October 26, 2025 • 67 mins
What if the systems designed to connect us are quietly deciding who gets seen, heard, and helped—and who gets stalled, flagged, and erased? We pull back the curtain on digital injustice, tracing how biased design and poisoned data can evolve into coordinated suppression that touches everyday life: uploads that freeze at key moments, purchases that mysteriously cancel, reputations reshaped by loops of partial truths, and surveillance that follows from the screen to the sidewalk.
I walk through real patterns that don’t look like accidents once you line them up—algorithmic mirrors that warp reality, environmental triggers that ping watchers in real time, and platform frictions that conveniently appear when truth is set to travel. We connect the dots with research on facial recognition errors, data poisoning risks, and the business cost of AI bias, then push further into the human cost: lost opportunities, spiritual spaces infiltrated by analytics masquerading as insight, and communities nudged to police one another.
This is not a call to fear technology; it’s a call to lead it. I share the practices that keep agency intact—documenting patterns, verifying offline first, building redundant channels, and pausing before we let a feed define our instincts. We talk policy too: transparent audits of training data, oversight of surveillance tech, and real accountability when digital harm causes real damage. And because clarity is both practical and spiritual, we end by reclaiming voice, presence, and purpose with a spoken decree that refuses erasure.
If this resonates, subscribe, share with someone who needs language for what they’re feeling, and leave a review with the biggest pattern you’ve noticed. Your story might be the proof someone else needs to see through the fog.
Ever felt like your darkest fears of being watched were actually true? Well, this powerful documentation pulls back the curtain on life as a targeted individual—where privacy becomes fiction and safety an illusion. This series stands as both documentation and resistance—a refusal to be silenced despite overwhelming odds.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
SPEAKER_00 (00:02):
Hello, welcome to the Diary of a Same Black Woman.
I am your host, Marie.
This is where I speak from theintersections of faith,
technology, surveillance, andsurvival.
This docuseries is where I namewhat's been done in the dark and
reclaim what's been stolen insilence.
(00:22):
Look, I'm not here to proveanything.
I'm here simply to testify/slashdocument my experiences as a
targeted individual.
Because what I've experiencedcan't be explained away by
glitches or paranoia.
It's deeper than that.
(00:43):
It's layered and it's real.
As the former CIO of the WhiteHouse, Teresa Payton said during
an interview on scammerspayback.
It's only paranoia if it's nottrue.
So on today's episode, I will goin and discuss digital
(01:07):
injustice, but not just the kindthat lives in the headlines or
research papers.
I'm talking about the kind thatshows up in your uploads, your
inbox, your neighborhood, oryour spirit.
And I'm not making absoluteclaims here.
I'm naming patterns.
(01:28):
I'm basically sharing what I'veobserved, what I've endured, and
what I continue to resist.
Because when you're targeted,the attack doesn't stay in one
lane.
It spreads.
It spreads into your personallife, your professional space,
your spiritual walk, and itdoesn't always come through the
(01:52):
front door.
Sometimes it's spyware,sometimes it's a neighbor on a
balcony, sometimes it's a botthat follows you just to watch,
not support.
So let's look at the algorithmicmirror.
They say that algorithms reflectus, right?
(02:12):
But what if the reflection iswarped?
What if the mirror is beingmanipulated by unseen hands,
malicious networks, or scriptsrunning in the background?
Here's what I've seen podcastepisodes that stall, the
inability to upload or getflagged before I even publish
(02:35):
something, depending on theplatform, right?
Thankfully, I've managed tochallenge these and have worked
around these obstructions,although the bad actors and
malicious codes continue toattempt to obstruct the progress
of the destined state.
Now, metrics don't match thefeedback I've received.
(02:55):
Followers who feel like plants,watching, not engaging, but
maybe even reporting, right?
And so a feed that's full ofcreators who either seem to have
read or heard content, but youknow, it's not reflected in the
analytics or stats, but thosewho seem to have been
(03:18):
commissioned also to troll,perpetrate false claims,
narratives, and make snarkcomments relating to recent
posts or just overall characterassassination and or whatnot,
right?
And so you also have peoplephysically lurking, whether it's
(03:38):
on balconies, parking lots,listening in, not to support,
but to intercept, to be nosy.
And I can say right now, I hearan echo as I'm recording this,
and I don't know if it's justthe structure of where I am that
has an echo, or if it'ssomething, a device that's been
(04:00):
planted and that's recording methat's causing the echo.
Now, also, I for the past sinceI've started this podcast, I've
been, you know, wondering ifthere's possible content that
leaks before I even share itpublicly, right?
Because I am constantly beingmonitored, my networks have been
(04:21):
infiltrated, my devices havebeen infiltrated, and people are
constantly trying to record,monitor me through various
means.
There's also been patterns oftechnological interference that
align too precisely with momentsof breakthrough or truth
telling, right?
(04:42):
Even if I'm using or trying toupload something, if I'm using
AI and I'm doing research on aparticular topic, you know,
whether it's to troubleshoot orto prepare for my podcast as I
do research on, you know, what'sbeen happening, right?
Not only to me, but potentiallyto others, and if there is a
(05:04):
pattern of this occurrence.
You also have loops of data ormoments, right?
Whether it's a snapshot in time,doxxed info, private private
data leaks, uh recycled um topaint a false picture or just
basically to amplify what'snegative, right?
And so they don't want to lookat the entire person or
(05:28):
individual, they magnify um amoment in time, something that
has happened, and try tobasically paint that as the
entire individual.
And so even without technology,even without tech, right, in
hand, I've noticed that I'vebeen monitored.
And whether I've walked intohotel lobbies and watched uh
(05:52):
cameras grab my face.
Seconds later, the front deskphone rings or a text goes out
to the host.
And I've seen it happen instores or on sidewalks in places
where I should be, notnecessarily anonymous, where
basically I should feel free.
I know that there's cameraseverywhere, but I I believe most
people, when they go out, theydon't get like a trigger, like
(06:16):
something is not triggered or anemail is not sent, a text
message is not sent when theyappear somewhere.
But that happens to me.
Like if I walk out my house tothrow out the garbage, you know,
there's a trigger, something istriggered electronically,
someone receives a message, andyou know, people react to that.
(06:38):
And so because someone orsomething is watching, right?
Digitally, there's been scriptsthat's been written to basically
trigger these things.
So it's a combination of notjust the digital, but also the
physical.
And so um it's not justalgorithmically, but also
(06:59):
environmentally, right?
So because these people who theyhave been programmed, they have
been programmed to watch me,they have been brainwashed to
watch me, and so they are nowpart of that network, right?
Not only their phones and theirdevices are part of part of this
bot network, but they have alsosubscribed to the network
(07:22):
because now they're devoted andthey're committed to monitor me,
to spread information, um, to,you know, dilute or you know,
the truth, and basically to toharm me um in every aspect.
And so this is what happens whentechnology is used not to serve,
(07:44):
but to surveil.
And um, you know, it's not usedto connect, but to control.
And so what are the real worldrepercussions when you have all
of this going on, right?
When the algorithm is poison, itdoesn't just affect what people
see, it actually affects whatyou can do, right?
(08:07):
And in my situation, I'veexperienced this firsthand,
whether it's online purchasesthat mysteriously fail or get
flagged, right?
For example, Best Buy.
You know how many times I'vetried to purchase certain things
on Best Buy where the the youknow the purchase would go
through and then like minuteslater it would be canceled,
(08:31):
right?
And I have a subscription withthem.
And regardless of that fact,I've actually had to go into the
store to purchase uh like a GeekSquad membership, and even after
physically walking in with mycredit card and making that
purchase, and they know that Ihave a subscription.
(08:53):
If I try to make a purchasetoday, it gets flagged.
And so there's a lot of thingsthat's basically embedded and uh
maliciously scripted basicallyto prevent me from doing certain
things, especially as it relatesto tech support or tech devices
and things of that nature.
(09:15):
You know, you have instanceswhere a c accounts will lock or
have glitches while uh rightafter I speak, you know, the
truth or right after I use akeyword, you know, that might
hint that I'm either whetherit's I'm praying or I am trying
to research, you know, thingsthat might help me digitally,
(09:39):
right?
Things that might help mesuppress the digital survey
surveillance, things that mighthelp me to remove malware,
things of that nature, you know,videos that have vanished and
things of that nature because ofcorruption on my devices, you
know, so tampering withevidence, things that have sites
(10:02):
that have malfunctioned, right?
I've had sites, especially frommy ISP, where certain tabs, like
I wasn't able, like for weeks, Ihaven't been able to access the
internet tab, right?
Why can't I access it?
So I had to actually try to finda workaround to access the
internet tab or certain buttons,right, that don't function, like
(10:26):
you can't actually click on it.
And you know, I've experiencedthat on multiple websites.
So these are all things thathave happened because of uh
digital corruption and thingsthat have been used to attack
me.
So first digitally, and thenobviously you have, I would say,
(10:48):
symptoms from the digitalattack, right?
And some of those symptomsinclude uh job opportunities
that disappear withoutexplanation, right?
Whether it's due to reputationaldamage, things that that they
have painted, things that theyhave received, you know, through
(11:08):
planted data.
And so you have reputationaldamage from half-truth loops and
manipulated metadata.
And the people who receive thedistorted info act on it, right?
And so this actually ends upblocking reporting or spreading
lies.
So that is what's occurring, um,ladies and gentlemen.
(11:32):
And so, even from a spiritualstandpoint, so I've mentioned
this before in previous episodeswhere spiritual spaces, whether
it's virtually or in person,have been become infiltrated,
whether it's YouTube, you know,spiritual space, a Zoom
spiritual space, an in-personspiritual space where these
(11:54):
networks, whether it's a digitalnetwork uh via devices or people
actually following you intothose spaces and where they're
basically trying to spread lies,spread misinformation, um,
spread private information aswell, right?
So it's a mixture.
And so oftentimes people justaccept everything that they see
(12:18):
as truth and they don't theydon't look at it from an
objective standpoint.
And so discernment is clouded,it becomes clouded by the
digital distortion.
When we're looking at thisdistortion, how can it actually
negatively impact you?
Right?
It's it's it's not just you knowdata that's being spread and
(12:43):
sent, but it's actually it it'sit's rooted in in a lot, it's a
lot deeper.
It's a lot, and so you know,these aren't just technical
flaws, they're livedconsequences, right?
And when they're aimed atsomeone like me, someone who
speaks my truth, someone whospeaks the truth, disrupts
(13:06):
silence, someone who disruptssilence and refuses to conform,
they become tools ofsuppression.
And let's look at some of somestats here.
So facial recognition systemsmisidentify darker skinned women
up to 34.7% of the time comparedto just 0.8% for lighter skinned
(13:32):
men.
And this is from MIT Media Laband Buolamwinny and Jabrew from
2018.
I'm just, this is just regularstats, right?
This is not even stats based onmalicious actors interfering.
(13:52):
This is just based on, you know,the the technology and and how
it was programmed, right?
Whether that was conscious orunconscious bias, you know, this
this is basically the end resultof it.
And additionally, AfricanAmerican and Asian faces are 10
(14:14):
to 100 times more likely to bemisidentified than white male
faces.
This is from NIST 2019, and thenpoisoning just 0.001% of
training data can cause AImodels to spread misinformation
(14:38):
or behave maliciously.
And you know, this is this ishappening more and more.
I recently watched uh a videothat uh Judge Faith, Judge
Faith, she posted a video abouthow misinformation about her and
(14:59):
her husband getting a divorcewas spreading, and you know, it
started with one blog, and thenother people took it and it was
just spreading like crazy, andthen it basically came, it
started to become like an actualsearch results as fact.
And so, you know, all it takesis just a small, you know,
(15:21):
misinformation or a small datapoint being spread um basically
to poison and affect, you know,someone's reputation,
livelihood, and so forth.
I'm happy in her in herscenario, she was actually able
not only to identify the peoplebehind the the malicious data
(15:46):
that was being spread, she wasactually able to get recourse.
And, you know, that is that isvery encouraging.
You know, however, I I mean noteveryone has the means that you
know she has basically to to tobasically get to the root cause
(16:07):
of of what was happening.
And in her case, you know,people were actually publicizing
information openly about her.
In my case, it's being done morecovertly, so I think it's it's
it's a little harder toidentify.
But the stats about the the0.001% data poisoning comes from
(16:28):
Cornell University, and it waspublished in 2020.
Now, predictive uh policingtools have wrongly flagged
individuals based on biaseddata, leading to false arrests
and reputational harm.
And this is from ProPublica andPred poll investigation.
(16:51):
And that's not just predictivepolicing tools, but also even
like with social media, youknow.
I know when I first launched mymy YouTube page and and
Instagram, it was flaggedimmediately because of, you
know, the corruption that washappening behind it.
And, you know, once they lookedat it immediately, it was
(17:14):
basically they saw that it was amistake, right?
And so that's how these thingscan basically trigger and affect
you, you know, negatively.
But this is not just one-sided,right?
It's not just about theprogramming, it's it's it's a
trifecta.
(17:35):
So it's bias in design, it'sbias in data, it's bias in
deployment, right?
Garbage in, garbage out.
And so when a malicious actorenters the equation, it's not
just bias anymore, it'sweaponized distortion.
Let me break it down for y'all.
(17:55):
Let me break it down for y'all.
AI, right, when we talk aboutbias in programming, AI reflects
its creators, right?
If the original training data ordesign choices are skewed,
whether consciously orunconsciously, the output will
mirror that bias.
(18:15):
For example, I spoke aboutfacial recognition earlier, you
know, in this segment.
And so facial recognition uhsystems trained mostly on
lighter skin faces misidentifieddarker skin individuals up to a
hundred times more often, right?
And then so language modelstrained on internet data absorb
(18:38):
toxic stereotypes, thenreproduce them as neutral
output.
And so that's those are twoexamples of bias programming.
Then you have malicious actorsamplifying the bias, right?
So now imagine someone with illintent, not just negligence,
(18:58):
injecting adversarial prompts,right?
They're they're injectingadversarial prompts, poison data
sets or surveillance triggers,that's bias, you know, and so
you have, and then now you havethese adversarial networks that
can sudden, you know, suddenlymanipulate AI, basically to
(19:22):
ignore certain voices, flagbenign content, or prioritize
harmful narratives.
And I've experienced this inevery aspect of my life, you
know, and so there's coordinatedsuppression, right?
So it's within the technology,but it's also now embedded in
the bot networks, but also theactual human networks that are
(19:45):
willfully, you know,participating.
So the coordinated suppressioncan look like the algorithmic
coincidence, but it it's youknow, it's actually engineered
silence, right?
Especially when targeting eitherprophetic or restorative voices.
So multiplication now happensthrough the infrastructure as a
(20:09):
result of that.
So once the bias is embedded,right there, either whether once
the bias is embedded, eitherthrough the the programming,
right, the initial programming,whether it was by bias or
intentional bias orunintentional bias, as well as
the malicious actors that arenow also injecting, you know,
(20:36):
poison data or malicious datainto the system.
Now this begins to spread acrossplatforms, right?
Social media, search, cloudtools, text messages, search
results, right?
Across the devices, phones,routers, power lines, across
(20:57):
perceptions, how others see you,how you see yourself.
So it it it it's viral, right?
It's viral, it's global.
Technology is basically there isno barrier to technology, right?
It's not, it's not, it's not inone location.
(21:18):
It's not confined to onelocation.
So it spreads and it spreadsrapidly, right?
Especially if it's gossip,especially if it's malicious
gossip, especially if it'ssalacious, it will spread.
This is why I believe it's soimportant for me to document
what's happening to me.
(21:39):
Documenting and diagnosing theobstruction that I am
experiencing real time, as wellas reclaiming my voice and
narrative.
You know, I believe it's vitalto do that.
And I'm not just resisting thebias, I'm disrupting it.
I'm disrupting the entirefeedback loop that multiplies it
(22:00):
as much as I can, right?
Because there's been a lot ofobstruction.
You know, there's a saying wherethere's movement, there's
friction.
There's been a lot of friction,there's been a lot of attempts
to obstruct the progress of thedestined state.
But, you know, I will continueto push through.
So let's look at biasmultiplied, right?
(22:22):
So when AI becomes the weapon,they say numbers don't lie,
right?
But what happens when thesystems counting those numbers
are trained to ignore you?
What happens when the algorithmisn't just flawed but
weaponized?
Let's talk about the stats alittle further, right?
We looked at some stats in theprior segment.
(22:44):
Let's let's look a littledeeper.
So let's look at then versusnow, right?
The stats they gave us versusthe truth emerging, right?
In 2020, they said that AI wasthe future.
Global IT spending hoveredaround$3.9 trillion.
(23:07):
Bias was framed as a glitch, abug to be patched.
Now, in 2025, global IT spendingis projected to hit$5.6
trillion, and AI is thecenterpiece.
(23:27):
The AI market alone is worth$500plus billion dollars, and that's
expected to triple by 2031.
Okay.
But here's the kicker (23:39):
36% of companies admit that AI bias has
already harmed their business,and 62% lost revenue, 61% lost
customers, and that's just whatthey're willing to admit, right?
(24:01):
I can't admit as anentrepreneur, as a small
business person, you know, as aprofessional, you know, I've
been impacted.
My business has been impacted.
You know, my mentorshipopportunities have been
impacted.
A lot of different things havebeen impacted.
Career opportunities have beenimpacted.
(24:22):
And so, yes, I can attest tothat.
Now, bias isn't a bug, right?
It's it's a blueprint, right?
Let's just call it what it is.
It's not a bug, it's not a it'snot a glitch, it's a blueprint,
right?
It's the programmer, if theprogrammer, right?
If the programmer was biased,the output will be biased as
(24:44):
well.
Now, let's add, you know, themalicious actor into the mix or
a network determined to destroyyou, and that malicious output
is multiplied several timesover, right?
And and we've seen that.
We see that in the experiencesthat I've been basically
documenting and sharing with youall.
(25:04):
And I'm sure, you know, thepeople that have uh lost
revenue, the 62% businesses thathave lost revenue or customers,
you know, they've seen that aswell.
And so those who are already, Ibelieve that those who are
already disenfranchised ormarginalized typically get the
(25:26):
brunt of it, unfortunately.
So imagine having pre-existingobstacles based on your color,
your ethnicity, your sex, yourreligious beliefs, and then that
negatively being amplified evenhigher or to the highest degree
by the way that technology wasprogrammed, and then also by
(25:50):
malicious actors.
The technology and the maliciousactors essentially set you up
for failure, whether that's whatwas intentionally or you know,
unintentionally, you know, bythe programmers, whether that
was done intentionally orunintentionally by the
programmers, and or how themalicious actors or networks
(26:16):
have exploited what they wereable to exploit, right?
And so let's call let's call itwhat it is.
Let's call it what it is.
It's engineered erasure, it'sengineered erasure.
So let's look at some more statshere.
(26:37):
GPT2 reduces black specificwords by 45.3% and female
specific words by 43.4% incontent filtering tests.
In hiring tools, resumes withblack male names are
systematically filtered out.
And in long documents, largelanguage models are trained to
(27:02):
ignore critical information,especially if it's buried,
nuanced, or inconvenient.
This isn't just about fairness,it's about who gets seen, who
gets silenced, and who getserased, right?
As I mentioned, it's engineerederasure.
You know, that's essentiallywhat it is.
(27:22):
So let's look even deeper.
Let's look at the feedback loopof suppression.
Bias in, bias out, right?
I mentioned it before, garbagein, garbage out.
But when you add maliciousintent, it becomes a feedback
loop of suppression.
So you have adversarial actorsthat can train AI to one,
(27:45):
ignore, to flag, to shadow ban,you know, whoever is basically
speaking out.
You have an infrastructure-levelmanipulation from power lines to
platforms, they amplify and umthe distortion, and then you
have the result ontologicalbias.
(28:06):
AI doesn't just reflect theworld, it reshapes what we're
allowed to imagine.
And, you know, a lot of people,they just whatever they get and
see, that's what they take astruth, and they don't even
second guess it.
And so prophetic disruption,reclaiming the narrative.
(28:26):
This is why, you know, I speak,this is why I document, this is
why I refuse to be edited out ofmy own story because you know,
everyone else is trying to tellmy story.
Everyone thinks they know me,everyone thinks they know me
better than I know myself,better than they, better than
(28:46):
those who are close to me knowme.
So it's it's it's prettyinteresting.
So, you know, every time, youknow, I think it's important,
you know, to speak out becauseevery time we name a pattern, we
break its power.
Every time we speak the truth,we rewire the algorithm, right?
Hopefully, every time we show upunfiltered, unbought,
(29:11):
unbothered, we reclaim the mic.
And so let's look at whereprejudice hides, right?
Again, garbage in, garbage outis the same.
But what if garbage was planted,right?
What if the algorithmic, youknow, feed or the algorithm was
(29:35):
fed poison data intentionally,right?
That's where you had themalicious actors.
That's why, you know, I'mconstantly seeing all these
obscene things in my feed, eventhough I'm not even touching um
this stuff or things that Ihaven't even looked up in like
ages, or things that I'm noteven involved in that I'm
(29:57):
seeing.
But, you know, this stuff isintentional.
Being planted.
And so someone who knew thatthey knew exactly what they were
doing.
It was intentional.
And so, like, I've seen how youknow one person tuned into a
malicious network can basicallytrigger a chain of reaction,
(30:17):
right?
And that's why now, like, I havea botnet of people and devices
that are essentially attackingme.
So they feed the system falseflags, right?
As they're receiving data,they're feeding it more data.
They're distorting the data,they're distorting the
narrative.
And so they're, you know, youhave, you now have distorted
(30:40):
metadata and you havemanipulated scripts.
I'm talking about tech scripts,such as, you know, whether it's
Python scripts or whateverscripts that they're using,
manipulated code and things ofthat nature.
You have the algorithm nowthat's absorbing this stuff and
it treats it as truth.
(31:00):
And people treat it as truth.
People make decisions based onthat.
Companies make decisions basedon that, you know, and this now
begins, you know, they begin torespond accordingly, right?
Platforms start to flag content,they slow uploads, things freeze
up, you know, decisions,important decisions are made
(31:24):
based on these, you know, thingsthat have been fed into the
system.
And so, you know, even likeonline purchases, right?
As I mentioned, they getdisrupted, you know, they'll get
canceled, you know, pages won'tload so many different things,
right?
Recommendations turn hostile, orrecommendations are obscene, you
(31:49):
know.
So people, you, you know, so nowyou have people who receive this
poison data.
As I mentioned, whether biasedor not, they begin to act on it,
right?
They begin to act on it, someconsciously, some unconsciously.
They take whatever they seefirsthand as truth, they make
(32:10):
decisions, life alteringdecisions, you know, on this,
right?
So, you know, this is nowblocking whether a system is
blocking you, whether a system,a platform is blocking you, or
it's reporting you, or it'signoring you, or it's not
loading, whether it's a DDoSattack, or just different things
(32:33):
that are happening, you know,behind the scenes um
technologically, or peoplereacting on it, you know, it
this is what's happening, oreven worse, right?
Some people are joining in,they're joining the bot network,
they're joining the gangstalkers.
And so now it's not just thealgorithm, it's a ripple effect.
(32:58):
It's a ripple effect.
And we, you know, I've spokentwo episodes ago, I've spoken
about the ripple effect and andhow that can, you know, the
ripple of silence and the rippleof harm.
Go back and watch it.
So once a malicious seed isplanted, it can grow into a
forest of obstruction.
And the worst part, the worstpart is that it's hard to prove
(33:23):
because the system is designedto look neutral, right?
And so it's designed to hide itshand.
And so, you know, people willsay it's a glitch, it's a
coincidence, or worse, that it'snot happening, or that you're
paranoid or delusional, or thatyou're even insane, you're not
in your right mind, you'reunstable, whatever, you know,
(33:44):
whatever it is that they'll saybasically just to make you seem
like you're lying, or what'shappening is actually not
happening.
So, you know, and and this isall happening while they're
trying to destroy your life andwhile they're trying to reroute
your destiny.
But, you know, the devil is aliar, and so is his
(34:04):
mother-in-law.
Whatever the enemy meant forbad, God will turn into good.
And so when we look at all this,I want to kind of break this all
down, right?
I've said a lot, I've sharedsome stats, and you know, I've
shared some examples of whatI've experienced, although I
(34:25):
haven't gone into like I haven'tdone like a deep dive, but you
know, high-level examples ofwhat I've experienced, you know,
with like just bias programmingas well as malicious, you know,
data poisoning and algorithmicpoisoning.
But let's look at it, you know,from like a structural
(34:49):
standpoint and how does thatactually feed into the system?
And for those who are listening,you probably, you know, you
can't see this, but I I do havelike a diagram up just basically
to help you visualize what I'mtalking about and how this
actually happens.
So if you're listening on like apodcast platform that's just
(35:12):
audio, check out the the YouTubeversion, you know, for the
visual.
I'm also on Rumble, so you knowyou can check that out as well.
The videos, I usually post thevideos, the visuals to YouTube
first.
So, you know, check out YouTube.
But if we look at the the firstsegment or the first part,
(35:33):
right?
Which is core bias and maliciousinjections, which speaks about,
you know, the core bias, whichis the programmers, right?
So as they're programmingcertain technologies, they're
training it, but the data isbiased, right?
Whether it's conscious orunconscious.
So you have the bias data, thetraining data, and then you now
(35:57):
have because someone hates you,someone's jealous of you,
someone you offended knowinglyor unknowingly, and uh is now
maliciously injecting, you know,malicious data into the system.
And then now you havealgorithmic poisoning.
And so that is the input, right?
(36:18):
That is the input, garbage in,garbage out.
So now what do you have?
You have bias programming andmalicious actors.
Now, the second part of this isthe algorithmic and platform
manipulation, right?
So that includes search enginedistortion, keyword triggers, ad
(36:43):
targeting bias, if thenprotocols, you know, that are
manipulated.
You know, like for example, ifI'm, let's say I'm searching for
targeted individual or cyberstalking, you know, I'll
probably see cyberpunk orscammer or you know, whatever,
(37:05):
right?
I I I my search results are arebasically being manipulated, but
also, you know, different thingson on my feed are also being
manipulated as a result.
And so the bias training data,the malicious actor injection
yields bias programming andmalicious actors, which then
(37:31):
yields to algorithmic poisoning.
Now, the third piece of thispuzzle or diagram is your
environmental and surveillancetactics, right?
Um, so I mentioned before thatin addition to all of these
devices and things that theyhave, you have uh facial
(37:54):
recognition alerts, right?
I can literally go outside withno technology.
We know that there are cameraseverywhere.
Wherever you go, there arecameras, right?
If you go to the store, if yougo outside, if I'm in a hotel
lobby, there are cameras there.
So even without any devices, orif even if my devices are
shielded and turned off, youhave these facial recognition
(38:17):
alerts.
And then you have uh spyware anddevice hijacking, right?
So that will trigger an alertto, you know, maybe someone
that's close by, they'll get amessage, you know, they'll get
triggered.
You know, I mentioned thisbefore.
The first the tactic is tooffend or alarm and then
basically, you know, try tomanipulate that person basically
(38:41):
to now target me as the targetedindividual.
And so their system will also behijacked, even if they're
they're not aware of that.
Now their system has spyware onit and their their devices now
become hijacked.
And then you you also have thepublic space monitoring and loot
metadata, right?
(39:02):
So the the hotel lobby is theperfect example of that.
And so, you know, that's that'sthat's key.
I I just want to kind of pausehere for a little bit.
That's the key thing.
Um, a lot of people who areinvolved in this bot network,
(39:27):
they don't realize that theirsystem is also hijacked.
They don't realize that theyalso have spyware.
They don't realize that thenetwork as a whole is hijacked.
Because the only way that or theonly reason why you got the
(39:47):
message was because the system,the network has information
about you.
They know what your triggersare, they know what's going to
offend you.
So then they send these, youknow, things, these messages to
you.
And then now they program youmentally to basically become
(40:08):
part of their force, their theirmalicious workforce, their
malicious uh bot network, right?
And this now triggers or spillsover into now your personal,
professional, and spiritualnetwork, right?
(40:30):
So, what does that look like?
It that looks like reputationaldistortion, purchase
interference, as I mentionedbefore, platform suppression,
and spiritual infiltration.
So this basically invades everyaspect of your life.
So that bias programming and themalicious actors basically will
(40:57):
now trigger the bias trainingdata, right?
You have the bias programmingand the malicious actors via the
malicious actor injection andthe bias training data, which
then leads to algorithmicpoisoning and ad targeting
distortion and also maliciousprofiling or just you know,
(41:23):
incorrect profiling, if youwill.
So, what does the full systemreveal?
So the full system, the fullpicture basically shows you how
this all comes together, right?
So let's look at it.
So here we see we have biasprogramming plus malicious
(41:48):
actors, right, will causepersonal reputation, distortion,
stigmas, rejection, exclusion,just harm, right?
Reputational harm.
Then you have the pro the biasprogramming and the malicious
actors will through public uhspaces, they the user patterns
(42:11):
will now trigger statements thatwill launch more harassment,
right?
These statements will launchmore harassment, and then now
you also have social media.
There are, you know, thingsspread so quickly on social
media.
So now things are being spreaduh via social media before
(42:33):
spyware, adware, malware,information leaks, you know,
manipulated data leaks andthings of that nature.
And then also obviously yourplatform, whether it's actually
shadow ban or blocked orobstructed, things of that
nature, and interference.
And then you also have uh thespiritual aspect I mentioned,
(42:54):
which is like spiritualinfiltration, things of that
nature.
I have the last episode, seasontwo, episode four, where I talk
about on like during the secondhalf of the episode, I talk
about how technology can be usedfor spiritual warfare, right?
Um, when we talk aboutmonitoring spirits, we no longer
(43:16):
need to talk about monitoringspirits from a spiritual
standpoint because theseindividuals they have technology
to monitor you.
They no longer need to seekspiritual means to know what
you're doing to try to obstructyour progress or to try to
obstruct your purpose.
(43:37):
And from the bias programmingand the malicious actors, you
have, you know, online purchasesare just basically online um
activities being obstructed,manipulated.
Um, and so, you know, validtransactions being canceled, not
being able to use a platform to,you know, the the the way that
(43:58):
it was intended to be usedbecause certain things are not
are malfunctioning.
You have ad targeting distortionthat are quote unquote
personalized ads, messaging anduh products based off of bias
and malicious data.
You also have another output isfrom the bias programming and
(44:19):
the malicious actors, you havepredictive algorithm
interference, which includes AImaking outcomes based on the
malicious data, right?
AI basically, so then it's justbasically again a feedback loop.
It's a cycle of malicious databeing recycled.
And then also search enginemanipulation.
(44:42):
So through that, the the biasprogramming and the malicious
actors will also impact thesearch engine manipulation.
Algorithms basically willprioritize the distorted
information based on theprogram, the bias programming.
And then the bias programmingand the malicious actors also
(45:03):
yields to algorithmic bias, andthe AI reflects the distortion
and it magnifies it, and it'sprejudicial training data,
essentially.
And so, you know, as I mentionedbefore, it's basically the
marginalized, thedisenfranchised becoming even
(45:26):
more marginalized anddisenfranchised as a result of
this, right?
So it's basically if you had,you know, if you were already,
you know, if you already had uhcertain obstructions based on
your race, color, imagine thatbeing multiplied, you know, a
(45:47):
thousand, a million times overas a real as a result of that,
right?
Um, how do you overcome that?
How do you overcome that, right?
I I mean speaking out, askingfor, you know, for things to be
done differently in theindustry, for for new laws and
(46:10):
regulations, basically that thatthat police and regulate AI and
and the data and and and how umtechnology is being developed.
And but most importantly,prayer, right?
Prayer.
So, you know, as we're navnavigating this, right, you
(46:33):
know, as I mentioned before, itwe're not, we're not, we're
we're not just talking aboutstats, right?
We're talking about systems,we're talking about a system,
we're talking about disruptedsystem, we're talking about
systems that try to silence.
And so through the God-givenauthority, we have to disrupt
(46:55):
them, right?
We have to navigate the fog.
This is where discernmentbecomes survival.
You know, I've I've seen I'veseen, you know, Christians, I've
seen preachers, I've seeninfluencers make decisions or
make comments based on distorteddata.
(47:17):
Not just my data, but just basedon data.
You know, things that are beingspread, things that are going
viral.
And I think now more than ever,it's it's crucial to first rely
on the Holy Spirit, rely onthings that are offline first,
(47:41):
and then second on things thatare online online, and then
cross-checking that.
I I I've seen prophetic wordsgiven to individuals that I'm
like, that sounds like myscenario.
That sounds like this is likeGod is speaking directly to me.
(48:04):
I I I know like people would saythat, you know, that happens
oftentimes, and you know,sometimes God may have a word
for someone else and it's foryou.
But like there's been so manyinstances where that's happened
because unfortunately, my myinformation is like is has been
(48:25):
disseminated all over the place,and so people are using my data
to make information.
People are using my data, andthey think that it pertains
maybe to someone else, andthey're using it to give out
prophecy.
(48:45):
I've seen it, I've seen it.
I've watched, I've watchedpeople give prophetic words, and
I'm like, okay, I know that thisis based on information that was
extracted from my system.
Because look, I'm not againstusing technology.
I'm not against usingtechnology.
I use technology.
(49:06):
I I think we, you know, we needto keep up with the times, but
we have to use our discernmentand we have to be careful,
right?
As we're using the technology.
I, you know, I I've been able totell.
I'm like, okay, this prophet,and I'm not saying that the
(49:27):
person is a false prophet, andand it's not just one prophet.
I've seen it over, you know,I've seen a few prophets do
this.
And I'm like, okay, they'reusing, you know, I don't know
what AI agent or AI tool they'reusing, but I'm like, or
whatever, you know, theanalytics are feeding back to
(49:48):
them.
I'm like, okay, I can tell thatthey're using the analytics
basically to come up withprophecy or to uh to to give
someone like a prophetic word.
And I think we have to becareful with that.
(50:12):
I think we have to be very, verycareful with that because while
I think it's it's good to acertain extent that okay, you
have more insight into whoeverit is that you're talking to,
potentially, right?
Potentially.
But, you know, if that data isnot that person's data and
(50:34):
you're giving them a propheticword based on the data that you
think is their data, now you'remisleading that person.
And that person may be making adecision, a life, altering
decision based on that data.
And so, yes, we we we we alwayssay test the spirit, um, but you
know, some people they will justgo off of whatever they hear
(50:59):
because they know that, okay,this is a reputable prophet.
I know that they they mean well,and they'll just go off of that.
And the prophet may also meanwell, right?
But they don't realize thatthey're being fed either
distorted data, malicious data,or even data that they think
pertains to this person does notpertain to them.
(51:21):
Okay.
I kind of went off on a tangenthere, but this was something
that I've observed and I I I'vebeen meaning to actually do a
either a short segment on it, Iwanted to talk about it, and it
just came up right now.
Maybe, you know, God wanted meto talk about it, or I, you
(51:43):
know, I was I just uh kind ofwent off on a tangent.
But, you know, I I I think youknow, we have to use our
discernment here.
We have to use our discernment,we have to be careful with
whatever data is being fed tous, and we have to cross-check
it, and we can't just makedecisions based on that.
(52:06):
So I've learned as a result,I've learned to listen
differently, I've learned toresearch differently, I've
learned to, you know, whatevercomes up in my feed, you know, I
I, you know, I have tocross-check it, you know, even
(52:27):
like I said, like I've seenpeople come on my feed and I'm
like, okay, they're trolling.
Or I've seen people, you know,that say that they're targeted
individuals and I'm like, okay,they're they're a plant.
They're not really targeted.
You know, they have an incentiveto do this, or what have you.
So, you know, just differentscenarios.
And so we have to be verycareful, right?
(52:50):
We have to be very careful.
And I'm not saying just to takemy word as truth or what I'm
saying is truth, right?
Right, just like I'm saying tobe careful of what the algorithm
is feeding you, you know, yeah.
But what I am saying is youdon't you don't have the full
(53:12):
picture.
You don't have the full picture.
Whatever is being fed to you isdistorted, it's painting a
narrative, and I think a lot ofpeople are just basically
blindly using this information,and whether it's willingly, like
I said, I I I've said thisbefore.
I think this is something thatis, you know, natural,
(53:34):
technological, and spiritual.
Some people are blindedspiritually, and so they are
being manipulated intoattacking, believing, you know,
whatever it is that they'rethey're seeing.
And some people they just, youknow, they like gossip.
They the first thing that theysee is what they believe.
Some people are thirsty for forviews, comments, for you know,
(54:00):
to be popular, they're thirstyfor money.
And so if someone says, well,help us spread this
misinformation, or instead ofposting this on your podcast,
post this and then, you know,we'll help you, uh, you know,
we'll help you grow your podcastor we'll pay you.
(54:22):
I mean, I I I I really suspect alot of people are getting paid
to spread this information andwhether um people are actually
approaching them or because, youknow, the misinformation is
doing well, right?
And so let's spread because youknow, this is trending, so I'm
(54:44):
gonna post about it, even thoughit's wrong.
I'll I'll post about it too,because it's trending.
And so, you know, I've seen thatin terms of trying to capitalize
on a lie, right?
Not just in my situation, butI've seen that with other
people, you know, well-known,you know, people, especially
within the Christian faith,right?
(55:07):
But just in general, right?
Whether it's just, you know, aregular celebrity or what have
you.
So what when the system ispoisoning, when the system is
poisoned, clarity becomeswarfare as well.
And so we have to be verycareful about that.
So discernment is so important.
(55:31):
I've had to ask myself, is thisa coincidence or a coordinated
delay?
Is this person following me ormonitoring me?
Is this feed, is this videothat's popping up on my feed?
Is this like a planted video ornot?
Is this glitch random orreactive to my voice?
(55:53):
So I've learned, I've learned tobasically listen differently,
react differently, researchdifferently, just basically move
differently.
And, you know, I I've documentedpatterns, I've documented a lot
of things mentally because it'svery exhaustive.
(56:14):
Like so much happens in myday-to-day life.
Like every minute, every secondof my life is being monitored,
it would be impossible for me todocument everything.
But like I do take mental notesof certain things, and then if
there is like a repeated patternin like a certain instance or
(56:35):
things of things happening, youknow, I'll potentially document
that.
But, you know, I've learned totrust my instincts, I've learned
to pray, I've learned to rely onthe Holy Spirit, you know, I've
learned to just take a pause ona lot of different things, you
know, before I publish, before Iclick on a video, just things in
(56:58):
general in general, right?
Information that I'm seeing,I've just learned to pause
because it's all it's alldesigned to program us.
And you know, I've I use theprophetic example because in my
mind, in my mind, right, andagain, I I said I'm not calling
(57:22):
these people false prophets, butin my mind, if a prophet, if a
prophet, someone who's a seer,right, someone who has more
discernment than the averageperson, if they can be fooled,
if they can be tricked by thealgorithm, by the data, by the
(57:46):
technology, how much more canthe normal or average person be
tricked, be programmed, bemanipulated, how much more?
We are in very serious times.
(58:07):
And if we don't, if we don'ttake back our control, we are in
big trouble.
We need to use discernment.
We need to use discernmentbecause when the system is
poison, clarity becomes warfare.
(58:28):
We need to take back thecontrol.
We need to take back thecontrol.
And so I've learned that evensilence can be weaponized,
right?
So the they loop old data tomake it seem like I'm stuck,
right?
They'll exaggerate moments topaint me as unstable.
They violate privacy in everycategory, whether it's personal,
(58:50):
professional, spiritual, andthen use that stolen insight to
distort my image.
But I'm not what they loop,right?
I'm not what they label and I'mI'm what I live.
I'm what I live.
And you know, I I just think wehave to be very careful, very
(59:10):
careful.
And I'm I'm just kind of I'mnudging the prophetic voices,
the prophets, the true prophetsout there to be careful, be
careful, and just to everyone,because if a prophet can be
misguided, if a prophet can be,you know, if they can be
(59:34):
tricked, how much more can theaverage person?
Because they have they haveinsight that we don't.
They have insight that we don't.
And so like I've seen it.
I'm like, okay, wow, thisprophet is acting exactly as the
algorithm was designed to to tomake them react.
They're saying exactly what thealgorithm wants them to say.
(59:57):
You know, they're actuallymaking a mistake.
Mistake in saying that thisprophetic word is for this
person.
And I'm like, okay, I can tellthat they're using something,
you know, to give this person aprophetic word.
And it's, you know, while I knowthat they are potentially also
leaning on the Holy Spirit, butthey're leaning on the
technology also.
(01:00:19):
And unfortunately, thetechnology is wrong.
And so we need to be careful,ladies and gentlemen.
We need to be careful.
Now, fighting the flaw.
So what do we do?
We name it, we document it asmuch as possible, right?
(01:00:41):
We speak it, we speak out.
It's hard to documenteverything, as I mentioned
before.
We build platforms that can't behijacked.
I don't know.
If there are any targetedindividuals who are tech
experts, reach out.
If you're a cybersecurityexpert, reach out.
(01:01:03):
You know, if you are alegislator, reach out and let's
just make things better.
Let's do better.
Let's do better.
Judge Faith actually called outYouTube because of how her
information was being distortedand basically the misinformation
was being spread and YouTubewasn't doing anything about it.
(01:01:26):
And so, you know, she foughtback.
She created content and shebasically exposed the people
that were spreading lies abouther.
And so we we need to createcontent that can't, that can't
be loot.
And so we we need to walk intruth that can't be silenced.
(01:01:48):
And you know, I think the moreand more we we we speak our
truth and we become the ones whoare in charge of our narrative
and we don't allow thetechnology to take control, I
think we're in a betterposition.
(01:02:10):
But from the looks of things andwhat I've been seeing, it seems
like a lot of people have beenallowing the technology to lead
them as opposed to them leadingthe technology.
And so we are not in a goodposition.
Humanity is not in a goodposition, you know, the
(01:02:30):
spiritual leaders aren't in agood position because they're
also being manipulated.
The prophets, a lot of them havebeen manipulated by the
technology as well.
So we we need to kind of tapinto, we need to tap it into how
things were done beforetechnology while still using
(01:02:52):
technology, right?
And so we need to rely on ournatural senses first and then on
the technology.
What I demand and what we allshould demand is transparency,
right?
We need to have a diverse teamor diverse teams building AI.
(01:03:14):
We need to have audits of thetraining data that are being
used.
There needs to be oversight ofsurveillance tech.
There needs to be accountabilityfor digital harm.
There needs to be moreregulation around tech as a
whole.
I know that, you know, as acountry, we're at, you know,
(01:03:34):
we're trying to race to beatother countries with AI and
things of that nature, but wecan't forget ethics.
We can't take ethics out of itjust, you know, because we're
trying to be first, right?
We need to do it right.
We need to do it right.
And we need to implementregulations and laws, you know,
(01:03:58):
statutes around how technologyis being used, how data is being
used.
And so as Christians, we alsoneed to reclaim our spiritual
authority because this isn'tjust about algorithms, right?
As I mentioned, a lot of, youknow, the spiritual enemy that
(01:04:20):
we have, they're usingtechnology to obstruct us.
They're using technology.
As I said, they don't, they nolonger need monitoring spirits
spiritually, right?
Uh the technology has becometheir monitoring spirits.
So it's about agency, it's aboutreclaiming your voice, your
platform, and your presence.
(01:04:42):
And we need to stay spirituallystrong.
We also need to be informedtechnologically, right?
We we need to be trained, weneed to know how to use the most
and the latest data, the latesttechnology, but at the same
time, we need to be careful inwhat is being presented to us
and how we're being how we'reusing it, because that same
(01:05:07):
data, that same technology isalso being used to program us.
And if we're not careful, we'regonna fall into what we don't
actually want to fall into.
And so as I close this episode,I want to say that, you know,
(01:05:31):
they tried to loop me, label me,silence me, obstruct me, but I'm
still here and I'm stillspeaking.
And so with that being said, Iwant to make this decree.
I decree that every false flag,every poison algorithm, every
(01:05:55):
surveillance tactic used againstme shall be exposed, dismantled,
and rendered powerless.
I reclaim my voice, my platform,my presence, and I declare that
no weapon formed against meshall prosper, and every tongue
that rises up against me injudgment shall be condemned.
(01:06:16):
I walk in divine clarity,digital sovereignty, and
prophetic truth.
My testimony will not beintercepted, my breakthrough
will not be delayed, my identitywill not be distorted.
I am seen rightly, heardclearly, and covered completely
in Jesus' mighty name.
(01:06:37):
Amen, amen, amen.
Teleo, tale, tale.
Hello, welcome to the Diary of a Same Black Woman.
I am your host, Marie.
This is where I speak from theintersections of faith,
technology, surveillance, andsurvival.
This docuseries is where I namewhat's been done in the dark and
reclaim what's been stolen insilence.
(00:22):
Look, I'm not here to proveanything.
I'm here simply to testify/slashdocument my experiences as a
targeted individual.
Because what I've experiencedcan't be explained away by
glitches or paranoia.
It's deeper than that.
(00:43):
It's layered and it's real.
As the former CIO of the WhiteHouse, Teresa Payton said during
an interview on scammerspayback.
It's only paranoia if it's nottrue.
So on today's episode, I will goin and discuss digital
(01:07):
injustice, but not just the kindthat lives in the headlines or
research papers.
I'm talking about the kind thatshows up in your uploads, your
inbox, your neighborhood, oryour spirit.
And I'm not making absoluteclaims here.
I'm naming patterns.
(01:28):
I'm basically sharing what I'veobserved, what I've endured, and
what I continue to resist.
Because when you're targeted,the attack doesn't stay in one
lane.
It spreads.
It spreads into your personallife, your professional space,
your spiritual walk, and itdoesn't always come through the
(01:52):
front door.
Sometimes it's spyware,sometimes it's a neighbor on a
balcony, sometimes it's a botthat follows you just to watch,
not support.
So let's look at the algorithmicmirror.
They say that algorithms reflectus, right?
(02:12):
But what if the reflection iswarped?
What if the mirror is beingmanipulated by unseen hands,
malicious networks, or scriptsrunning in the background?
Here's what I've seen podcastepisodes that stall, the
inability to upload or getflagged before I even publish
(02:35):
something, depending on theplatform, right?
Thankfully, I've managed tochallenge these and have worked
around these obstructions,although the bad actors and
malicious codes continue toattempt to obstruct the progress
of the destined state.
Now, metrics don't match thefeedback I've received.
(02:55):
Followers who feel like plants,watching, not engaging, but
maybe even reporting, right?
And so a feed that's full ofcreators who either seem to have
read or heard content, but youknow, it's not reflected in the
analytics or stats, but thosewho seem to have been
(03:18):
commissioned also to troll,perpetrate false claims,
narratives, and make snarkcomments relating to recent
posts or just overall characterassassination and or whatnot,
right?
And so you also have peoplephysically lurking, whether it's
(03:38):
on balconies, parking lots,listening in, not to support,
but to intercept, to be nosy.
And I can say right now, I hearan echo as I'm recording this,
and I don't know if it's justthe structure of where I am that
has an echo, or if it'ssomething, a device that's been
(04:00):
planted and that's recording methat's causing the echo.
Now, also, I for the past sinceI've started this podcast, I've
been, you know, wondering ifthere's possible content that
leaks before I even share itpublicly, right?
Because I am constantly beingmonitored, my networks have been
(04:21):
infiltrated, my devices havebeen infiltrated, and people are
constantly trying to record,monitor me through various
means.
There's also been patterns oftechnological interference that
align too precisely with momentsof breakthrough or truth
telling, right?
(04:42):
Even if I'm using or trying toupload something, if I'm using
AI and I'm doing research on aparticular topic, you know,
whether it's to troubleshoot orto prepare for my podcast as I
do research on, you know, what'sbeen happening, right?
Not only to me, but potentiallyto others, and if there is a
(05:04):
pattern of this occurrence.
You also have loops of data ormoments, right?
Whether it's a snapshot in time,doxxed info, private private
data leaks, uh recycled um topaint a false picture or just
basically to amplify what'snegative, right?
And so they don't want to lookat the entire person or
(05:28):
individual, they magnify um amoment in time, something that
has happened, and try tobasically paint that as the
entire individual.
And so even without technology,even without tech, right, in
hand, I've noticed that I'vebeen monitored.
And whether I've walked intohotel lobbies and watched uh
(05:52):
cameras grab my face.
Seconds later, the front deskphone rings or a text goes out
to the host.
And I've seen it happen instores or on sidewalks in places
where I should be, notnecessarily anonymous, where
basically I should feel free.
I know that there's cameraseverywhere, but I I believe most
people, when they go out, theydon't get like a trigger, like
(06:16):
something is not triggered or anemail is not sent, a text
message is not sent when theyappear somewhere.
But that happens to me.
Like if I walk out my house tothrow out the garbage, you know,
there's a trigger, something istriggered electronically,
someone receives a message, andyou know, people react to that.
(06:38):
And so because someone orsomething is watching, right?
Digitally, there's been scriptsthat's been written to basically
trigger these things.
So it's a combination of notjust the digital, but also the
physical.
And so um it's not justalgorithmically, but also
(06:59):
environmentally, right?
So because these people who theyhave been programmed, they have
been programmed to watch me,they have been brainwashed to
watch me, and so they are nowpart of that network, right?
Not only their phones and theirdevices are part of part of this
bot network, but they have alsosubscribed to the network
(07:22):
because now they're devoted andthey're committed to monitor me,
to spread information, um, to,you know, dilute or you know,
the truth, and basically to toharm me um in every aspect.
And so this is what happens whentechnology is used not to serve,
(07:44):
but to surveil.
And um, you know, it's not usedto connect, but to control.
And so what are the real worldrepercussions when you have all
of this going on, right?
When the algorithm is poison, itdoesn't just affect what people
see, it actually affects whatyou can do, right?
(08:07):
And in my situation, I'veexperienced this firsthand,
whether it's online purchasesthat mysteriously fail or get
flagged, right?
For example, Best Buy.
You know how many times I'vetried to purchase certain things
on Best Buy where the the youknow the purchase would go
through and then like minuteslater it would be canceled,
(08:31):
right?
And I have a subscription withthem.
And regardless of that fact,I've actually had to go into the
store to purchase uh like a GeekSquad membership, and even after
physically walking in with mycredit card and making that
purchase, and they know that Ihave a subscription.
(08:53):
If I try to make a purchasetoday, it gets flagged.
And so there's a lot of thingsthat's basically embedded and uh
maliciously scripted basicallyto prevent me from doing certain
things, especially as it relatesto tech support or tech devices
and things of that nature.
(09:15):
You know, you have instanceswhere a c accounts will lock or
have glitches while uh rightafter I speak, you know, the
truth or right after I use akeyword, you know, that might
hint that I'm either whetherit's I'm praying or I am trying
to research, you know, thingsthat might help me digitally,
(09:39):
right?
Things that might help mesuppress the digital survey
surveillance, things that mighthelp me to remove malware,
things of that nature, you know,videos that have vanished and
things of that nature because ofcorruption on my devices, you
know, so tampering withevidence, things that have sites
(10:02):
that have malfunctioned, right?
I've had sites, especially frommy ISP, where certain tabs, like
I wasn't able, like for weeks, Ihaven't been able to access the
internet tab, right?
Why can't I access it?
So I had to actually try to finda workaround to access the
internet tab or certain buttons,right, that don't function, like
(10:26):
you can't actually click on it.
And you know, I've experiencedthat on multiple websites.
So these are all things thathave happened because of uh
digital corruption and thingsthat have been used to attack
me.
So first digitally, and thenobviously you have, I would say,
(10:48):
symptoms from the digitalattack, right?
And some of those symptomsinclude uh job opportunities
that disappear withoutexplanation, right?
Whether it's due to reputationaldamage, things that that they
have painted, things that theyhave received, you know, through
(11:08):
planted data.
And so you have reputationaldamage from half-truth loops and
manipulated metadata.
And the people who receive thedistorted info act on it, right?
And so this actually ends upblocking reporting or spreading
lies.
So that is what's occurring, um,ladies and gentlemen.
(11:32):
And so, even from a spiritualstandpoint, so I've mentioned
this before in previous episodeswhere spiritual spaces, whether
it's virtually or in person,have been become infiltrated,
whether it's YouTube, you know,spiritual space, a Zoom
spiritual space, an in-personspiritual space where these
(11:54):
networks, whether it's a digitalnetwork uh via devices or people
actually following you intothose spaces and where they're
basically trying to spread lies,spread misinformation, um,
spread private information aswell, right?
So it's a mixture.
And so oftentimes people justaccept everything that they see
(12:18):
as truth and they don't theydon't look at it from an
objective standpoint.
And so discernment is clouded,it becomes clouded by the
digital distortion.
When we're looking at thisdistortion, how can it actually
negatively impact you?
Right?
It's it's it's not just you knowdata that's being spread and
(12:43):
sent, but it's actually it it'sit's rooted in in a lot, it's a
lot deeper.
It's a lot, and so you know,these aren't just technical
flaws, they're livedconsequences, right?
And when they're aimed atsomeone like me, someone who
speaks my truth, someone whospeaks the truth, disrupts
(13:06):
silence, someone who disruptssilence and refuses to conform,
they become tools ofsuppression.
And let's look at some of somestats here.
So facial recognition systemsmisidentify darker skinned women
up to 34.7% of the time comparedto just 0.8% for lighter skinned
(13:32):
men.
And this is from MIT Media Laband Buolamwinny and Jabrew from
2018.
I'm just, this is just regularstats, right?
This is not even stats based onmalicious actors interfering.
(13:52):
This is just based on, you know,the the technology and and how
it was programmed, right?
Whether that was conscious orunconscious bias, you know, this
this is basically the end resultof it.
And additionally, AfricanAmerican and Asian faces are 10
(14:14):
to 100 times more likely to bemisidentified than white male
faces.
This is from NIST 2019, and thenpoisoning just 0.001% of
training data can cause AImodels to spread misinformation
(14:38):
or behave maliciously.
And you know, this is this ishappening more and more.
I recently watched uh a videothat uh Judge Faith, Judge
Faith, she posted a video abouthow misinformation about her and
(14:59):
her husband getting a divorcewas spreading, and you know, it
started with one blog, and thenother people took it and it was
just spreading like crazy, andthen it basically came, it
started to become like an actualsearch results as fact.
And so, you know, all it takesis just a small, you know,
(15:21):
misinformation or a small datapoint being spread um basically
to poison and affect, you know,someone's reputation,
livelihood, and so forth.
I'm happy in her in herscenario, she was actually able
not only to identify the peoplebehind the the malicious data
(15:46):
that was being spread, she wasactually able to get recourse.
And, you know, that is that isvery encouraging.
You know, however, I I mean noteveryone has the means that you
know she has basically to to tobasically get to the root cause
(16:07):
of of what was happening.
And in her case, you know,people were actually publicizing
information openly about her.
In my case, it's being done morecovertly, so I think it's it's
it's a little harder toidentify.
But the stats about the the0.001% data poisoning comes from
(16:28):
Cornell University, and it waspublished in 2020.
Now, predictive uh policingtools have wrongly flagged
individuals based on biaseddata, leading to false arrests
and reputational harm.
And this is from ProPublica andPred poll investigation.
(16:51):
And that's not just predictivepolicing tools, but also even
like with social media, youknow.
I know when I first launched mymy YouTube page and and
Instagram, it was flaggedimmediately because of, you
know, the corruption that washappening behind it.
And, you know, once they lookedat it immediately, it was
(17:14):
basically they saw that it was amistake, right?
And so that's how these thingscan basically trigger and affect
you, you know, negatively.
But this is not just one-sided,right?
It's not just about theprogramming, it's it's it's a
trifecta.
(17:35):
So it's bias in design, it'sbias in data, it's bias in
deployment, right?
Garbage in, garbage out.
And so when a malicious actorenters the equation, it's not
just bias anymore, it'sweaponized distortion.
Let me break it down for y'all.
(17:55):
Let me break it down for y'all.
AI, right, when we talk aboutbias in programming, AI reflects
its creators, right?
If the original training data ordesign choices are skewed,
whether consciously orunconsciously, the output will
mirror that bias.
(18:15):
For example, I spoke aboutfacial recognition earlier, you
know, in this segment.
And so facial recognition uhsystems trained mostly on
lighter skin faces misidentifieddarker skin individuals up to a
hundred times more often, right?
And then so language modelstrained on internet data absorb
(18:38):
toxic stereotypes, thenreproduce them as neutral
output.
And so that's those are twoexamples of bias programming.
Then you have malicious actorsamplifying the bias, right?
So now imagine someone with illintent, not just negligence,
(18:58):
injecting adversarial prompts,right?
They're they're injectingadversarial prompts, poison data
sets or surveillance triggers,that's bias, you know, and so
you have, and then now you havethese adversarial networks that
can sudden, you know, suddenlymanipulate AI, basically to
(19:22):
ignore certain voices, flagbenign content, or prioritize
harmful narratives.
And I've experienced this inevery aspect of my life, you
know, and so there's coordinatedsuppression, right?
So it's within the technology,but it's also now embedded in
the bot networks, but also theactual human networks that are
(19:45):
willfully, you know,participating.
So the coordinated suppressioncan look like the algorithmic
coincidence, but it it's youknow, it's actually engineered
silence, right?
Especially when targeting eitherprophetic or restorative voices.
So multiplication now happensthrough the infrastructure as a
(20:09):
result of that.
So once the bias is embedded,right there, either whether once
the bias is embedded, eitherthrough the the programming,
right, the initial programming,whether it was by bias or
intentional bias orunintentional bias, as well as
the malicious actors that arenow also injecting, you know,
(20:36):
poison data or malicious datainto the system.
Now this begins to spread acrossplatforms, right?
Social media, search, cloudtools, text messages, search
results, right?
Across the devices, phones,routers, power lines, across
(20:57):
perceptions, how others see you,how you see yourself.
So it it it it's viral, right?
It's viral, it's global.
Technology is basically there isno barrier to technology, right?
It's not, it's not, it's not inone location.
(21:18):
It's not confined to onelocation.
So it spreads and it spreadsrapidly, right?
Especially if it's gossip,especially if it's malicious
gossip, especially if it'ssalacious, it will spread.
This is why I believe it's soimportant for me to document
what's happening to me.
(21:39):
Documenting and diagnosing theobstruction that I am
experiencing real time, as wellas reclaiming my voice and
narrative.
You know, I believe it's vitalto do that.
And I'm not just resisting thebias, I'm disrupting it.
I'm disrupting the entirefeedback loop that multiplies it
(22:00):
as much as I can, right?
Because there's been a lot ofobstruction.
You know, there's a saying wherethere's movement, there's
friction.
There's been a lot of friction,there's been a lot of attempts
to obstruct the progress of thedestined state.
But, you know, I will continueto push through.
So let's look at biasmultiplied, right?
(22:22):
So when AI becomes the weapon,they say numbers don't lie,
right?
But what happens when thesystems counting those numbers
are trained to ignore you?
What happens when the algorithmisn't just flawed but
weaponized?
Let's talk about the stats alittle further, right?
We looked at some stats in theprior segment.
(22:44):
Let's let's look a littledeeper.
So let's look at then versusnow, right?
The stats they gave us versusthe truth emerging, right?
In 2020, they said that AI wasthe future.
Global IT spending hoveredaround$3.9 trillion.
(23:07):
Bias was framed as a glitch, abug to be patched.
Now, in 2025, global IT spendingis projected to hit$5.6
trillion, and AI is thecenterpiece.
(23:27):
The AI market alone is worth$500plus billion dollars, and that's
expected to triple by 2031.
Okay.
But here's the kicker (23:39):
36% of companies admit that AI bias has
already harmed their business,and 62% lost revenue, 61% lost
customers, and that's just whatthey're willing to admit, right?
(24:01):
I can't admit as anentrepreneur, as a small
business person, you know, as aprofessional, you know, I've
been impacted.
My business has been impacted.
You know, my mentorshipopportunities have been
impacted.
A lot of different things havebeen impacted.
Career opportunities have beenimpacted.
(24:22):
And so, yes, I can attest tothat.
Now, bias isn't a bug, right?
It's it's a blueprint, right?
Let's just call it what it is.
It's not a bug, it's not a it'snot a glitch, it's a blueprint,
right?
It's the programmer, if theprogrammer, right?
If the programmer was biased,the output will be biased as
(24:44):
well.
Now, let's add, you know, themalicious actor into the mix or
a network determined to destroyyou, and that malicious output
is multiplied several timesover, right?
And and we've seen that.
We see that in the experiencesthat I've been basically
documenting and sharing with youall.
(25:04):
And I'm sure, you know, thepeople that have uh lost
revenue, the 62% businesses thathave lost revenue or customers,
you know, they've seen that aswell.
And so those who are already, Ibelieve that those who are
already disenfranchised ormarginalized typically get the
(25:26):
brunt of it, unfortunately.
So imagine having pre-existingobstacles based on your color,
your ethnicity, your sex, yourreligious beliefs, and then that
negatively being amplified evenhigher or to the highest degree
by the way that technology wasprogrammed, and then also by
(25:50):
malicious actors.
The technology and the maliciousactors essentially set you up
for failure, whether that's whatwas intentionally or you know,
unintentionally, you know, bythe programmers, whether that
was done intentionally orunintentionally by the
programmers, and or how themalicious actors or networks
(26:16):
have exploited what they wereable to exploit, right?
And so let's call let's call itwhat it is.
Let's call it what it is.
It's engineered erasure, it'sengineered erasure.
So let's look at some more statshere.
(26:37):
GPT2 reduces black specificwords by 45.3% and female
specific words by 43.4% incontent filtering tests.
In hiring tools, resumes withblack male names are
systematically filtered out.
And in long documents, largelanguage models are trained to
(27:02):
ignore critical information,especially if it's buried,
nuanced, or inconvenient.
This isn't just about fairness,it's about who gets seen, who
gets silenced, and who getserased, right?
As I mentioned, it's engineerederasure.
You know, that's essentiallywhat it is.
(27:22):
So let's look even deeper.
Let's look at the feedback loopof suppression.
Bias in, bias out, right?
I mentioned it before, garbagein, garbage out.
But when you add maliciousintent, it becomes a feedback
loop of suppression.
So you have adversarial actorsthat can train AI to one,
(27:45):
ignore, to flag, to shadow ban,you know, whoever is basically
speaking out.
You have an infrastructure-levelmanipulation from power lines to
platforms, they amplify and umthe distortion, and then you
have the result ontologicalbias.
(28:06):
AI doesn't just reflect theworld, it reshapes what we're
allowed to imagine.
And, you know, a lot of people,they just whatever they get and
see, that's what they take astruth, and they don't even
second guess it.
And so prophetic disruption,reclaiming the narrative.
(28:26):
This is why, you know, I speak,this is why I document, this is
why I refuse to be edited out ofmy own story because you know,
everyone else is trying to tellmy story.
Everyone thinks they know me,everyone thinks they know me
better than I know myself,better than they, better than
(28:46):
those who are close to me knowme.
So it's it's it's prettyinteresting.
So, you know, every time, youknow, I think it's important,
you know, to speak out becauseevery time we name a pattern, we
break its power.
Every time we speak the truth,we rewire the algorithm, right?
Hopefully, every time we show upunfiltered, unbought,
(29:11):
unbothered, we reclaim the mic.
And so let's look at whereprejudice hides, right?
Again, garbage in, garbage outis the same.
But what if garbage was planted,right?
What if the algorithmic, youknow, feed or the algorithm was
(29:35):
fed poison data intentionally,right?
That's where you had themalicious actors.
That's why, you know, I'mconstantly seeing all these
obscene things in my feed, eventhough I'm not even touching um
this stuff or things that Ihaven't even looked up in like
ages, or things that I'm noteven involved in that I'm
(29:57):
seeing.
But, you know, this stuff isintentional.
Being planted.
And so someone who knew thatthey knew exactly what they were
doing.
It was intentional.
And so, like, I've seen how youknow one person tuned into a
malicious network can basicallytrigger a chain of reaction,
(30:17):
right?
And that's why now, like, I havea botnet of people and devices
that are essentially attackingme.
So they feed the system falseflags, right?
As they're receiving data,they're feeding it more data.
They're distorting the data,they're distorting the
narrative.
And so they're, you know, youhave, you now have distorted
(30:40):
metadata and you havemanipulated scripts.
I'm talking about tech scripts,such as, you know, whether it's
Python scripts or whateverscripts that they're using,
manipulated code and things ofthat nature.
You have the algorithm nowthat's absorbing this stuff and
it treats it as truth.
(31:00):
And people treat it as truth.
People make decisions based onthat.
Companies make decisions basedon that, you know, and this now
begins, you know, they begin torespond accordingly, right?
Platforms start to flag content,they slow uploads, things freeze
up, you know, decisions,important decisions are made
(31:24):
based on these, you know, thingsthat have been fed into the
system.
And so, you know, even likeonline purchases, right?
As I mentioned, they getdisrupted, you know, they'll get
canceled, you know, pages won'tload so many different things,
right?
Recommendations turn hostile, orrecommendations are obscene, you
(31:49):
know.
So people, you, you know, so nowyou have people who receive this
poison data.
As I mentioned, whether biasedor not, they begin to act on it,
right?
They begin to act on it, someconsciously, some unconsciously.
They take whatever they seefirsthand as truth, they make
(32:10):
decisions, life alteringdecisions, you know, on this,
right?
So, you know, this is nowblocking whether a system is
blocking you, whether a system,a platform is blocking you, or
it's reporting you, or it'signoring you, or it's not
loading, whether it's a DDoSattack, or just different things
(32:33):
that are happening, you know,behind the scenes um
technologically, or peoplereacting on it, you know, it
this is what's happening, oreven worse, right?
Some people are joining in,they're joining the bot network,
they're joining the gangstalkers.
And so now it's not just thealgorithm, it's a ripple effect.
(32:58):
It's a ripple effect.
And we, you know, I've spokentwo episodes ago, I've spoken
about the ripple effect and andhow that can, you know, the
ripple of silence and the rippleof harm.
Go back and watch it.
So once a malicious seed isplanted, it can grow into a
forest of obstruction.
And the worst part, the worstpart is that it's hard to prove
(33:23):
because the system is designedto look neutral, right?
And so it's designed to hide itshand.
And so, you know, people willsay it's a glitch, it's a
coincidence, or worse, that it'snot happening, or that you're
paranoid or delusional, or thatyou're even insane, you're not
in your right mind, you'reunstable, whatever, you know,
(33:44):
whatever it is that they'll saybasically just to make you seem
like you're lying, or what'shappening is actually not
happening.
So, you know, and and this isall happening while they're
trying to destroy your life andwhile they're trying to reroute
your destiny.
But, you know, the devil is aliar, and so is his
(34:04):
mother-in-law.
Whatever the enemy meant forbad, God will turn into good.
And so when we look at all this,I want to kind of break this all
down, right?
I've said a lot, I've sharedsome stats, and you know, I've
shared some examples of whatI've experienced, although I
(34:25):
haven't gone into like I haven'tdone like a deep dive, but you
know, high-level examples ofwhat I've experienced, you know,
with like just bias programmingas well as malicious, you know,
data poisoning and algorithmicpoisoning.
But let's look at it, you know,from like a structural
(34:49):
standpoint and how does thatactually feed into the system?
And for those who are listening,you probably, you know, you
can't see this, but I I do havelike a diagram up just basically
to help you visualize what I'mtalking about and how this
actually happens.
So if you're listening on like apodcast platform that's just
(35:12):
audio, check out the the YouTubeversion, you know, for the
visual.
I'm also on Rumble, so you knowyou can check that out as well.
The videos, I usually post thevideos, the visuals to YouTube
first.
So, you know, check out YouTube.
But if we look at the the firstsegment or the first part,
(35:33):
right?
Which is core bias and maliciousinjections, which speaks about,
you know, the core bias, whichis the programmers, right?
So as they're programmingcertain technologies, they're
training it, but the data isbiased, right?
Whether it's conscious orunconscious.
So you have the bias data, thetraining data, and then you now
(35:57):
have because someone hates you,someone's jealous of you,
someone you offended knowinglyor unknowingly, and uh is now
maliciously injecting, you know,malicious data into the system.
And then now you havealgorithmic poisoning.
And so that is the input, right?
(36:18):
That is the input, garbage in,garbage out.
So now what do you have?
You have bias programming andmalicious actors.
Now, the second part of this isthe algorithmic and platform
manipulation, right?
So that includes search enginedistortion, keyword triggers, ad
(36:43):
targeting bias, if thenprotocols, you know, that are
manipulated.
You know, like for example, ifI'm, let's say I'm searching for
targeted individual or cyberstalking, you know, I'll
probably see cyberpunk orscammer or you know, whatever,
(37:05):
right?
I I I my search results are arebasically being manipulated, but
also, you know, different thingson on my feed are also being
manipulated as a result.
And so the bias training data,the malicious actor injection
yields bias programming andmalicious actors, which then
(37:31):
yields to algorithmic poisoning.
Now, the third piece of thispuzzle or diagram is your
environmental and surveillancetactics, right?
Um, so I mentioned before thatin addition to all of these
devices and things that theyhave, you have uh facial
(37:54):
recognition alerts, right?
I can literally go outside withno technology.
We know that there are cameraseverywhere.
Wherever you go, there arecameras, right?
If you go to the store, if yougo outside, if I'm in a hotel
lobby, there are cameras there.
So even without any devices, orif even if my devices are
shielded and turned off, youhave these facial recognition
(38:17):
alerts.
And then you have uh spyware anddevice hijacking, right?
So that will trigger an alertto, you know, maybe someone
that's close by, they'll get amessage, you know, they'll get
triggered.
You know, I mentioned thisbefore.
The first the tactic is tooffend or alarm and then
basically, you know, try tomanipulate that person basically
(38:41):
to now target me as the targetedindividual.
And so their system will also behijacked, even if they're
they're not aware of that.
Now their system has spyware onit and their their devices now
become hijacked.
And then you you also have thepublic space monitoring and loot
metadata, right?
(39:02):
So the the hotel lobby is theperfect example of that.
And so, you know, that's that'sthat's key.
I I just want to kind of pausehere for a little bit.
That's the key thing.
Um, a lot of people who areinvolved in this bot network,
(39:27):
they don't realize that theirsystem is also hijacked.
They don't realize that theyalso have spyware.
They don't realize that thenetwork as a whole is hijacked.
Because the only way that or theonly reason why you got the
(39:47):
message was because the system,the network has information
about you.
They know what your triggersare, they know what's going to
offend you.
So then they send these, youknow, things, these messages to
you.
And then now they program youmentally to basically become
(40:08):
part of their force, their theirmalicious workforce, their
malicious uh bot network, right?
And this now triggers or spillsover into now your personal,
professional, and spiritualnetwork, right?
(40:30):
So, what does that look like?
It that looks like reputationaldistortion, purchase
interference, as I mentionedbefore, platform suppression,
and spiritual infiltration.
So this basically invades everyaspect of your life.
So that bias programming and themalicious actors basically will
(40:57):
now trigger the bias trainingdata, right?
You have the bias programmingand the malicious actors via the
malicious actor injection andthe bias training data, which
then leads to algorithmicpoisoning and ad targeting
distortion and also maliciousprofiling or just you know,
(41:23):
incorrect profiling, if youwill.
So, what does the full systemreveal?
So the full system, the fullpicture basically shows you how
this all comes together, right?
So let's look at it.
So here we see we have biasprogramming plus malicious
(41:48):
actors, right, will causepersonal reputation, distortion,
stigmas, rejection, exclusion,just harm, right?
Reputational harm.
Then you have the pro the biasprogramming and the malicious
actors will through public uhspaces, they the user patterns
(42:11):
will now trigger statements thatwill launch more harassment,
right?
These statements will launchmore harassment, and then now
you also have social media.
There are, you know, thingsspread so quickly on social
media.
So now things are being spreaduh via social media before
(42:33):
spyware, adware, malware,information leaks, you know,
manipulated data leaks andthings of that nature.
And then also obviously yourplatform, whether it's actually
shadow ban or blocked orobstructed, things of that
nature, and interference.
And then you also have uh thespiritual aspect I mentioned,
(42:54):
which is like spiritualinfiltration, things of that
nature.
I have the last episode, seasontwo, episode four, where I talk
about on like during the secondhalf of the episode, I talk
about how technology can be usedfor spiritual warfare, right?
Um, when we talk aboutmonitoring spirits, we no longer
(43:16):
need to talk about monitoringspirits from a spiritual
standpoint because theseindividuals they have technology
to monitor you.
They no longer need to seekspiritual means to know what
you're doing to try to obstructyour progress or to try to
obstruct your purpose.
(43:37):
And from the bias programmingand the malicious actors, you
have, you know, online purchasesare just basically online um
activities being obstructed,manipulated.
Um, and so, you know, validtransactions being canceled, not
being able to use a platform to,you know, the the the way that
(43:58):
it was intended to be usedbecause certain things are not
are malfunctioning.
You have ad targeting distortionthat are quote unquote
personalized ads, messaging anduh products based off of bias
and malicious data.
You also have another output isfrom the bias programming and
(44:19):
the malicious actors, you havepredictive algorithm
interference, which includes AImaking outcomes based on the
malicious data, right?
AI basically, so then it's justbasically again a feedback loop.
It's a cycle of malicious databeing recycled.
And then also search enginemanipulation.
(44:42):
So through that, the the biasprogramming and the malicious
actors will also impact thesearch engine manipulation.
Algorithms basically willprioritize the distorted
information based on theprogram, the bias programming.
And then the bias programmingand the malicious actors also
(45:03):
yields to algorithmic bias, andthe AI reflects the distortion
and it magnifies it, and it'sprejudicial training data,
essentially.
And so, you know, as I mentionedbefore, it's basically the
marginalized, thedisenfranchised becoming even
(45:26):
more marginalized anddisenfranchised as a result of
this, right?
So it's basically if you had,you know, if you were already,
you know, if you already had uhcertain obstructions based on
your race, color, imagine thatbeing multiplied, you know, a
(45:47):
thousand, a million times overas a real as a result of that,
right?
Um, how do you overcome that?
How do you overcome that, right?
I I mean speaking out, askingfor, you know, for things to be
done differently in theindustry, for for new laws and
(46:10):
regulations, basically that thatthat police and regulate AI and
and the data and and and how umtechnology is being developed.
And but most importantly,prayer, right?
Prayer.
So, you know, as we're navnavigating this, right, you
(46:33):
know, as I mentioned before, itwe're not, we're not, we're
we're not just talking aboutstats, right?
We're talking about systems,we're talking about a system,
we're talking about disruptedsystem, we're talking about
systems that try to silence.
And so through the God-givenauthority, we have to disrupt
(46:55):
them, right?
We have to navigate the fog.
This is where discernmentbecomes survival.
You know, I've I've seen I'veseen, you know, Christians, I've
seen preachers, I've seeninfluencers make decisions or
make comments based on distorteddata.
(47:17):
Not just my data, but just basedon data.
You know, things that are beingspread, things that are going
viral.
And I think now more than ever,it's it's crucial to first rely
on the Holy Spirit, rely onthings that are offline first,
(47:41):
and then second on things thatare online online, and then
cross-checking that.
I I I've seen prophetic wordsgiven to individuals that I'm
like, that sounds like myscenario.
That sounds like this is likeGod is speaking directly to me.
(48:04):
I I I know like people would saythat, you know, that happens
oftentimes, and you know,sometimes God may have a word
for someone else and it's foryou.
But like there's been so manyinstances where that's happened
because unfortunately, my myinformation is like is has been
(48:25):
disseminated all over the place,and so people are using my data
to make information.
People are using my data, andthey think that it pertains
maybe to someone else, andthey're using it to give out
prophecy.
(48:45):
I've seen it, I've seen it.
I've watched, I've watchedpeople give prophetic words, and
I'm like, okay, I know that thisis based on information that was
extracted from my system.
Because look, I'm not againstusing technology.
I'm not against usingtechnology.
I use technology.
(49:06):
I I think we, you know, we needto keep up with the times, but
we have to use our discernmentand we have to be careful,
right?
As we're using the technology.
I, you know, I I've been able totell.
I'm like, okay, this prophet,and I'm not saying that the
(49:27):
person is a false prophet, andand it's not just one prophet.
I've seen it over, you know,I've seen a few prophets do
this.
And I'm like, okay, they'reusing, you know, I don't know
what AI agent or AI tool they'reusing, but I'm like, or
whatever, you know, theanalytics are feeding back to
(49:48):
them.
I'm like, okay, I can tell thatthey're using the analytics
basically to come up withprophecy or to uh to to give
someone like a prophetic word.
And I think we have to becareful with that.
(50:12):
I think we have to be very, verycareful with that because while
I think it's it's good to acertain extent that okay, you
have more insight into whoeverit is that you're talking to,
potentially, right?
Potentially.
But, you know, if that data isnot that person's data and
(50:34):
you're giving them a propheticword based on the data that you
think is their data, now you'remisleading that person.
And that person may be making adecision, a life, altering
decision based on that data.
And so, yes, we we we we alwayssay test the spirit, um, but you
know, some people they will justgo off of whatever they hear
(50:59):
because they know that, okay,this is a reputable prophet.
I know that they they mean well,and they'll just go off of that.
And the prophet may also meanwell, right?
But they don't realize thatthey're being fed either
distorted data, malicious data,or even data that they think
pertains to this person does notpertain to them.
(51:21):
Okay.
I kind of went off on a tangenthere, but this was something
that I've observed and I I I'vebeen meaning to actually do a
either a short segment on it, Iwanted to talk about it, and it
just came up right now.
Maybe, you know, God wanted meto talk about it, or I, you
(51:43):
know, I was I just uh kind ofwent off on a tangent.
But, you know, I I I think youknow, we have to use our
discernment here.
We have to use our discernment,we have to be careful with
whatever data is being fed tous, and we have to cross-check
it, and we can't just makedecisions based on that.
(52:06):
So I've learned as a result,I've learned to listen
differently, I've learned toresearch differently, I've
learned to, you know, whatevercomes up in my feed, you know, I
I, you know, I have tocross-check it, you know, even
(52:27):
like I said, like I've seenpeople come on my feed and I'm
like, okay, they're trolling.
Or I've seen people, you know,that say that they're targeted
individuals and I'm like, okay,they're they're a plant.
They're not really targeted.
You know, they have an incentiveto do this, or what have you.
So, you know, just differentscenarios.
And so we have to be verycareful, right?
(52:50):
We have to be very careful.
And I'm not saying just to takemy word as truth or what I'm
saying is truth, right?
Right, just like I'm saying tobe careful of what the algorithm
is feeding you, you know, yeah.
But what I am saying is youdon't you don't have the full
(53:12):
picture.
You don't have the full picture.
Whatever is being fed to you isdistorted, it's painting a
narrative, and I think a lot ofpeople are just basically
blindly using this information,and whether it's willingly, like
I said, I I I've said thisbefore.
I think this is something thatis, you know, natural,
(53:34):
technological, and spiritual.
Some people are blindedspiritually, and so they are
being manipulated intoattacking, believing, you know,
whatever it is that they'rethey're seeing.
And some people they just, youknow, they like gossip.
They the first thing that theysee is what they believe.
Some people are thirsty for forviews, comments, for you know,
(54:00):
to be popular, they're thirstyfor money.
And so if someone says, well,help us spread this
misinformation, or instead ofposting this on your podcast,
post this and then, you know,we'll help you, uh, you know,
we'll help you grow your podcastor we'll pay you.
(54:22):
I mean, I I I I really suspect alot of people are getting paid
to spread this information andwhether um people are actually
approaching them or because, youknow, the misinformation is
doing well, right?
And so let's spread because youknow, this is trending, so I'm
(54:44):
gonna post about it, even thoughit's wrong.
I'll I'll post about it too,because it's trending.
And so, you know, I've seen thatin terms of trying to capitalize
on a lie, right?
Not just in my situation, butI've seen that with other
people, you know, well-known,you know, people, especially
within the Christian faith,right?
(55:07):
But just in general, right?
Whether it's just, you know, aregular celebrity or what have
you.
So what when the system ispoisoning, when the system is
poisoned, clarity becomeswarfare as well.
And so we have to be verycareful about that.
So discernment is so important.
(55:31):
I've had to ask myself, is thisa coincidence or a coordinated
delay?
Is this person following me ormonitoring me?
Is this feed, is this videothat's popping up on my feed?
Is this like a planted video ornot?
Is this glitch random orreactive to my voice?
(55:53):
So I've learned, I've learned tobasically listen differently,
react differently, researchdifferently, just basically move
differently.
And, you know, I I've documentedpatterns, I've documented a lot
of things mentally because it'svery exhaustive.
(56:14):
Like so much happens in myday-to-day life.
Like every minute, every secondof my life is being monitored,
it would be impossible for me todocument everything.
But like I do take mental notesof certain things, and then if
there is like a repeated patternin like a certain instance or
(56:35):
things of things happening, youknow, I'll potentially document
that.
But, you know, I've learned totrust my instincts, I've learned
to pray, I've learned to rely onthe Holy Spirit, you know, I've
learned to just take a pause ona lot of different things, you
know, before I publish, before Iclick on a video, just things in
(56:58):
general in general, right?
Information that I'm seeing,I've just learned to pause
because it's all it's alldesigned to program us.
And you know, I've I use theprophetic example because in my
mind, in my mind, right, andagain, I I said I'm not calling
(57:22):
these people false prophets, butin my mind, if a prophet, if a
prophet, someone who's a seer,right, someone who has more
discernment than the averageperson, if they can be fooled,
if they can be tricked by thealgorithm, by the data, by the
(57:46):
technology, how much more canthe normal or average person be
tricked, be programmed, bemanipulated, how much more?
We are in very serious times.
(58:07):
And if we don't, if we don'ttake back our control, we are in
big trouble.
We need to use discernment.
We need to use discernmentbecause when the system is
poison, clarity becomes warfare.
(58:28):
We need to take back thecontrol.
We need to take back thecontrol.
And so I've learned that evensilence can be weaponized,
right?
So the they loop old data tomake it seem like I'm stuck,
right?
They'll exaggerate moments topaint me as unstable.
They violate privacy in everycategory, whether it's personal,
(58:50):
professional, spiritual, andthen use that stolen insight to
distort my image.
But I'm not what they loop,right?
I'm not what they label and I'mI'm what I live.
I'm what I live.
And you know, I I just think wehave to be very careful, very
(59:10):
careful.
And I'm I'm just kind of I'mnudging the prophetic voices,
the prophets, the true prophetsout there to be careful, be
careful, and just to everyone,because if a prophet can be
misguided, if a prophet can be,you know, if they can be
(59:34):
tricked, how much more can theaverage person?
Because they have they haveinsight that we don't.
They have insight that we don't.
And so like I've seen it.
I'm like, okay, wow, thisprophet is acting exactly as the
algorithm was designed to to tomake them react.
They're saying exactly what thealgorithm wants them to say.
(59:57):
You know, they're actuallymaking a mistake.
Mistake in saying that thisprophetic word is for this
person.
And I'm like, okay, I can tellthat they're using something,
you know, to give this person aprophetic word.
And it's, you know, while I knowthat they are potentially also
leaning on the Holy Spirit, butthey're leaning on the
technology also.
(01:00:19):
And unfortunately, thetechnology is wrong.
And so we need to be careful,ladies and gentlemen.
We need to be careful.
Now, fighting the flaw.
So what do we do?
We name it, we document it asmuch as possible, right?
(01:00:41):
We speak it, we speak out.
It's hard to documenteverything, as I mentioned
before.
We build platforms that can't behijacked.
I don't know.
If there are any targetedindividuals who are tech
experts, reach out.
If you're a cybersecurityexpert, reach out.
(01:01:03):
You know, if you are alegislator, reach out and let's
just make things better.
Let's do better.
Let's do better.
Judge Faith actually called outYouTube because of how her
information was being distortedand basically the misinformation
was being spread and YouTubewasn't doing anything about it.
(01:01:26):
And so, you know, she foughtback.
She created content and shebasically exposed the people
that were spreading lies abouther.
And so we we need to createcontent that can't, that can't
be loot.
And so we we need to walk intruth that can't be silenced.
(01:01:48):
And you know, I think the moreand more we we we speak our
truth and we become the ones whoare in charge of our narrative
and we don't allow thetechnology to take control, I
think we're in a betterposition.
(01:02:10):
But from the looks of things andwhat I've been seeing, it seems
like a lot of people have beenallowing the technology to lead
them as opposed to them leadingthe technology.
And so we are not in a goodposition.
Humanity is not in a goodposition, you know, the
(01:02:30):
spiritual leaders aren't in agood position because they're
also being manipulated.
The prophets, a lot of them havebeen manipulated by the
technology as well.
So we we need to kind of tapinto, we need to tap it into how
things were done beforetechnology while still using
(01:02:52):
technology, right?
And so we need to rely on ournatural senses first and then on
the technology.
What I demand and what we allshould demand is transparency,
right?
We need to have a diverse teamor diverse teams building AI.
(01:03:14):
We need to have audits of thetraining data that are being
used.
There needs to be oversight ofsurveillance tech.
There needs to be accountabilityfor digital harm.
There needs to be moreregulation around tech as a
whole.
I know that, you know, as acountry, we're at, you know,
(01:03:34):
we're trying to race to beatother countries with AI and
things of that nature, but wecan't forget ethics.
We can't take ethics out of itjust, you know, because we're
trying to be first, right?
We need to do it right.
We need to do it right.
And we need to implementregulations and laws, you know,
(01:03:58):
statutes around how technologyis being used, how data is being
used.
And so as Christians, we alsoneed to reclaim our spiritual
authority because this isn'tjust about algorithms, right?
As I mentioned, a lot of, youknow, the spiritual enemy that
(01:04:20):
we have, they're usingtechnology to obstruct us.
They're using technology.
As I said, they don't, they nolonger need monitoring spirits
spiritually, right?
Uh the technology has becometheir monitoring spirits.
So it's about agency, it's aboutreclaiming your voice, your
platform, and your presence.
(01:04:42):
And we need to stay spirituallystrong.
We also need to be informedtechnologically, right?
We we need to be trained, weneed to know how to use the most
and the latest data, the latesttechnology, but at the same
time, we need to be careful inwhat is being presented to us
and how we're being how we'reusing it, because that same
(01:05:07):
data, that same technology isalso being used to program us.
And if we're not careful, we'regonna fall into what we don't
actually want to fall into.
And so as I close this episode,I want to say that, you know,
(01:05:31):
they tried to loop me, label me,silence me, obstruct me, but I'm
still here and I'm stillspeaking.
And so with that being said, Iwant to make this decree.
I decree that every false flag,every poison algorithm, every
(01:05:55):
surveillance tactic used againstme shall be exposed, dismantled,
and rendered powerless.
I reclaim my voice, my platform,my presence, and I declare that
no weapon formed against meshall prosper, and every tongue
that rises up against me injudgment shall be condemned.
(01:06:16):
I walk in divine clarity,digital sovereignty, and
prophetic truth.
My testimony will not beintercepted, my breakthrough
will not be delayed, my identitywill not be distorted.
I am seen rightly, heardclearly, and covered completely
in Jesus' mighty name.
(01:06:37):
Amen, amen, amen.
Teleo, tale, tale.
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