Models Always Win”

There’s a myth that the path to success is building the biggest, most complex models possible. But training massive models is expensive, risky, and rarely practical for startups. You can’t outspend OpenAI or Google.

Reality: Many thriving startups (like Runway and Perplexity) succeed with smaller, fine-tuned, or specialized models that do one thing incredibly well.

Focus instead: Find niches where smaller, more efficient models shine. Customers care about accuracy, speed, and usability. If using a model adds clear business value, then they aren't going to care about the parameter count of your model.

Myth #4: “Riding the Wave of AI Hype Is Enough to Attract Investors”

In 2021, this myth almost seemed true. Money poured into anything with “AI” in the pitch deck. But now, the market feels saturated and investors have gotten more discerning. They’ve seen too many flashy pitches that never turned into revenue to continue to throw money at every "AI" opportunity.

Reality: Funding has shifted toward startups with traction, not just cool technology or POCs. Investors want to see paying customers, proof of ROI, and a path to scale. Even buzzy startups like Adept AI have faced tough funding rounds because hype alone doesn’t pay the bills.

Focus instead: Build traction before chasing big investors. Focus on the fundamentals by nailing customer validation, proving ROI, and showing a repeatable sales model. Then funding just becomes fuel to keep moving down the road.

Myth #5: “AI Will Replace the Humans (So Customers Won’t Need Staff)”

Founders sometimes oversell AI as a total replacement for human roles. That’s not just misleading, it can be a trust killer if it's not definitively true. Customers don’t want to fire entire teams unless they have an immediate need to significantly reduce admin cost. Rather, they want tools that augment their people and make them more productive.

Reality: Startups like UiPath succeeded by positioning AI as a “digital assistant” that helps workers get rid of repetitive tasks. That narrative won trust and adoption.

Focus instead: Frame your AI as augmenting humans, not replacing them. Show how it makes employees smarter, faster, or more effective. That’s a message customers can embrace without fear. It's also a message that their employees can embrace, increasing the odds of a successful implementation.

Myth #6: “Ethics and Compliance Can Wait Until Later”

Startups often push responsible AI to the back burner, figuring they’ll fix it once they get bigger. Big mistake. Issues like bias, privacy, and transparency can kill deals early if enterprise customers sense risk.

Reality: Companies like Anthropic have built their entire brand around responsible AI...and it’s winning them major enterprise contracts.

Focus instead: Bake ethics, privacy, and transparency into your company DNA from day one. Clear model cards, explainable results, and thoughtful data policies aren’t just compliance...they’re competitive advantages.

Myth #7: “You Have to Go Broad to Succeed”

Some founders try to build AI that can solve everything for everyone. That’s a fast track to confusion and value dilution.

Reality: The most successful AI startups almost always start narrow. DeepL didn’t try to “do all AI." Instead, they nailed translation. PathAI focused on pathology before expanding. Specialization builds credibility, customers, and traction.

Focus instead: No company, AI or not, can be everything to everyone. Pick one pain point, solve it exceptionally well, and then expand once you’ve earned trust and revenue.

Let's Recap What You Should Actually Focus On

Strip away the myths and the playbook becomes clearer. Focus on business fundamentals:

Customer pain points first. Solve urgent problems, not just interesting ones.

Quality over quantity in data. Curated datasets beat massive ones.

Practical AI. Choose speed, usability, and ROI over chasing the biggest model size that can do everything.

Responsible AI. Make ethics and compliance part of your company's DNA, not an afterthought.

Start narrow. Dominate one use case before expanding. Then look for complimentary problems to address.

Final Thought

AI is still one of the most exciting places to build right now. But the graveyard of failed AI startups is filling up quickly. They all had brilliant ideas but believed in the wrong myths. If you stay grounded in the business fundamentals, customer-focused, and ethics-driven, you’ll put yourself in the small but powerful group of AI startups that not only survive, but thrive.

Building Traction With Your AI Startup

Posted by Marcus Bourke on Saturday, October 11. 2025

If you read last week's article, then you have a good idea on how to build trust with your AI startup. So, what comes next? Well, how do you actually get traction and grow your business. We don't want you stuck in the pilot phase for ever. So, let's explore some ways to build traction this week.

Moving From Pilot to Sustainable Profit: How AI Startups Can Win Their First Real Customers

We've talked extensively about how AI startups are popping up everywhere. We've also talked about how most never make it past the pilot stage. Here are some ways to avoid “pilot purgatory” and start building real customer traction.

Why So Many AI Startups Get Stuck in Pilot Purgatory

Pilots can be a double-edged sword. On one hand, they’re a great way to test a product in the real world with lower risk. On the other, they often stall for predictable reasons:

No clear success metrics. Without defined outcomes, it’s hard to prove a pilot was worth paying for.

Solving the wrong problem. Flashy AI tricks don’t matter if they don’t address a core pain point.

AI curiosity, not commitment. Some companies just want to “check the AI box.”

Integration headaches. A standalone pilot may break down in real workflows and systems.

Too broad a focus. If your AI “does everything,” customers may not know what you actually solve.

Lessons from AI Startups That Escaped Pilot Purgatory

1. Hugging Face: Build a Community Before the Customers

We've talked about Hugging Face in past articles. It started as a chatbot app but pivoted when they saw demand for open-source AI tools. By fostering a developer-first community, they built credibility and adoption before monetizing.

Takeaway: Sometimes your first “customers” are users and developers who expand your reach.

2. Scale AI: Solve Painfully Specific Problems

Scale AI tackled a very specific problem, which was labeling training data. Their narrow focus won contracts with OpenAI, Cruise, and others.

Takeaway: Pick a specific problem that’s urgent and critical, and become the very best at solving that poblem. Launch a pilot with a clear plan to scale.

3. DataRobot: Sell ROI, Not Tech

DataRobot emphasized cost savings and faster predictions, not algorithms. Essentially, they focused on delivering clear business value and their ROI-driven messaging helped close deals.

Takeaway: Customers buy outcomes, not technology. Show the financial impact or some other way to deliver real business value to stand out from your competitors.

4. Gong: Build Insights Into the Workflow

Gong didn’t just analyze call transcripts, rather they delivered insights directly into sales managers’ workflows. This made adoption seamless, addressing a common barrier to new technology adoption.

Takeaway: Package insights so they fit naturally into the customer’s workflow, lowering the barrier for new technology adoption.

How do You Turn Pilots Into Paying Customers?

If you’re an AI founder worried about getting stuck in the pilot phase, then here are some steps to convert your experiment into recurring revenue:

Step 1: Choose Pilots Carefully

Ask: Does the company have budget authority? Is the problem urgent and tied to money or risk? Can impact be measured in 60–90 days?

Step 2: Define Success Metrics Upfront

Agree on adoption, accuracy, and ROI goals at the start and put them in the pilot agreement.

Step 3: Price for Commitment

Free pilots often go nowhere. Even small fees give customers skin in the game. Use tiered pricing to filter out “tire kickers.”

Step 4: Integrate Early

Don’t isolate your pilot. Integrate into workflows or systems from day one for higher adoption.

Step 5: Show Quick Wins

Design pilots to deliver visible results in 30–60 days to build momentum and executive support.

Step 6: Turn Champions Into Evangelists

Empower internal champions with dashboards, case studies, and wins they can brag about.

Step 7: Document and Scale

Each successful pilot should generate case studies, testimonials, and ROI data you can be used to drive future sales.

The Mindset Shift: From Producing Cool Tech to Becoming a Trusted Partner

The startups that thrive don’t just show off flashy AI toys. Rather, they solve real difficult problems, deliver measurable ROI, and fit into common workflows. They become partners, not vendors.

Remember, AI can be fun and exciting, but customers don’t buy excitement. They buy results.

Final Thought

Breaking out of pilot purgatory is the defining challenge for AI startups. But you can treat pilot as a springboard instead of the end result by choosing wisely, pricing smartly, and proving value. You’ll soon build traction that hype can’t deliver.

Because in the end, the AI startups that thrive aren’t the ones with the fanciest models. They’re the ones with the happiest customers.

Build Trust With Your AI Startup

Posted by Marcus Bourke on Saturday, October 4. 2025

Well, the past few weeks have unpacked the reasons why so many AI startups fail, what you can do to beat the odds and have even put together a survival guide. What could be next?

We know AI startups are all the rage. We also know that for every success story like OpenAI or Anthropic, there are dozens of AI startups that quietly vanish. The number one factor that separates survivors from failures? Trust. So, that's what's next this week. Let's talk about building trust.

Building an AI Startup That Investors and Customers Actually Trust

In a world flooded with overhyped promises and half-baked AI products, winning (and keeping) the trust of investors, customers, and end-users isn't just a good idea. It's the secret sauce. Let’s dig into some practical steps, real-world examples, and some templates you can start using to build trust with your customers and investors.

Why Does Trust Matter?

AI startups often overpromise, underdeliver, or hide key details about how their technology actually works. Customers and investors don’t just want cutting-edge models...they want transparency, reliability, and accountability. Without those, even the coolest AI demo won’t last long in the real world.

Case in point: Babylon Health, once valued at $4B, collapsed after questions arose about the accuracy and safety of its AI-powered medical claims. The tech itself wasn’t the demise. It was the lack of trust that killed the company.

Compare that with Anthropic or Perplexity AI, who lead with transparency and safety. They not only push “smarter” AI, but they emphasize guardrails, explainability, and ethical use. That’s what builds credibility and trust.

How to Build Trust: A Playbook for AI Startups

Here are some hey ways to build lasting trust with your AI startup.

1. Publish Trust Artifacts

Don’t just say you’re transparent. Every startup can do that. Remember, actions speak louder than words. Publish documents that spell out how your AI works, what it can and cannot do, and how you handle data. Then, do exactly what you say you're doing in those documents.

Model Card:

Include model name & version, release date, training-data summary, intended use cases, evaluation metrics, known limitations, and a support contact. See below for an example:

Model: Acme-Summarizer v1.0 (released 2025-08-01)
Trained on: Mix of public web data + anonymized customer docs
Intended use: Summarizing business text
Not for: Medical, legal, or safety-critical advice
Primary metrics: ROUGE-L 45, factuality 92% (sampled)
Known limits: May omit key facts; verify critical outputs

Datasheet for Datasets: Summarize sources, sampling, cleaning, and bias checks.

"What We Can’t Do Yet" Page: Openly and honestly list the limits of your AI product.

We do not provide medical diagnoses. Use our suggestions as drafts, not final decisions.

Security & Compliance Summary: List encryption, audits, and compliance status.

2. Use Operational Checklists

Checklists keep you honest and prevent oversight. Start with these three:

Data Governance Checklist

Inventory: what data you have, where it lives, who has access

Retention & deletion policy

Consent tracking for customer data

Anonymization / minimization steps

Immutable logs for dataset updates

Security Checklist

TLS + encryption at rest

Role-based access control (RBAC)

Secrets management

Automated backup + tested restore

Incident response runbook

Compliance Checklist

Data Protection Impact Assessment (DPIA) if handling personal data (GDPR)

Map requirements for SOC 2, HIPAA, ISO27001 as needed

3. Run Pilots That Prove Value

Pilots build trust when they’re structured. Consider using this four-phase approach:

Discovery: Map data, define success metrics

MVP: Deliver a working feature for small user group

Pilot: Limited production use with metrics tracking

Evaluate & Scale: Decide go/no-go with customer

Create Clear Pilot Success Criteria

Adoption: % of users using weekly

Accuracy: % of outputs verified correct

ROI: measurable savings or revenue lift

Safety: zero critical incidents

4. Test and Monitor Relentlessly

Trust grows when customers know you’re always testing and looking for issues or vulnerabilities. Here’s one way to do that:

Red Teaming: Stress-test your model quarterly

Human Sampling: Audit 1–2% of outputs

Monitors: Track uptime, cost, hallucination rate

Rollback Criteria: Predefine thresholds for disabling features or rolling back to a previous version

5. Track Trust Metrics

You can't just assume that you're building trust. You also can't guess at how well you're doing. You must measure it.

Quality: Accuracy, hallucination rate

Usage: Retention, adoption, daily & weekly active users

Business: Customer Churn, Net Revenue Retention (NRR), Lifetime Value (LTV) and Customer Acquisition Cost (CAC)

Support: Customer Issue Escalations, resolution time

Security: Incidents, audit findings

6. Communicate Transparently

Clear communication is half the battle.

Pre-Launch

Publish FAQs, model cards, and limitations upfront.

In-Product Disclaimers and Guidance

This content was generated by Acme AI. It may omit details. Click "Show Sources" to verify.

Incident Response Template

Timeline: what happened & when

Root cause

Impact

Mitigations

Preventive actions

7. Build Trust Into Your UX

Explain This Button: Show sources or reasoning

Confidence Scores: Simple ranges, not magic numbers

Feedback Loop: Easy reporting of bad outputs

Data Controls: Clear opt-outs for training data

8. Formalize Governance

Assign a Safety Owner

Create an external Ethics Board (if working in a regulated domain)

Conduct regular third-party audits

Align contracts & SLAs with reality

Key Takeaways

Building an AI startup that people actually trust isn’t about showing off the smartest model. It's not about the wow factor. It’s about making your work transparent, reliable, and accountable from day one and never deviating from that philosophy.

Publish trust artifacts

Run disciplined pilots

Track trust metrics

Communicate openly (especially when things go wrong)

Embed trust in product design and governance

Do this, and you won’t just avoid the AI startup graveyard, you’ll stand out from the crowd. Because in the long run, trust beats buzz every time.

AI Startup Survivor's Guide

Posted by Marcus Bourke on Saturday, September 27. 2025

Last week, we unpacked why 90% of AI startups flame out. That’s enough to make any founder clutch their pitch deck a little tighter. But here’s the good news...failure isn’t inevitable. There’s a growing list of AI companies that not only survived but thrived by doing things differently. What exactly are they doing differently? And more importantly, how can you do the same? Let's dig a little deeper into that this week.

This isn’t about recycling the “why they fail” conversation that we had last week. You already know that story. If not, go back and read it now. This is about what comes next. If you’re thinking about building your own AI venture, how do you give yourself a real shot at being one of the survivors? Let's see if we can build an survivor guide for you to follow.

The AI Startup Survival Guide: How to Build for the Long Haul

Lesson 1: Solve Problems That Will Still Exist in Five Years

AI tech is moving faster than a toddler on a sugar rush. Today’s breakthrough can be next year’s open-source commodity. That’s why surviving startups pick problems that outlast the hype cycle.

Take Duolingo. They didn’t launch as an “AI company.” They launched as a language-learning platform. But they leaned into AI as it matured, first for adaptive learning and now for conversational bots. The problem of learning new languages never went away. However, the AI technology kept making their solution better.

Your survival lesson: Ask yourself, "If a better model drops tomorrow, will my core problem still matter?" If yes, you’re on stable ground. If no, you’re building on quicksand.

Lesson 2: Be Useful Before You’re Impressive

Some of the best survivors started small and almost boring. Grammarly wasn’t sexy at first. It just fixed typos better than Word. But it solved a daily irritation for millions of people, then layered on smarter AI as the tech matured.

Meanwhile, flashy launches like the Humane AI Pin promised the future of computing but delivered a clunky device people abandoned in a drawer. Impressive? It certainly sounded impressive. Useful? Not really.

Your survival lesson: Resist the urge to wow consumers when you're first starting out. Focus on being ridiculously useful. Start with features people can’t live without. The “wow” can come later once you've proven your product or service in the marketplace.

Lesson 3: Build a Moat Beyond the Model

Here’s the tough truth...most of you won't own the AI models. OpenAI, Anthropic, Meta, and Google do. And their stuff will always be cheaper and faster. Survivors know this. They don’t compete on the model. They compete on everything wrapped around it. That's how they differentiate themselves and make it difficult to copy what they're doing.

Look at Perplexity AI. It doesn’t matter that other startups can hook into OpenAI’s API. Perplexity’s moat is their experience by providing clear, cited answers in a search-like interface. That’s their differentiator.

Your survival lesson: Build moats in data, workflow integration, user trust, a desirable experience or brand. Don’t hinge survival on access to a single model. Your competitors have access to that model too!

Lesson 4: Grow with Customers, Not Just Investors

Raising $100 million feels good, but it doesn’t guarantee survival. Just ask Zume, the robot pizza startup that raised nearly half a billion before collapsing.

Now contrast that with Writesonic, which bootstrapped revenue early by selling affordable tools to creators. Customers funded their growth, not just investors. Today, they’re cash-flow positive and expanding sustainably.

Your survival lesson: Make your customer your first investor. If people pay you, you’ve got validation that there is demand for your product or service. If VCs pay you, you’ve only got runway, and eventually runways run out.

Lesson 5: Keep One Foot in Today, One in Tomorrow

AI startups die in two traps:

Focusing only on today and they get leapfrogged by their competition, or

Focusing only on tomorrow and they handicap themselves while their competition consistently delivers today.

The survivors straddle both. Jasper AI started with copywriting but quickly evolved to serve marketing teams with workflows, brand voice tools, and enterprise features. They nailed “today” while planting seeds for “tomorrow.”

Your survival lesson: Build for the customer in front of you, but keep the innovation pipeline full by exploring where the tech is headed and planning for how to leverage that tech to evolve. That’s your insurance policy.

Lesson 6: Transparency Builds Trust

AI is still the wild west, and trust is rare currency. Startups that treat users like guinea pigs erode consumer trust quickly. Those that are transparent about data use, model limits, and even failures stand out. Customers feel comfortable doing business with them and nobody likes to leave their comfort zone.

Perplexity AI wins points by showing where its answers come from. Compare that to Babylon Health, which overhyped its diagnostic AI and imploded when reality didn’t match promises.

Your survival lesson: Be honest. Show your work. Users and regulators will reward transparency more than perfection. Make them comfortable and they'll relish the thought of leaving.

Lesson 7: Hire for Grit, Not Just Brilliance

AI attracts brilliant people. But brilliance without grit builds a house of cards. Survivors know they need teams that can grind through uncertainty, not just dazzle with ideas that might be short-lived.

Anthropic is a great example. Their “constitutional AI” approach came from disciplined, principled work, not a rush to hype the market. Their culture emphasizes alignment, resilience, and thoughtful execution. That’s a team that can last.

Your survival lesson: Hire fewer “genius founders” and more builders, operators, and pragmatists. Brilliance fades without grit, so build a sustainable model with a solid, well-rounded team.

The New Survival Mindset

If last week’s post was about avoiding landmines, this one’s about building momentum. Here’s the distilled mindset of survivors:

Pick enduring problems and solve them.

Be useful before you’re impressive.

Build moats beyond the model.

Grow with customers, not just investors.

Keep one foot in today, one in tomorrow.

Lead with transparency.

Hire gritty teams, not just brilliant ones.

It’s not about being the “AI-first” startup. It’s about being the problem-first startup that uses AI wisely now while simultaneously building for the future.

Closing Thought

Yes, the odds are scary. But every AI unicorn you’ve heard of, whether it be Grammarly, Duolingo, Perplexity, Anthropic, started with the same odds. They survived because they remembered the golden rule: AI is the enabling tool, not the business.

So if you’re building right now, take a breath. Slow the hype train. Focus on solving something real, earning trust, and playing the long game. The rest? That’s survivorship in action.

AI Startup Failures and How to Beat the Odds

Posted by Marcus Bourke on Saturday, September 20. 2025

If you read last week's post, then now you know that you can leverage open source LLMs and do some powerful stuff. This greatly reduces the startup cost if you're looking to launch your own AI business. But is that a good idea? Are AI startups actually turning a profit or are they just hype? Here's a hint...things aren't looking so hot for most AI startups. Knowing that, can your startup idea actually succeed? Let's investigate the current state of AI startups today.

Why So Many AI Startups Fail And How Yours Can Beat The Odds

AI startups are all the rage right now. Everyone’s dreaming of launching the next ChatGPT. But here’s the harsh truth: AI startups have an alarmingly high failure rate. It’s not just people on social media spreading gloom and doom. According to recent studies, around 90–92% of AI startups fail (AIM Media House, AI4SP). And even beyond startups, an MIT report found that 95% of generative AI projects in businesses don’t produce any meaningful results (yahoo!finance, Tom’s Hardware).

So why? And more importantly, what can you do to avoid common pitfalls and beat the odds? Let’s dig in.

What’s Causing Such a High Failure Rate?

1. Chasing Technology

When a new AI model drops, it’s tempting to hop on the bandwagon. But launching with the flashiest tech doesn’t mean your idea will land. Aakash Gupta highlights in his article that founders get distracted by demos, chasing technology instead of users. The lesson? Focus fiercely on a clear use case...and nail it (Aakash Gupta).

2. Theory vs the Real World

Demos are great, but they are designed to sell a product and rarely function as well under real world scenarios. Humane’s AI Pin, for example, promised to replace smartphones, only to fail spectacularly. It left customers with dead devices and no compensation (cnet.com).

3. Failure to Solve a Real Problem

If nobody needs your product, it fails. Period. According to AIM Media House, lack of demand and poor product–market fit are leading causes of failure. RAND’s research confirms that many AI projects falter because teams misalign on what problem they're solving.

4. Data and Infrastructure Gaps

AI thrives on quality data and solid infrastructure. Too often, startups lack clean, accurate data, or don’t invest in pipelines and deployment frameworks, and that’s a recipe for disaster.

5. Lack of Product Alignment

Some AI startups seek funding before actually aligning their products to the market. With large sums of venture capital in hand, they launch their products and then learn that there is no clear target market.

6. High Compliance Burdens

AI startups face an evolving regulatory landscape. Compliance can eat up massive chunks of resources, putting them in a “compliance trap.”

7. Talent and Capital Constraints

AI needs deep expertise and costly compute. Starsky Robotics, a self-driving truck startup, shut down because talent and tech advances didn’t match investor expectations.

8. Hype vs. Reality

Nicknames like “AI bubble” are popping up. OpenAI’s Sam Altman warns that we’re seeing elevated excitement, and inflated valuations that don’t always match real-world impact.

Real AI Startup Failures and What They Teach Us

Builder.ai: Claimed to offer AI-powered app development but mostly used humans. The company filed for bankruptcy amid misreporting concerns.

Forward (CarePods): Raised $650M for AI-powered medical pods. Technical failures + poor adoption led to their collapse.

Humane's AI Pin: The $700 wearable phone replacement. Launched, fizzled, and shut down in under a year.

Babylon Health: Once valued at $4.2B, fell to regulatory woes and poor scaling in healthcare.

Starsky Robotics: Autonomous trucking pioneer. Demos were impressive, but they couldn’t sustain funding or pace of innovation.

Enterprise AI pilots: MIT found that 95% fail, largely due to poor integration and weak ROI.

How to Tilt the Odds in Your Favor

1. Start Small, Solve a Real Problem

Be ruthlessly specific. The most successful pilots focus on solving single well-defined problems, often in back-office automation, not grandiose promises.

2. Build Real Infrastructure

Cohere spent years building a stable serving platform. By the time they launched their API, they had $1M ARR and real traction.

3. Lean Startup + AI = Magic

Pair AI with Lean Startup principles: MVPs, rapid testing, feedback loops. Iterate smartly before scaling.

4. Fail Fast (But Learn Faster)

Test early, pivot quickly. Be disciplined and learn from failure. Failures should provide valuable lessons, not just burn resources.

5. Invest in Data & Compliance Early

Ensure clean data and proper governance upfront. In regulated sectors, compliance is not just protection, it’s a competitive advantage.

6. Bootstrap Your Idea

Test the market and prove demand before chasing big VC rounds. Every dollar should build additional value for an existing market, not just hype.

7. Build a Resilient Core

Writesonic thrives by staying lean and modular, balancing infrastructure across multiple models to reduce risk.

8. Build the Right Team & Culture

Focus on building a diverse team, establishing clear ownership, and building transparency to establish a solid foundation. Avoid founder syndrome, and build a culture of resilience.

AI Startups That Thrive

Cohere: Takes an infrastructure-first approach and sought focused traction before scaling.

Anthropic: Focuses on constitutional AI with clarity and discipline in innovation.

Writesonic: Aims to be modular, cost-conscious, and relentlessly user-focused.

MIT’s 5% Success Stories: The few firms that made AI pay off were pragmatic, adaptive, and grounded in ROI. Essentially, they focused on sound business principles

Final Thoughts

Look, I get why AI looks like a gold rush. But it's also a minefield. The winners? They're not blazing a new trail in the wild frontier. Rather, they’re picking their focus, identifying their market, building rock-solid foundations, and growing intentionally. If you're thinking of launching an AI startup, or advising one, here’s are some tips:

Define one tangible problem.

Build just enough to solve it reliably.

Learn from every failure (fast).

Secure your data, and don’t ignore compliance.

Bootstrap market fit before chasing VC.

Keep your team diverse and grounded.

Choose users over hype and deliver consistent value.

Done right, AI may just be the biggest opportunity you’ll actually get. So, go build your future, but build it with intentionality, focus, and care.

Open Source LLMs

Posted by Marcus Bourke on Saturday, September 13. 2025

We took a little detour last week and focused on AI in healthcare. Admittedly, if you're not in healthcare, then it may not have been applicable to you. Today, let's get back to a topic that's more applicable to the AI masses. Have you heard of open source LLMs? Have you heard that they can be run locally on your own hardware? Let's unpack this topic today.

Use Open-Source LLMs to Power Your Business (Without Losing Your Soul)

Whether you're scrambling to keep your business afloat or closing in on the next level like a run away freight train, you've no doubt been studying how to best leverage AI. If you've heard people talking about open-source LLMs that they run locally, you're now tuned into one of the most exciting shifts in AI adoption. Let's walk you through why they matter, what's actually available, what they’re capable of, and how they stack up against the likes of ChatGPT.

1. Why Open-Source LLMs Could Be Beneficial To Your Business

Privacy & data control. Open source LLMs run locally, so your data stays on your machine. No surprises. No “Did they train on my emails?” anxiety. That’s gold for lawyers, healthcare, or any business handling sensitive info.

No vendor lock-in. Open-source = freedom. You can tweak the model, move it, or extend it. If the vendor used by our competitor changes pricing or terms, guess what? You're still in business because you're not affected.

Offline resilience. Internet cut out? You're still in AI mode. For offline environments or reports to the boss when the network is dodgy, local AI can be lifesaving.

Cost control. After the initial setup, you're not paying per token, per query, per trial. Just your hardware and electricity. Compare that to ChatGPT subscriptions and usage fees, that can add up. It definitely tips the scales in favor of an open source model.

Learning by doing. Want to understand how the sausage is made? This is your chance. Tweak the prompts, inspect the internals, adapt the logic. You'll build deep AI expertise, and that's experience you can sell.

2. So, What's Actually Available? The Who's Who of Open-Source LLMs (Mid-2025 Edition)

OpenAI’s New Move: gpt-oss-20b and gpt-oss-120b

Yes, the same OpenAI that offers ChatGPT has an open source model. It delivered it's first open-weight models in years, available under Apache-2 on Hugging Face. Chain-of-thought capable, customizable, and runnable fully offline. The 20B version can run on consumer gear (~16 GB RAM), while the 120B model competes with proprietary options in benchmarks.

Mistral AI

Known for strong open-source performance, Mixtral series, now Mistral Small 3.1, Mistral Medium 3, and innovative reasoning models like Magistral Small and Magistral Medium (chain-of-thought capable).

Gemma (Google DeepMind)

Lightweight, powerful, and open. Gemma 3 released March 2025. It's ideal for running locally on smaller hardware and it's also multilingual and multimodal.

BLOOM

A massive 176B-parameter multilingual model from the BigScience open-science initiative. 46 human + 13 coding languages supported. Entirely open.

DBRX (Databricks / Mosaic)

A 132-billion-parameter mixture-of-experts model and only part of it activates per token. Delivered on benchmarks, dominating LLaMA 2, Mistral’s Mixtral, and xAI’s Grok in tests. Released under an open license.

IBM Granite

Coding-focused models (e.g., Granite 13B) outperform LLaMA 3 in code tasks. Fully open under Apache-2.

EleutherAI’s Legacy Models

GPT-Neo, GPT-J, GPT-NeoX, and Pythia are all open and foundational in the community. Great if you're a researcher or tinkerer.

TinyLlama

A 1.1B model based on LLaMA 2 architecture. It's tiny, efficient, expressive. It also handles downstream tasks impressively for its size.

Others (Emerging/TBD)

China’s DeepSeek R1, Qwen, Moonshot, MiniMax, Z.ai, etc., are starting to appear in open-weight form. Use cases are growing fast.

3. Which Models Shine Right Now and Why

Broad Capability + Practicality: gpt-oss-20b hits a sweet spot with solid performance and lower resource needs.

Raw Power: gpt-oss-120b and DBRX hold their own vs. proprietary models in benchmarks.

Reasoning-First Models: Magistral Small & Medium are built for chain-of-thought logic.

Small but Mighty: Gemma 3 and TinyLlama are super accessible with modest hardware.

Multilingual & Large-Scale: BLOOM is the multilingual go-to.

Domain-Specific: Granite 13B shines in code-heavy tasks.

4. Capabilities You Can Expect from Local, Open-Source LLMs

Capability	What It Means for You
Prompting & Chat	Full conversation ability. Edit prompts locally and control flows without leakage.
Reasoning / Chain-of-Thought	Models like Magistral, gpt-oss, DBRX support step-by-step logic, not just surface responses.
Fine-Tuning / Customization	Retrain or prompt-tune on your own data. No “locked API.”
Multimodal / Language Flexibility	Gemma supports multimodal and BLOOM speaks many tongues.
Coding / Analysis	IBM Granite and DBRX handle code generation and logic tasks with ease.
Offline Operation	Works without the internet. Perfect for secure or remote environments.
Hardware Adaptability	From laptops to GPUs: TinyLlama and Gemma 3 work on modest gear, but gpt-oss-120b needs serious machines.

5. Open-Source vs. ChatGPT (or Other Hosted LLMs) — What’s the Trade-Off?

ChatGPT / Hosted LLMs (e.g., ChatGPT, Claude, DeepSeek Chat)

Pros: Cutting-edge, polished, constantly improving UI, plug-and-play, with tools like browsing or plugins.

Cons: Cost per use, data leaves your control, potential vendor policy shifts, black box / locked logic, API limits.

Open-Source Local LLMs

Pros: Full ownership, privacy, customization, no per-token fees, offline usage, learning opportunities.

Cons: Setup time, hardware requirements, model maintenance. Not as flashy out of the box...yet.

6. Who Should Pick What, and When?

Go Open-Source If:

Data privacy is non-negotiable.

You want full control and flexibility.

You're tech-savvy, or at least up for learning.

Budget-conscious with ongoing volumes.

You want to build deep AI expertise.

Stick with ChatGPT-style If:

You need results fast, with zero setup.

You need tools like image, code, or browsing plugins.

You're OK with recurring payments.

You're fine with limited customization.

7. Making the Leap (Without Risking Everything)

Start small. Try TinyLlama or Gemma 3 on a test project. Spend some time to get comfortable with it.

Match the model to the task. For code, try Granite or DBRX. For logic, Magistral or gpt-oss.

Plan hardware. Consider Gemma/TinyLlama to run on minimal hardware requirements. Choose gpt-oss-120B if you have serious GPU power available to run it. Balance ability vs. access.

Run pilot experiments. Compare prompts, latency, accuracy vs. ChatGPT.

Evolve gradually. Once you're comfortable, layer in tuning, domain-specific enhancements, custom UI, or enterprise deployment.

Final Thoughts: Embrace the AI, Keep Your Voice

Here’s the deal: picking an LLM isn’t just about functionality, it’s about alignment. Do you want AI to feel like a glitzy black box or a tool that amplifies your voice, your values, your way? Open-source LLMs let you keep a human in the loop. They give you control. And they give you the chance to level up, not just your AI, but your ownership of it. So, pick the right LLM for you and let it do the heavy lifting for you.

Current State of AI in Healthcare

Posted by Marcus Bourke on Saturday, September 6. 2025

Last week was dedicated to the corporate employee. Particularly those who are interested in making the leap to entrepreneurship and want to leverage AI to do so. Let's dedicate this week to leaders. Not just any leaders, but healthcare leaders and investors. AI is showing up in healthcare in a big way. As a healthcare leader, it's good to have a solid understanding of the current state of AI in your field. How else can you make informed investment decision. Given that, let's lay out the current state now.

AI in Healthcare: What Healthcare Leaders and Investors Should Know Right Now (and the next 1–2 years)

If you run a hospital system, a clinic network, or a health-tech shop and someone on your team hasn’t already suggested “let’s do AI”, they probably will soon. AI isn't just some novelty technology that will fade away as fast as it blew up. No, it's rapidly moving into the healthcare space in big ways. It can already be found in areas like imaging, screening, workflow orchestration, and even clinical knowledge work. But, as always, hearing “it works” is not the same as “it’s ready for prime time in your org.” Let's take a realistic look at AI in the healthcare space, so you can make better decisions over the next 12–24 months on what's a fit and what isn't.

What AI capabilities already exist in healthcare?

AI’s presence in healthcare is no longer just about prototypes or academic studies. Today, it spans several mature and emerging categories:

Diagnostic imaging & triage: Algorithms can detect conditions like intracranial hemorrhage, stroke, pulmonary embolus, and suspicious lesions on mammograms and CT scans. Some systems prioritize urgent cases, alerting radiologists in real time, while others integrate seamlessly with Picture Archiving and Communication Systems (PACS) for concurrent reading. In some regions, autonomous AI in mammography screening has already shown measurable improvements in detection rates.

Autonomous point-of-care screening: FDA-cleared systems such as those for diabetic retinopathy screening allow primary care providers to offer specialized diagnostics without needing on-site specialists. These tools enable earlier detection and treatment while reducing patient referral delays.

Clinical decision support & workflow automation: AI-enhanced systems now integrate with Electronic Health Records (EHRs) to recommend personalized care pathways, flag drug-to-drug interactions, and even suggest preventive interventions based on patient history and population health data.

Large language models (LLMs): Beyond summarizing clinical notes and drafting patient instructions, LLMs are being embedded into secure medical knowledge assistants for clinicians, enabling human-language queries of guidelines, research papers, and patient charts.

Remote monitoring & predictive analytics: AI-powered wearable integration can detect early signs of patient deterioration, predict hospital readmissions, and trigger early interventions. These systems are particularly effective in chronic disease management, ICU monitoring, and post-surgical recovery.

Operational optimization: Predictive staffing models, supply chain demand forecasting, and operating room scheduling optimization are already in play, leading to reduced costs and more efficient use of resources.

Clearly, AI is already being used on a daily basis in meaningful ways. Many experts would say that this is the tip of the iceberg. With pressure to reduce cost, clinical staff shortages, and desire to improve outcomes, it's fair so say that AI will become more prevalent in the healthcare setting...not less. But, are they really accurate?

In specific, narrow tasks, accuracy can rival, and even sometimes exceed, that of human experts. Examples include AI-assisted mammography screening improving detection rates, and autonomous diabetic-retinopathy screening with robust sensitivity and acceptable specificity. But accuracy is task and data dependent. Prospective, local evaluations are still essential to validate acceptable accuracy in your specific situation.

What about ethics and legal implications? Are these systems even ethical and legal to use?

Bias & fairness: AI can perpetuate biases from training data, so audits and subgroup performance reporting are essential to ensure the system is functioning fairly and without bias.

Clinical validity vs. outcomes: Better detection isn’t always better care, so focus on outcomes. Prioritize investment in systems that actually improve outcome first.

Privacy & data governance: HIPAA compliance and proper governance are critical. Both are feasible, but it requires proper controls, a strong understanding of the model algorithms, strong AI governance and frequent audits to truly protect patient privacy.

Regulatory compliance: Verify claims against FDA resources and clearance pathways. Never trust marketing materials or the sales person. Ensuring the system will meet compliance requirements ahead of time will save a lot of time and money compared to finding out post-implementation.

So, what impact can I expect in the next 1–2 years?

AI’s short-term impact in healthcare is likely to be transformative in small, meaningful steps rather than massive overnight shifts. Here’s what leaders can realistically expect:

Workflow acceleration, not replacement: AI will continue to take over high-volume, low-complexity tasks. This may include things like triaging imaging studies, automating documentation, and routing critical alerts. This will free clinicians for more complex cases and decision-making, directly improving throughput and reducing burnout.

Point-of-care screening expansion: Autonomous AI systems will become more common in primary care and retail health settings, expanding access to diagnostics for underserved populations, particularly in ophthalmology, dermatology, and certain cardiovascular screenings.

LLMs in clinician tooling (cautiously): LLM-powered assistants will mature into reliable, context-aware helpers for physicians, nurses, and administrative staff. Expect integration into EHRs for natural language search, clinical summarization, and evidence synthesis, but always with human review safeguards. Some call this Augmented Intelligence to stress the importance of having a human in the loop.

Regulatory normalization: The FDA and equivalent bodies worldwide will publish more refined guidelines for adaptive AI, making compliance clearer and lowering the barrier for enterprise adoption.

Operational optimization at scale: Predictive AI for staffing, bed management, and supply chains will become mainstream, yielding cost savings and better patient flow, especially in high-volume hospitals.

Personalized treatment recommendations: Advances in AI-driven genomics interpretation and treatment response prediction will begin to influence oncology, rare diseases, and pharmacogenomics-based prescribing, albeit in controlled, evidence-driven environments.

How should a leader go about evaluating an AI solution before investing?

Here's a quick punch list to use as a framework for evaluating an AI solution. Obviously, each of these steps may require a lot of time and effort to complete, but you don't want to cut corners here. The time and effort invested now will pay dividends down the road.

Define the clinical question & expected outcomes.

Check regulatory status & existing evidence.

Plan a proof of concept for local validation.

Ensure data governance for the local validation.

Test for bias & fairness.

Assess integration & workflow complexity and potential impact.

Plan ongoing monitoring & maintenance needs and costs.

Pending a successful local validation, build the economic case for full scale implementation.

Additionally, consider the following:

Create an AI governance board to govern both this investment and future AI investments.

Demand vendor transparency to ensure you understand how the models work and what's changing when new versions are released.

Invest in observability and re-validation once full scale implementation is complete.

Train clinicians on how to use the model and, just as importantly, what the limits are.

Final thoughts

AI in healthcare is no longer theoretical. Narrow, validated models are delivering value today. Over the next 1–2 years, organizations that demand evidence, pilot locally, govern ethically, and measure outcomes will see the greatest benefits. If you invest the sweat equity in governance and validation now, AI will be an amplifier for safer, faster, more equitable care rather than some risky experiment sitting in a vendor slide deck.

Escape The Corporate World With AI

Posted by Marcus Bourke on Saturday, August 30. 2025

We learned last week about how AI can help with e-commerce. If you're in the corporate world, that may have gotten the wheels turning in your mind about leaving and starting your own business. Maybe it's your dream, but it feels too daunting. Afterall, you have responsibilities.

Well, I'd argue that it's not completely impossible to exit the corporate world and start your own business. Yes, there's risk involved, but there's risk in being a corporate employee these days too. Let's spend some time today examining one way to make the leap from employee to entrepreneur.

Escaping the Corporate World (Safely) and Launching Your Own AI Business

How to leave your job without losing your mind, your money, or your momentum.

Let’s start with the cold, hard truth

If you’re in a corporate job right now, you’re probably watching what little job security you may have had fly out the window. Layoffs are increasing. Budgets are shrinking. Meetings are multiplying. And maybe, just maybe, your soul is quietly shriveling.

But here’s the good news...AI is creating new types of businesses that never existed before. Jobs that don't necessarily require staying in Corporate America. And the best part? You don’t have to be a tech genius or burn your savings to participate.

You just need a solid strategy and plan to help you make the transition from the corporate world to running your own AI-based business. Let's see if we can help you with that today.

Part 1: Mindset Shift — From Employee to Entrepreneur

Leaving corporate life isn’t just a career move, it’s an identity shift. A huge change. You’re going from:

Doer → Thinker

Execution-focused → Value-focused

Task-based → Outcome-based

Stable paycheck → Self-driven income

This shift takes intentionality. Every hour you spend on your side business should be on laying a solid foundation and building assets (products, audience, systems) rather than just checking boxes. Every move should bring you one step close to exiting the corporate world.

Here are Some Mini-Mindset Exercises to Help Shift Your Thinking:

Audit your time: track how much of your day is spent reacting to stuff in your corporate environment vs. being strategic and proactive.

Write out your “anti-resume”: what you don’t want in your new AI-based business.

Ask: What do I know that others would pay for if it was packaged and delivered clearly, with AI automation behind it?

Part 2: Pick the Right AI Business for You

Here’s are four possible beginner-friendly AI-powered business types to consider, and what it takes to launch them. Use this a launchpad to finding the right business for you.

1. Launch an AI-Augmented Consulting or Coaching Business

Use your subject matter expertise, combined with relevant AI, to deliver high-value insights to clients

Great for corporate veterans who want to pursue high-end B2B work

Examples: Fractional AI marketing advisor, AI onboarding consultant, leadership coach with AI-enhanced tools.

Tools to learn: ChatGPT, Notion, Loom

2. Start an AI Micro-Agency

Offer done-for-you services powered by AI

Scalable business model with repeatable client work

Examples: Create social media content, Set up AI chatbots, SEO content repurposing.

Tools to learn: Canva, ChatGPT, Zapier, Descript

3. Create and Sell AI-Enhanced Digital Products

Create the product once and sell repeatedly

Perfect for creators and introverts who shy away from face-to-face sales tactics

Example products: Prompt packs, workbooks, Notion templates.

Tools to learn: Gumroad, Canva, Beehiiv

4. Develop Low-Code AI SaaS (Software as a Service) or Other AI Tools

Build niche tools driven by AI APIs for clients to interface with

High risk business model with high reward potential

Example tools: AI resume analyzer, grant-writing chatbot, AI meeting summarizer.

Tools to explore: Bubble, OpenAI API, Stripe

Part 3: A Realistic 6-Phase Transition Plan

Phase 0 – Complete Your Pre-Flight Checklist

Build an emergency fund: Save up 3–6 months of expenses to support you during your transition

Establish your income goal: know your target income from your new business to safely exit the corporate world

Get Health insurance: research options, understand costs and have a plan to sign up before your corporate insurance lapses

Do a skills audit: what skills do you have that you can magnify and monetize with AI?

Phase 1 – Pilot Your Side Business (3–6 months)

Pick one business model and validate it with a proof of concept (POC)

Launch a simple offer with payment methods and other essentials, such as a scheduler, to launch the POC

Get your first 3–5 test clients, which can be discouraging process...so, stick with it

Milestone: Work to achieve your first $1,000–$2,000 in side income...then celebrate

Phase 2 – Build Systems & Recurring Revenue

Build repeatable systems and automate everything you can

Document the client process

Scale your business to 2–3 clients or deliver a second product

Milestone: $2,500–$4,000/month of recurring revenue

Phase 3 – Budget & Timeline Your Exit

Track your 3-month income average

Forecast a 6-month sales pipeline

Secure 1–2 anchor clients

Milestone: 3 consistent months of side income that matches or exceeds your 3-month average + 6 months runway

Phase 4 – Give Your Notice & Shift Full-Time to your Business

Exit the corporate world gracefully without burning any bridges

Publicly launch your business

Focus your time: 30% marketing, 40% delivery, 30% systems

Phase 5 – Strategically Expand Your Business

Develop and promote upsell and client retainer packages

Build a content engine (newsletter, social media, podcast, etc.)

Hire a virtual assistant or support staff if needed

Milestone: $7K–$10K/month consistent revenue

Phase 6 – Long-Term Plan

Build scalable assets: courses, community, IP

Systemize growth and operations

License or white-label your frameworks

Part 4: Tools to Stay on Track

Project management: Notion, Trello

Financial tracking: Wave, QuickBooks

Learning: LinkedIn Learning, FutureTools.io

Community: The AI Exchange, FailingCompany

Final Thought: This Doesn't Have to be a Leap, it can be a Smooth Staircase

Corporate escape doesn't have to a single gut wrenching decision. It can be an intentional transition. Each phase gives you more confidence, financial safety and personal clarity. You already have the hard-earned experience. Now pair it with leverage. Build slowly. Exit wisely. Grow confidently. And when you're doubting yourself? Remember:

“If AI can automate the routine, your job is to make what you do irreplaceably human.”

You’ve got this.

AI Advancing E-Commerce

Posted by Marcus Bourke on Saturday, August 23. 2025

You may have thought that last week's post was a little gloom and doom as we dug into the impact of AI on corporate jobs. Hopefully you saw the bright side and didn't feel too depressed about the future. Let's lighten the tone a bit this week. What's the latest with how AI is being used in E-commerce? I've covered a lot of this in previous posts, but it's been awhile. Let's see what's new!

Welcome to the Future: AI and E-Commerce

If you've ever wondered whether AI really belongs in your online storefront or if it’s more buzzword than business tool, then you’re not alone. Actually, Today AI-enabled E-commerce sites feel like we're living in the future. You'll find Customer service bots that actually understand nuance, dynamic product recommendations that feel personal (not creepy), hands‑off inventory forecasts, and marketing campaigns that write themselves. It’s no longer sci‑fi, it’s just smart business.

Let’s walk through what's changed recently, then break down how concepts like AIO vs. SEO fit in, and finally map out how AI can turbo‑charge your own E-commerce shop, whether it's B2C or B2B.

Recent Advancements in AI for E-Commerce

1. Conversational AI and AI‑Powered Support

AI chatbots are no longer constrained to simple FAQs. They can now hold fairly natural conversations, recommend products, handle returns, and escalate to humans when needed.

Vendors like Zendesk and Intercom integrate large language models (LLMs) to power even more capable bots.

2. Personalization and Recommendation Engines

Algorithms use browsing, past purchases, and even seasonality to suggest products dynamically.

LLM‑based embeddings can even connect shoppers with products that semantically match their taste.

3. AI for Inventory Forecasting and Supply Chain

Machine Learning tools reduce stockouts and overstock by predicting demand per SKU, region, and day.

Some ingest external signals like Google Trends and social media to detect emerging trends early.

4. AI‑Generated Content, Copy & Visuals

Product descriptions, category pages, and blog content can be created or enhanced with LLMs.

Visual tools like DALL·E allow fast generation of mockups and lifestyle imagery.

5. AI in Pricing & Promotions

AI tools help with Dynamic pricing adjusts based on demand, competition, and stock levels.

AI tools also suggest bundles or deals to increase conversion and profit margins.

6. Voice Commerce & Visual Search

Shoppers can use voice assistants and image search to find products.

AI visual recognition improves conversion for mobile-first or discovery-heavy products.

AIO vs. SEO: What’s That?

SEO — Search Engine Optimization

SEO has been around for a very long time. It optimizes your content, site structure, and keywords to rank well on search engines like Google. It’s about organic traffic acquisition.

AIO — AI Optimization

AIO focuses on optimizing your business using AI tools, improving everything from customer experience and conversion to pricing and operations. If SEO gets people to your site, AIO makes sure they convert and stick around.

How Can AI Optimize Your E‑Commerce Business?

1. Start with Clear Business Goals

Define 1–3 measurable goals, such as:

Increase conversion rate by 10%

Improve average order value (AOV)

Reduce customer support costs

2. Clean Your Data

AI is only as smart as the data it’s given. Clean product catalogs, customer profiles, and purchase histories are critical to success.

3. Quick‑Win Use Cases to Test

a) Smart Chat & Support Assistants

Deploy an AI chatbot to handle common questions and track resolution rate and customer satisfaction.

b) Personalized Product Recommendations

Implement AI engines that show relevant suggestions and A/B test their effectiveness.

c) AI‑Generated Content

Use AI for descriptions, FAQs, and SEO copy, but always review and polish before publishing.

d) Demand Forecasting

Pilot forecasting for top SKUs and compare forecast vs. actual performance.

e) Dynamic Pricing & Bundles

Automate pricing based on velocity and margin, and test AI-suggested bundles.

f) Visual & Voice Search

Add functionality to let customers search using voice or images, which is particularly useful for fashion and home goods.

4. Build Your AI Roadmap

Triage Zone: Chatbots & recommendations

Content Zone: AI-driven product content

Operations Zone: Forecasting, pricing

Emerging Zone: Voice, visual search, logistics

5. Measure, Iterate, Human Oversee

Track KPIs, run monthly reviews, and involve a human AI integrator. Don’t try to automate empathy.

6. Ethical Considerations

Be transparent about AI usage. Respect privacy and avoid over-personalization that could feel intrusive.

Some Hypothetical Examples

Example 1: Boutique Apparel Shop

Deployed chatbot and personalized product recommendations.

Results: 10% conversion increase and 40% ticket deflection for customer service tickets.

Example 2: Specialty Home Goods Brand

Used AI-generated descriptions and dynamic pricing tools.

Results: 12% increase in Average Order Value, boosting profit margin by 8%.

Example 3: Niche Electronics Store

Piloted demand forecasting and upsell bundles.

Results: 20% stockout reduction, resulting in higher average revenue per customer per transaction.

Putting It All Together: From SEO to AIO Success

If you've operated an E-commerce site for any length of time, you probably already invest in SEO. AIO complements that effort by optimizing everything that happens after the click.

With AIO, you’ll:

Convert more visitors to paying customers

Serve customers faster and better

Predict demand and automate pricing

Free up time for strategic work

Let SEO bring the people, then let AI turn them into buyers.

A Few Tools & Platforms to Research (as of Mid‑2025)

Chatbots: Intercom, Ada, Tidio

Personalization: Klevu, Vue.ai, Nosto

Content: Jasper AI, Copy.ai

Forecasting: Inventory Planner, Lokad

Pricing: Prisync, Omnia Retail

Visual Search: Syte, ViSenze

Important Reminder: Always pilot before full rollout.

Common Mistakes to Avoid

Skipping goal setting and other business basics while jumping right to AI

Using bad data and expecting solid AI results

Expecting AI to fully replace people

Overdoing personalization

Chasing every shiny tool without strategy

Final Thoughts: Lead Your Business Through AI

E‑commerce today isn’t just about having a sleek store. It's highly competitive and requires running an intelligent, efficient, data-driven operation. AI can help build a competitive advantage. So, you need to blend tried and true SEO with high-performing AIO. Start small, stay curious about what works, and boldy lead your business into this new era. Remember, AI isn’t something to think about for the future, it should be your co‑pilot today.

AI Impact on Corporate Jobs

Posted by Marcus Bourke on Saturday, August 16. 2025

Can you still be a thought leader in this new brave world of AI? That's what we discussed last week. Spoiler alert, it's not all gloom and doom.

Speaking of gloom and doom, what about AI's impact on corporate jobs? It seems like most people I know in the corporate world are either concerned that they'll soon lose their job to AI or they're a leader who's actively trying to find a home for AI on their team. Are most corporated jobs doomed? What's the truth? Let's shed some light on that today.

AI Is No Longer the Future...it’s the Workplace Reality

Artificial Intelligence isn’t lurking around the corner anymore. It’s in the boardrooms, the inboxes, the HR systems, the customer‑service chatbots...and it's already reshaping corporate jobs in the U.S. and globally.

What’s Happening Now

White‑collar entry‑level jobs are under the most pressure today. CEOs from big tech and finance firms warn that AI agents are reaching the productivity level of junior roles. Anthropic’s Dario Amodei has said up to 50% of entry‑level white‑collar jobs in tech, finance, law, and consulting could vanish within five years.

Even more immediately, HR, recruiting, and administrative roles are in the crosshairs. AI browser agents like Comet are already automating the work of recruiters and executive assistants by managing outreach, scheduling, and correspondence, with minimal human input.

Walmart is rolling out “AI super-agents” for employees to handle internal requests like onboarding, PTO, performance data, and order entry. And companies like ServiceNow are projecting $100 million in savings in 2025 largely by not hiring humans for roles they now automate.

Meanwhile, the World Economic Forum reports that 40% of employers expect to reduce headcount in jobs heavily exposed to automation. While new roles will be created, the old ones are fading fast.

Who’s Feeling It Most - What Roles and Tasks?

High‑risk roles today:

Administrative and data‑entry positions

Recruiting and HR coordinators

Junior analysts and scheduling assistants

Customer‑service agents handling routine inquiries

Fields with increasing risk over the next 6 to 12 months:

Junior roles in finance, consulting, and legal services

Software developers handling boilerplate tasks

Marketing and content production teams

Surveys show employees estimate that as much as 30% of their current workload will be replaced by AI within the next year. Leaders often underestimate this exposure, meaning your instincts are probably more accurate than your executive team’s.

What Size Company and What Industries Are Hit Hardest?

Large enterprises in tech, finance, retail, and consulting are leading the charge. Microsoft, IBM, Amazon, and JPMorgan have all announced headcount reductions or hiring freezes directly tied to AI efficiency gains. Meanwhile, mid‑size companies are starting to restructure workflows rather than slash jobs, but many are following closely behind the big players. Then there are small businesses which are deploying AI tools to boost productivity, but most aren't materially cutting staff yet. They're using AI to do more with fewer resources and not necessarily to eliminate headcount.

Most affected industries include:

Professional services (consulting, legal, IT)

Finance and banking

Retail and logistics

Office and administrative support

Manufacturing (especially in repetitive production roles)

What About Re‑deployment? Are People Getting New Jobs?

Hard numbers are elusive here, but signals suggest most workers aren’t simply laid off. They’re redeployed or re-skilled.

The WEF projects 97 million new jobs by 2030 globally to offset 92 million lost.

PWC research shows higher wages in roles that require AI tools or collaboration.

Human-AI collaboration skills like digital literacy, judgment, and ethics are becoming critical differentiators.

There’s no clean “60% redeployed” stat yet, but early data suggest that transformation, not total job loss, is the trend.

The Raw Truth (Without the Spin)

Routine knowledge work is already being automated. AI is handling inboxes, calendars, data entry, and even simple strategy tasks.

Big companies are acting now. Layoffs, hiring freezes, and reorgs are happening because of AI efficiency, not despite it.

CEOs are being direct: half of white-collar jobs could disappear in the next five years.

Transformation, not mass unemployment, is the current trend. New roles are coming, even as old ones fade.

Upskilling is the lifeboat. People who adapt to work alongside AI, rather than fight it, will thrive.

The Bright Side: Why You’re Not Doomed

Wage premiums are going up for AI-skilled workers.

New job titles like AI auditor, prompt engineer and ethics officer are being created.

You’re early in the change. Most companies are still in pilot phase. There’s still time to learn, adapt and lead.

What to Do as a Corporate Employee (Coach’s Advice)

Shift from tasks to skills: Learn prompt engineering, AI governance, and digital fluency.

Ask about training programs: If your company doesn’t offer one, ask why not.

Look laterally: Many new roles are growing from within existing departments.

Speak up: If your work is vulnerable, communicate early. Don’t wait for your name to disappear from the org chart.

Final Word: This Is Rapid Reshaping, Not a Job Apocalypse

AI is real, and it’s already impacting thousands of jobs. But that doesn’t mean your career is over. But it DOES means your career is changing. If you lean in, learn the tools, and evolve with the transformation, you won’t just survive...you’ll thrive. The future belongs to those who adapt.

AI and Thought Leadership

Posted by Marcus Bourke on Saturday, August 9. 2025

Well, we covered a somewhat controversial topic last week. We evaluated whether AI is getting dumber...or are our expectations just too high? Whether you believe it's getting dumber or not, there's no denying that AI is incredibly capable. You may even be wondering if you can still be a thought leader and expert in the age of AI. Let's tackle this sticky subject today.

Can You Still Be a Thought Leader If ChatGPT Writes Better Than You?

Let’s be real. If you’ve ever used ChatGPT, you’ve probably had this moment:

You’re drafting a LinkedIn post, keynote script, blog article, or even a client proposal that you're pretty proud of. Then, out of curiosity, you paste the same prompt into ChatGPT, or another LLM, just to “see what it would write.”

And boom.

It’s cleaner. It’s punchier. It’s... better.

Suddenly, you’re questioning your entire existence and your future. If an AI can say it better than you, does your voice still matter?

I dedicate this article to every aspiring AI consultant, thought leader, strategist, coach, and creative who has ever felt a twinge of dread after realizing that a non-human language model just one-upped their writing game.

Part 1: The Ego Hit — “But I Thought I Was the Thought Leader?”

We live in an era where “thought leadership” is currency. Whether you're a consultant, coach, speaker, or founder, your authority in the marketplace often hinges on how clearly and confidently you can express your point of view. It's part of your personal brand and, when done well, can lead to a lucrative career.

And here comes ChatGPT, dropping elegantly structured, jargon-free, high-conviction thought pieces in 10 seconds flat.

Don't feel bad if this triggers an identity crisis. You may even be asking questions like:

“Am I actually smart, or have I just been good at writing?”

“If anyone can produce a great blog post using ChatGPT, what makes me special?”

“Do I have to compete with AI now or do I need to find a new career?”

Here’s the hard truth:

Yes, AI can often write better than you.

And no, that doesn’t mean you’re obsolete.

But it does mean the game has changed and you'll have to learn the new rules.

Part 2: Authorship vs. Authority — What Actually Makes You a Thought Leader?

Being a thought leader has never been just about being the best writer.
It’s about being a trusted source of insight, perspective, and synthesis in your field.

AI can help you write, but it doesn’t have real-life experience. Remember:

It doesn’t attend messy boardroom meetings.

It doesn’t talk down a panicked client at midnight.

It doesn’t feel market shifts in its bones the way a seasoned consultant or entrepreneur does.

You’re not paid just to write or speak words. You’re paid to create meaning and connection with those words.

And meaning comes from:

Contextual judgment

Strategic pattern recognition

Industry intuition

Empathy and timing

Skin in the game

AI can simulate expertise, but it doesn’t own ideas. You do. You see the idea through from concept to completion. That's valuable!

Part 3: What AI Can Do for You...and Where to Draw the Line

Here’s where it gets fun. As an AI consultant, you’re not here to compete with ChatGPT. You’re here to collaborate with it...intelligently. The best thought leaders of this next era will know how to use LLMs to accelerate their thinking, not replace it.

Use AI to:

Draft rough outlines or brainstorm angles for your content

Clarify fuzzy thoughts into clean frameworks

Rewrite dense copy into plain English

Test tone variations (e.g., formal vs. snarky vs. TED-style)

Generate “first passes” at articles, speeches, or decks

Explore opposing arguments you might have missed

Don’t use AI to:

Pretend it’s your original thought leadership

Outsource every blog or opinion post without editing

Avoid doing the real thinking yourself

Write in a voice that doesn’t match your own

Publish content you wouldn’t confidently defend in a room full of your peers

AI is your co-pilot, not your pilot. Not even your personal ghostwriter. It should amplify your intelligence, not impersonate it.

Part 4: What Makes You You (That AI Can’t Replicate)

This is the part most AI-curious consultants miss. While ChatGPT, or any similar LLM, can write clearly and convincingly, it doesn’t know you. It doesn’t have:

Your career scars

Your weird analogies

Your spicy or quirky takes on ideas or problems

Your actual client stories and related experiece

Your mix of humor, sarcasm, and cultural references

Your deeply felt values and personal quirks

That’s your secret sauce. Being human with human experiences.

In a world where everyone is publishing AI-enhanced content, the real differentiator is not perfect grammar or flawless formatting. It’s authenticity, specificity, and vulnerability.

The most successful consultants will learn how to pair AI’s speed and structure with their own professional experience. That’s where true thought leadership lives in the brave new world of AI.

Part 5: How to Stay Credible in a Flood of AI-Generated Noise

Let’s face it, the content space is getting noisier by the day. LinkedIn, Medium, YouTube, TikTok and every other platform is now flooded with polished, ChatGPT-polished takes. So how can you cut through the noise and get noticed?

1. Don’t hide the fact that you use AI...Show how you think with it

Write posts like:

“I asked ChatGPT how to solve this leadership issue. Here’s what it said and here’s what I actually did.”

“AI gave me 10 strategies to grow this brand. I tried 3 of them. Here’s what worked and what didn't.”

“I prompted ChatGPT to explain this framework I’ve been refining for years. Here’s how I'll improve it based on the response.”

2. Share first-hand stories, not just opinions

Everyone can share opinions now. Few can share real outcomes and that's what businesses are craving.

If you helped a client navigate AI integration and saved them $250k in operating costs, then talk about that. That will resonate with other potential clients.

If your strategy failed and you learned something from the failure, then don't be afraid to talk about that either. People like to do business with others who own up to their mistakes and learn from them.

3. Have a real point of view

Neutral content is dead. It's too sanitary. Too boring. People want to follow consultants with a perspective, even if it's a bit edgy or polarizing. For example:

Don’t say: “AI will transform consulting.” That’s obvious.

Instead, say: how, when, and why others are getting it wrong.

The sharper your view, the more magnetic your voice becomes. Good or bad, people will recognize and remember you.

Part 6: A Word to the AI-Curious Consultant — The New Skills That Matter Most

If you’re building a consulting business around AI, you’re not just here to understand the tools. You’re here to model the mindset that clients need to survive and thrive in this new world. That includes:

Prompt literacy: Knowing how to design great prompts for high-leverage output

Critical thinking: Spotting AI’s blind spots and factual hallucinations

Original synthesis: Merging AI insights with real-world strategy

Meta-communication: Teaching clients how to use AI without outsourcing their brain

Personal branding: Writing and speaking with a tone only you could deliver

Final Thoughts: Your Voice Still Matters...Even If AI Is Louder

If we get down to brass tacks:

Yes, ChatGPT may technically write better than you.

But it will never be you...or any other human.

Your messy, human, hilarious, experience-packed voice is what cuts through the algorithmic sameness. Your job as a thought leader is not to out-write the machine. It’s to out-human it. In other words, play to your strengths. That means showing up with:

Original angles

Experience-based insight

Supportive Enthusiasm

Strategic Perspective

A willingness to say what others are afraid to

So keep writing. Keep speaking. Keep thinking out loud in public.

Use AI to elevate your ideas..not erase your identity.

Because in the end, thought leadership isn’t about sounding perfect.
It’s about being brave enough to have a point of view and human enough to own it.

Want to Become the AI Thought Leader Others Quote?

If you’re an emerging AI consultant looking to build authority, attract better clients, and stay relevant in a world of infinite content, here’s your next move:

Develop your voice. Get weird. Get honest. Get specific.

Use ChatGPT as your thought partner, not your substitute.

Don’t just write about thought leadership. Live it.

Is AI Getting Dumber?

Posted by Marcus Bourke on Saturday, August 2. 2025

Hopefully, you're not in AI despair after reading last week's post. It's easy to focus on the negatives and start playing out doomsday scenarios in your head. There's always hope and I personally see a bright future where we coexist with AI. To help balance things out, let's shine the spotlight on AI this week. Do you think AI is continually getting smarter? I'd say overall, yes. However, there are some power users and even some researchers who would disagree. Let's break it down now.

LLMs Are Getting Dumber? The Shocking Truth About AI Model Degradation

Is your once-sharp AI assistant suddenly dull? You’re not imagining it. Across Reddit, X, and Slack, I’m seeing a common theme of complaints: “ChatGPT’s creativity is flat.” “Claude used to be smarter.” “Gemini keeps echoing generic answers.”

So what’s really happening? Let's dig into it today and see if we can separate fact from fiction:

1. What People Actually Mean by “Dumb”

When users say LLMs are getting dumber, they’re usually pointing to:

Worsening output quality – more bugs, less flair in code, more repetition

Generic or evasive replies – overly safe, even when not warranted

Memory and context fading – earlier prompt context slipping out of conversation

Over-apologizing or hedging – more “I’m sorry” and less substance

These aren’t isolated gripes. Researchers at Stanford and UC Berkeley recently documented noticeable declines in GPT-4’s math and coding competence over time. So, is it really getting "dumber?"

2. Fine‑Tuning: When “Alignment” Backfires

Models are fine-tuned using reinforcement earning from human feedback, or RLHF, which sounds great on paper. But here’s the catch:

Too much emphasis on avoiding mistakes, staying neutral, or being safe

Leads to over-alignment and the model loses its edge, its creativity

Imagine training a guard dog not to bark, even when it really should. Not very useful, is it?

3. The Synthetic Data Trap

AI is increasingly trained on its own output. We covered this in-depth a few weeks ago, so here's a refresher:

Fresh natural data is expensive and time-consuming

Instead, companies spin up models, collect their outputs, and retrain

This creates a reinforcement loop where errors get baked into more errors

Think of it like the classic “photocopy of a photocopy” where details fade, distortions get magnified.

4. Model Compression: You pay for the Premium Model, They Serve You Economy

Running massive models (500B+ parameters) is very expensive. So what happens?

Free tiers and default settings often run smaller, quantized versions

You're getting stripped-down intelligence without knowing it

“Great performance” becomes a paywall feature

“They baited us with luxury, and now we’re stuck with economy class.”

5. Expectation vs. Reality

Does the problem lie with us? We’re more spoiled than we know.

GPT-3.5 impressed us. GPT-4 blew us away. Now what? Perhaps we're bored

Users expect one model to be lawyer, poet, coder, project manager, all at once

So when it bails on creativity or dodges nuance, we're quick to call it “dumb”

Sometimes, technology just can't keep pace with our expectations.

6. Four Possible Ways to Help Address the Concerns

Want sharper, more powerful AI? Here’s what needs to happen:

A. ???? Version Transparency

Give users insight into model versions, by providing a release schedule with a summary of what's coming in each release (some LLMs are better at this than others). For example:

LLM ver. 5.0: September 2025 - Here's what you can expect...

LLM ver. 5.1: December 2025 - Here's what you can expect...

LLM ver. 5.2: March 2026 - Here's what you can expect...

No more unplanned updates that surprise users.

B. ???? Specialization Over Generalization

Ditch “one‑size‑fits‑all” LLMs:

Use vertically focused agents: coding bot, creative writer, legal researcher

Avoid constant re-tuning of a jack-of-all-trades

C. ???? Real-World Natural Data > Model Echoes of Synthetic Data

Prioritize human-generated content:

Books, expert forums, licensed articles—real voices with context

Avoid synthetic data fatigue and preserve nuance

D. ⚙️ User‑Tunable Settings

Put control back in users’ hands:

Creativity vs accuracy sliders

Toggle safety filters

Profiles optimized for specific tasks (e.g. “code‑first” vs “HR‑safe”)

Let people shape the model to what they need...not what someone else decided was “safe.”

7. Why Does This Matter Now?

This isn’t just an academic debate. No, it’s a turning point in AI adoption:

New LLM startups could ride quality-first waves over bloated incumbents

Open-source models may build trust by providing solid documentation while staying transparent and flexible

AI consulting emerges: “model optimization specialists” will be the next hot skill

Users who recognize model degradation are the ones shaping future AI.

8. What You Can Do Today

Ask Yourself: “Which model version am I using?”

Compare releases side by side to pick the best option for you.

Explore alternative agents that focus on single tasks that best address your needs.

Demand settings: sliders, toggles, profiles to chip away at the black box approach of current models.

Stay curious: model drifts will happen. Awareness is your ally.

Final Thoughts

It’s easy to blame the model. But degradation is rarely accidental. Rather, it’s baked into business decisions, training shortcuts, and user complacency. So if your AI model seems sluggish or stale, don’t be to quick to call it “dumb.” Instead, do your homework and decide if that model is still the best solution for your needs. Everything evolves in life, so maybe it's time for a change?

AI Despair

Posted by Marcus Bourke on Saturday, July 26. 2025

If you read last week's post, then what do you think about the use of synthetic data in future AI models. There are certainly reasons to use synthetic data, but it can also be a cause for concern. Does it make you feel like the word "artificial" in artificial intelligence is beginning to outweigh the word "intelligence"? Only time will tell.

This leads us to today's topic. AI is evolving so rapidly that it's hard to keep up with it. Everyday we learn about something new that it can do that tells us we're living in the future...today. Whether it be creating realistic videos or images, reading X-rays better than a doctor or automating entire workflows with AI agents, it can do it better than we can. This can lead to what I like to call "AI Despair", or that sense of dread that we'll all be displaced in our jobs sooner rather than later.

It's important to talk about, so let's spend some time on this topic today.

From Despair to Empowerment: How to Financially Thrive in the Age of AI

We are living through one of the most transformative technological moments in human history. Artificial Intelligence, once the stuff of sci-fi movies, is now writing, coding, designing, analyzing, forecasting, diagnosing, and even coaching. Understandably, many knowledge workers and professionals are watching this unfold with anxiety, asking:

“If AI can do what I do, but faster, cheaper, maybe even better, then what’s left for me?”

In the past year, I've discussed the impact of AI with dozens of high achievers, smart thinkers, and people who’ve built impressive careers for themselves. There was an underlying tone of despair in many of those conversations. Many felt that they were becoming obsolete. Dispensable. Unemployable if something happened to their job. They think to themselves what they’re afraid to say out loud:

“Am I…done? Washed up?”

I think some basic psychology, business insight, and an understanding of AI can help us come to a more positive conclusion: You are not done. In fact, you may be on the cusp of the most financially and personally rewarding chapter of your life...if you shift your mindset and take action. Here’s how:

Step 1: Understand the Psychology of AI Despair

Let’s start by naming the emotional undercurrent here. Some would say fear, but I think it's grief.

AI-induced despair is driven by grief over the loss of certainty, identity, and control. Your job, your skills, your role and your value has been the foundation of your life. When AI threatens that, it can feel like a death of the self and to your current way of life.

This grief is real and valid. But it’s not the end. It’s a signal.
It means you're being asked to evolve, just like past generations did when machines took over factories, cars replaced horses, or computers replaced filing cabinets. Factory workers had to learn how to use machines, gas stations and repair shops had to be created for cars, and people learned data processing and databases in place of filing skills. They evolved and you can too.

If you feel overwhelmed, the worst thing to do is retreat into your shell. Emotionally, intellectually, financially, it creates a loop of helplessness that feeds the despair. Instead, recognize that your feelings are not proof of doom, but signs that it’s time for to recreate yourself.

Step 2: AI Isn’t Replacing You. It’s Repositioning You

Let’s reframe your narrative...

AI doesn’t want your job. It's ready to help with your tasks. It’s not gunning for your identity. It’s waiting to help automate your routine. That distinction is key.

Yes, AI will automate portions of your job. This will be most obvious in jobs that are repetitive, predictable, and formulaic. But that leaves behind what AI can’t do:

Relationship building

Strategic judgment

Emotional nuance

Ethical decision-making

Creative synthesis

Vision, leadership, and contextual awareness

These are not just miscellaneous skills for your job. These are the core of human value.

In truth, AI clears the clutter. It’s your intern, your researcher, your junior analyst, your ghostwriter. That gives you the freedom to move up the value chain. You can now focus on consultation, innovation, leadership, and entrepreneurship.

Step 3: Embrace Your Role as a Human-AI Integrator

One of the highest-demand roles in the coming years will be an "AI integrator." This is someone who doesn’t just use AI but knows how to apply it to real-world problems and how to integrate it into existing processes. That can be you.

Whether you’re a marketer, teacher, attorney, accountant, therapist, project manager, or entrepreneur, your new mission is to become the person who can:

Understand your industry’s needs

Know what AI tools are available

Apply those tools thoughtfully and ethically

Help others adapt

This is not about coding or technology. It’s about context. Business. Leadership. Communication. The very things you’ve been cultivating for years.

If you learn to use AI fluently, you will not be replaced. You’ll be indispensable.

Step 4: Develop Your Unique Value Proposition, then Monetize It with AI

Here’s where we shift from mindset to money.

To financially thrive in the age of AI, you need to align three elements:

Your Unique Human Edge – What do people come to you for? (Trust, insight, perspective, style, guidance?)

AI Tools That Supercharge You – Which AI tools can help you do it faster, cheaper, or at scale?

A Market That Pays – Who needs your value, and how can you reach them?

Let me give you some examples:

The Copywriter
Instead of fearing ChatGPT, she starts using it to create first drafts, freeing her time to focus on strategy, brand voice, and client consulting. She now charges more as a fractional content strategist instead of being stuck in “$50 per blog post” gigs.

The Educator
He trains himself on AI tutoring tools and starts offering AI-assisted learning design for home-schoolers and micro-schools. Parents pay a premium for personalized education with a human heart and AI precision.

The Accountant
She builds an AI-augmented financial coaching program for solopreneurs. Instead of just filing taxes, she’s now a strategic partner and, of course, bills accordingly.

Step 5: Build an AI-Leveraged Side Income

If you’re employed and worried about job security, the best medicine is action. Use AI to build a small, diversified stream of income. Ideas include:

AI-generated digital products (eBooks, templates, Notion boards)

Coaching or consulting powered by AI (e.g., “How to use AI in your HR practice”)

Micro-agency models where you use AI to deliver freelance services at scale

AI course creation in your niche (AI for yoga instructors? For real estate agents?)

Here’s the magic: AI reduces startup time, lowers risk, and helps you move fast. You can build a product or business in a weekend that used to take months.

Not all of these will make you rich, but they’ll remind you that you are not powerless. From that feeling of agency, everything changes.

Step 6: Shift from Consumer to Creator

AI thrives on content, data, and prompts. Most people use it to consume content. They ask for answers, templates, summaries. But the real value lies in creating with it.

When you create products, services, businesses, or communities, you move into the driver’s seat. Here’s what that could look like:

Use AI to co-write your book or lead magnet.

Use AI to prototype a business idea or SaaS concept.

Use AI to summarize academic papers and turn them into blog posts.

Use AI to create niche communities around AI use cases in your profession.

AI rewards creators. The earlier you start creating, even messy, small, scrappy things, the more momentum you’ll build. What you create will quickly evolve into polished, professional products.

Step 7: Redefine Success in Human Terms

Finally, a reminder: You are not a robot. Your worth isn’t defined by productivity or output. In this AI-driven age, success will be increasingly defined by who you are, not just what you do:

Trust will be more valuable than technical skill.

Integrity will separate leaders from opportunists.

Empathy will be the killer app.

While AI can scale logic, it can’t scale meaning. That’s your specialty. So ask yourself: What do I want to be known for in this new world? That answer, when supercharged with AI, will lead you to a financially and personally fulfilling future.

Final Words: You Are Not Being Replaced. You Are Being Called Forward

AI is not the end of your relevance. It’s the end of your routine.

The people who will thrive in this next era aren’t necessarily the most tech-savvy...they’re the most emotionally agile. They’re the ones who can stay curious, keep learning, and lead others through the fog with compassion and empathy.

If you're feeling despair, don’t try to escape it. Use it. Let it wake you up. Let it push you to level up. Let it drive you to reinvent yourself. Because AI may change the world, but it can’t replace the human spirit. And you, my friend, are still very much needed.

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Synthetic Data in Large Language Models

Posted by Marcus Bourke on Saturday, July 19. 2025

Did you enjoy the post on vibe coding last week? It's pretty empowering to know that a non-technical person can now develop applications using AI. It opens the door for lots of opportunities to start a tech company. So, what's on the agenda this week? How about synthetic data? More specifically, the use of synthetic data in training newer generations of Large Language Models (LLMs). Sound interesting? Let's dig into it now.

Synthetic Data vs. Natural Data in Training Future LLMs: What We Need to Know

As we progress farther into the age of artificial intelligence, the fuel powering the engine of LLMs is evolving. We've talked many times before that this fuel is data, but we're not interested in just any data. Increasingly, synthetic data is being used alongside or even in place of natural (real-world) data in the training of future LLMs.

For AI consultants and aspiring entrepreneurs, understanding the differences between synthetic and natural data, and the impact on model performance, safety, and alignment, is more than just academic. It’s absolutely crucial. To help you get up to speed, let's explore the following today:

The definitions and differences between synthetic and natural data.

The risks and benefits of training LLMs on synthetic data.

Implications for model selection in business environments.

How you, as an AI consultant, can guide your clients in choosing LLMs based on their data lineage.

What's the Difference Between Natural vs. Synthetic Data?

Natural Data

Natural data refers to information generated by real human activity. It includes text from books, websites, social media, legal documents, academic papers, and conversations. It is noisy, diverse, and often messy—but it reflects authentic human language, intent, and complexity. It reflects reality because it's 100% real.

Synthetic Data

Synthetic data, on the other hand, is artificially generated by machines, such as LLMs. This often means:

Text generated by existing language models.

Simulated conversations or tasks created through scripted prompts.

Augmented or manipulated natural data (e.g., paraphrased, translated, or summarized).

Synthetic data can be created at scale and customized for specific applications. It allows AI developers to sidestep licensing restrictions or privacy issues tied to real-world data.

Why Are LLMs Using Synthetic Data?

There are several reasons why synthetic data is now being incorporated into new LLMs:

Exhaustion of High-Quality Natural Data: Most publicly available high-quality datasets have already been scraped and used. There’s diminishing marginal return in collecting more.

Legal and Ethical Barriers: Real-world data often comes with copyright issues, data privacy concerns, and ethical challenges.

Controlled Distribtion: Synthetic data can be crafted to over-represent underrepresented languages, dialects, or content types in an attempt to balance bias present in natural data.

Cost and Speed: Synthetic data can be generated quickly, allowing for rapid experimentation and iterative training of models.

Alignment and Safety: Developers can tune synthetic data to reinforce alignment goals to ensure the LLM responds in ways that are safer, more polite, or more informative.

Benefits of Training with Synthetic Data

1. Scale Without Limits
Synthetic data can be generated endlessly. That’s a massive advantage in a world where trillion-parameter models require equally vast training data.

2. Customization
Developers can generate domain-specific data for medical, legal, or technical use cases, helping to create LLMs that are more specialized.

3. Bias Correction
By rebalancing synthetic datasets, developers can mitigate historical or cultural biases present in natural data sources.

4. Privacy and Safety
Synthetic data avoids the inclusion of sensitive or personally identifiable information (PII), making models safer for public and enterprise use.

5. Simulated Edge Cases
Models can be trained on rare or hypothetical situations that might not appear often in natural datasets (e.g., emergency scenarios, rare diseases).

Risks of Relying on Synthetic Data

1. Model Collapse and Feedback Loops
Training models on data generated by previous models can lead to a "model collapse" as quality degrades over generations of models. It’s like making a photocopy of a photocopy, where detail and richness are lost in each copy.

2. Loss of Human Nuance
Synthetic text often lacks the subtlety, humor, ambiguity, and error that make human communication feel authentic and natural. Models trained on overly synthetic data may become sterile or disconnected from how people actually talk.

3. Over-Optimization
When models are trained on data that is “too perfect,” they may fail to generalize well in real-world applications, especially when facing unexpected inputs or natural language variation.

4. Reinforced Errors
If synthetic data is generated from flawed models or biased prompts, those issues are amplified in the next generation. It creates a kind of "generational drift" that can harm safety and performance.

5. Opacity and Trust
It becomes harder for users and businesses to trust outputs when they don’t understand what kinds of data the model was trained on. Transparency becomes a challenge.

Navigating LLM Selection: Guidance for AI Consultants

With synthetic data becoming a core ingredient in many state-of-the-art LLMs, AI consultants must know how to ask the right questions and evaluate models effectively for their clients. Here are some suggestions to help:

1. Understand the Data Lineage
Ask vendors or model providers:

What proportion of the training data is synthetic vs. natural?

Was synthetic data generated by a base model or through human-curated prompts?

Example: A healthcare startup should prefer a model trained on rigorously curated and verified clinical data, natural or synthetic, rather than generic internet-based data.

2. Balance Generalization vs. Specialization
Synthetic data allows models to specialize quickly, but general models may still be better for open-ended tasks. Guide clients toward:

General LLMs (like GPT-4 or Claude) for wide-ranging content generation or summarization.

Synthetic-trained niche models (e.g., finance, law) for narrowly focused scenarios.

3. Prioritize Transparency and Auditability
If a vendor can’t clearly explain how their model handles data quality, ethics, and safety in the synthetic data pipeline, treat it as a red flag. Encourage your clients to work with providers committed to auditable AI.

4. Consider Cost vs. Accuracy Tradeoffs
Some synthetic-heavy models may be cheaper due to the reduced data acquisition costs. However, if quality is essential, such as in legal or policy drafting, clients may be better off paying more for models with a larger natural dataset base.

5. Pilot Across Multiple Models
Help clients set up lightweight pilot tests with multiple LLMs to evaluate the following:

Accuracy and relevancy of responses

Sensitivity to ambiguity or edge cases

Tone and communication style

Ability to follow instructions and constraints

Often, the difference between a synthetic-heavy model and a natural-data-heavy model becomes clear through side-by-side comparison.

A Sample LLM Evaluation Matrix for Clients

Criteria	Synthetic-Heavy Model	Natural-Data-Heavy Model
Customization	High (easy to fine-tune)	Medium
General Language Fluency	Medium to High	High
Human Nuance	Lower	Higher
Bias Management	High control	Lower control
Long-Term Degradation Risk	Higher	Lower
Licensing & Compliance	Fewer issues	Potential concerns
Transparency of Data Sources	Often opaque	Varies
Best Use Case	Domain-specific bots	General-purpose assistants

Helping Clients Future-Proof Their AI Strategy

As a new AI consultant, your job is not just to help clients implement AI. It’s also to help them implement the right AI for their business needs.

Synthetic data is here to stay, and its role in training future LLMs will likely grow. But so will the risks of over-dependence. Helping your clients navigate this shifting landscape involves:

Staying informed about the evolution of LLM architectures.

Developing vendor relationships with transparent and responsible providers.

Building small-scale test environments to evaluate model performance in context.

Offering ongoing monitoring and feedback loops to adjust as models evolve.

Final Thoughts

Synthetic data opens the door to powerful new AI capabilities, but also demands a more intentional, cautious approach to LLM selection and deployment. As a consultant or entrepreneur in the AI space, your ability to understand and explain these trade-offs will make you a trusted advisor in an increasingly complex AI field. By focusing on transparency, context-driven evaluation, and risk-aware strategies, you'll help your clients pick the right model for their needs, whether fueled by human language, machine imagination, or (most likely) both.

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